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Visual Stimuli-Based Dynamic Commands With Intelligent Control for Reactive BCI Applications
Abstract
In this study, inconspicuous visual stimuli with hidden targets are considered as a new paradigm for reactive brain-computer interface (BCI) applications suitable for actuating a motorized system with a dynamic command from user. To drive the motor’s control signal level as close as possible to the targeted dynamic command via wireless transmission, an embeddable intelligent control scheme is introduced to improve the overall electroencephalography (EEG)-based decoding strategy of the BCI system. The proposed technique which can induce motivated attention only requires a single EEG channel, and the intelligent control scheme is constructed with a decoder consisting of a multilayer perceptron (MLP) neural network and a recursive digital Boxcar filter to suppress the influence of noise and ocular artifacts that are typically caused by user’s involuntary movements. Results from thirty subjects showed that the predictive ability of the BCI system was significantly improved via the proposed decoder compared to the performance of MLP models alone and those with low pass and Kalman filters which were two existing methods commonly used to alleviate the aforementioned perturbations in real-time applications. The BCI’s predictive ability could also be further enhanced by selecting a suitable stimulus to construct a generic MLP model for each gender due to notable performance disparities between male and female groups. The findings of this study will have the potential to increase the degree of freedom in reactive BCI applications particularly when embedded control systems with multiple actuator speeds or accelerations are desired such as those used to control mobile robotics.
... Alternatives to the ICA include multiscale principal component analysis [19], signal decomposition methods [20,21], and general filtering methods such as wavelet transform, adaptive and Wiener filters but most of them are frequently adopted for offline analysis due to high computational cost. To minimize delays in real-time BCI application with an external actuator, infinite-impulse response, Kalman and Boxcar filters have been proposed as they can offer better solutions with less demanding computational requirements [22]. ...
... Unlike other ML approaches, a notable advantage of the GPMs lies on their ability to operate on small datasets and provide measurements of uncertainty on predictions. The effectiveness of the proposed approach is demonstrated via a comparative study against other competitive classifiers which have been previously evaluated with a single-channel EEG device in recent works such as multilayer perceptron NN [22], EL [27], LDA [28], kNN, and SVM [29]. In the light of [22] which proposes an alternative to motor imagery BCI that typically entails flickering stimuli and extensive training [30], inconspicuous stationary visual stimuli are introduced in the BCI paradigm to elevate the user's attentiveness while controlling the actuator. ...
... The effectiveness of the proposed approach is demonstrated via a comparative study against other competitive classifiers which have been previously evaluated with a single-channel EEG device in recent works such as multilayer perceptron NN [22], EL [27], LDA [28], kNN, and SVM [29]. In the light of [22] which proposes an alternative to motor imagery BCI that typically entails flickering stimuli and extensive training [30], inconspicuous stationary visual stimuli are introduced in the BCI paradigm to elevate the user's attentiveness while controlling the actuator. The use of such a paradigm is also in line with a recent review in [31] that highlights the significance of selecting suitable stimuli to induce the user's attention. ...
A single-channel electroencephalography (EEG) device, despite being widely accepted due to convenience, ease of deployment and suitability for use in complex environments, typically poses a great challenge for reactive brain-computer interface (BCI) applications particularly when a continuous command from users is desired to run a motorized actuator with different speed profiles. In this study, a combination of an inconspicuous visual stimulus and voluntary eyeblinks along with a machine learning-based decoder is considered as a new reactive BCI paradigm to increase the degree of freedom and minimize mismatches between the intended dynamic command and transmitted control signal. The proposed decoder is constructed based on Gaussian Process model (GPM) which is a nonparametric Bayesian approach that has the advantages of being able to operate on small datasets and providing measurements of uncertainty on predictions. To evaluate the effectiveness of the proposed method, the GPM is compared against other competitive techniques which include k-Nearest Neighbors, linear discriminant analysis, support vector machine, ensemble learning and neural network. Results demonstrate that a significant improvement can be achieved via the GPM approach with average accuracy reaching over 96% and mean absolute error of no greater than 0.8 cm/s. In addition, the analysis reveals that while the performances of other existing methods deteriorate with a certain type of stimulus due to signal drifts resulting from the voluntary eyeblinks, the proposed GPM exhibits consistent performance across all stimuli considered, thereby manifesting its generalization capability and making it a more suitable option for dynamic commands with a single-channel EEG-controlled actuator.
... Similarly, mowing robots need to follow intricate patterns across lawns, requiring precise trajectory tracking to avoid obstacles and ensure efficient operation [3]. In scenarios where robots must follow a leader -be it another robot or a human -the ability to maintain an accurate trajectory becomes paramount [4], [5]. These interactions often involve complex, nonlinear paths rather than simple straight lines, demanding advanced control strategies to enhance performance. ...
Improving trajectory tracking in Differential Wheeled Mobile Robots (DWMRs) is vital for enhancing their effectiveness in various applications, such as autonomous cleaning, mowing, and leader-following scenarios. These scenarios often involve navigating complex, nonlinear paths, requiring advanced control strategies for enhanced performance. This work presents the novel integration of a Backstepping Controller (BSC) and a Fractional-Order Proportional-Integral-Derivative (FOPID) controller within a cascade closed-loop structure for Differential Wheeled Mobile Robots (DWMRs). The proposed BSC-FOPID controller addresses velocity saturations and nonlinearities, ensuring system stability and precise trajectory tracking. A key contribution is the enhanced Grey Wolf Optimization strategy, termed GWO-SMA, which integrates Grey Wolf Optimization (GWO) with Slime Mould Algorithm (SMA). By leveraging opposition space and optimum cache concepts, GWO-SMA improves fitness optimization in each iteration, enhancing both exploration and exploitation efficiency. This hybrid approach optimizes controller parameters using a multi-metric cost function that incorporates Integral Absolute Error (IAE) and Integral Squared Error (ISE) to minimize long-term steady-state error and enhance responsiveness to larger deviations. Simulations demonstrate the superior performance of the proposed GWO-SMA algorithm compared to existing optimization techniques, such as Particle Swarm Optimization (PSO), Gazelle Optimization Algorithm (GOA), and its individual components, GWO and SMA, which have shown strong performance in recent literature for optimizing PID-type controllers. In addition, simulation results using three distinct reference paths, i.e. lemniscate, square, and cloverleaf; demonstrate that the GWO-SMA-optimized BSC-FOPID controller outperforms both adaptive dynamic compensation control (ADCC) and BSC-PID controller in position and posture tracking accuracy. Specifically, the BSC-FOPID controller achieves significant improvements, including average reductions of 55.65% in ISE and 38.25% in IAE for position control, as well as 62.12% and 38.95% improvements in ISE and IAE for posture control, respectively. These improvements highlight the controller's enhanced responsiveness and smoother error convergence, particularly during maneuvers involving sharp curves.
... In comparison with other wireless technologies, BLE can significantly reduce deployment and maintenance costs [37]. ...
Although Global Navigation Satellite Systems (GNSS) typically provide sufficient accuracy for outdoor positioning, positioning systems face significant challenges in complex application scenarios, particularly in GPS-denied environments (GDE). As application demands increase, single-sensor positioning methods are increasingly inadequate to meet the requirements for high accuracy and robustness, making multi-sensor fusion techniques a growing research focus. In recent years, while many studies have aimed to achieve indoor positioning stability and reliability through Simultaneous Localization and Mapping (SLAM), Ultra-Wideband (UWB), odometry, and Wi-Fi fusion technologies, these efforts have often been conducted in isolation, lacking a systematic synthesis of these fusion methods. Notably, there is a particular gap in the comprehensive analysis of Dynamic Target Localization (DTL). DTL involves real-time tracking of moving targets such as pedestrians, robots, vehicles, and Unmanned Aerial Vehicle (UAV), which not only requires maintaining high accuracy in complex and dynamic environments but also demands the capability to handle the challenges posed by rapidly moving targets. This paper addresses this research gap by systematically reviewing the existing sensor fusion techniques, providing a detailed analysis of the application of SLAM fusion, UWB fusion, odometry fusion, and Wi-Fi fusion in DTL, and thoroughly evaluating their effectiveness and performance. Additionally, this paper summarizes the application of these technologies across different types of moving targets, discusses future development trends and potential challenges, and offers valuable insights for advancing the widespread application of multi-sensor fusion techniques in dynamic target localization. Finally, the challenges facing future work in this field are discussed.
... The efficiency of BLE in terms of power consumption has spurred the development of numerous IoT devices. In addition, there exists substantial potential in this domain for integrating artificial intelligence (AI) with BLE technology to create a genuinely intelligent devices and logistics chain [39]. ...
Indoor localization (IL) systems are crucial for enhancing operational efficiency, safety, and user experience by allowing precise tracking of objects, robots, or individuals within various environments such as healthcare, retail, and industrial sectors. Despite their increasing importance, there remains a notable deficiency in the literature, particularly concerning systematic reviews that consolidate findings from experimental research. This work fills this crucial gap by rigorously assessing the advancements and challenges faced by Wireless Sensor Network (WSN)-based IL systems, with a focused examination of experimental studies conducted over the past five years. It delves into both radio frequency (RF) and non-RF technologies, critically evaluating a spectrum of localization methods including fingerprinting, geometric mapping, proximity, and dead-reckoning. It systematically evaluates the advantages, limitations, and current solutions of each method, based on their citation metrics and prevalence in scholarly literature. Furthermore, the paper explores innovative performance enhancement techniques, including the integration of machine learning and the hybridization of multiple technologies, to demonstrate significant improvements in IL functionalities. It also identifies and analyzes key trends, such as the choice of technologies for specific methods, typical network density configurations, and accuracy enhancements achieved through different approaches. Research gaps are highlighted, including the need for advancements in machine learning for offline and edge computing, standardization of sensor components, and improvements in interoperability and energy efficiency. The paper concludes by proposing strategic future research directions, outlining a roadmap for advancing IL research and development in this rapidly evolving field.
... Complexities in load detection arise with irregular load shapes and varying environmental conditions, which necessitate adaptable and intelligent sensing systems [127], [128]. One effective approach to address these challenges is sensor fusion, where data from different sensor types are integrated to compensate for individual sensor limitations, resulting in more robust and precise load detection [129]. ...
This review paper provides a detailed overview of the advancements and identifies pivotal challenges in the realm of autonomous load-carrying mobile robots, with a particular focus on indoor applications for both ground and aerial platforms. It critically examines the past decade’s innovations in load-carrying designs and sensor technologies, scrutinizing their impact on the enhancement of robotic autonomy and load management. The paper also presents an in-depth analysis of the latest trends in navigation and control algorithms essential for refining these robots’ operational efficacy in diverse indoor scenarios. By evaluating current research outputs, this work identifies critical areas for future exploration, such as improving indoor navigation complexity, optimizing load handling for varying conditions, and pioneering precise load-sensing techniques. The paper proposes innovative research paths designed to address the identified gaps, underscoring the necessity for breakthroughs in robot design, enhanced integration of systems, and increased operational efficiency. The overarching aim is to propel the functionality of autonomous load-carrying robots to new heights, ensuring they meet the increasing demands of various sectors including industrial, commercial, and service domains.
... In another work presented in [24], both ant colony optimization and the Ziegler-Nichols tuning method are used to parameterize the PID controller. Nonlinear control methods such as fuzzy logic (FL) [25], [26] and neural network (NN) [27]- [29] can be advantageous due to their adaptive and self-learning capabilities. In [30]- [32] for instance, the PID and FL are combined to form a Fuzzy-PID controller to improve the MagLev's position control performance. ...
This work demonstrates the design and development of a magnetic levitation (MagLev) system that is able to control both the position and orientation of the levitated object. For the position control, a pole placement method was exploited to estimate parameters of the proportional integral derivative (PID) controller. In addition, the MagLev was constructed using a pair of electromagnets, two infrared (IR) receiver-emitter pairs and a servo motor to allow the orientation of the object to be controlled. The proposed controller was programmed in a LabVIEW environment, which was then compiled and deployed into an embedded NI myRIO board. Experimental results demonstrated that the proposed method was able to achieve a zero steady-state orientation error when the object was rotated from 0 ◦ to ±90◦ , a steady-state position error of 0.3 cm without rotation, and steady-state position errors of no greater than 1.2 cm with rotation.
This study enhances brain-controlled vehicle (BCV) lateral control using a steady-state visual evoked potential (SSVEP) interface and probabilistic support vector machine (SVM). A filter bank CSP (FBCSP) algorithm improves brain signal decoding, while a sigmoid-fitted SVM (SF-SVM) enables smoother control through probabilistic commands. Online tests achieved 84.03% classification accuracy. In lane-keeping tasks, SF-SVM improved completion rates by over 20% compared to standard SVM, reducing EEG non-stationarity effects. The probabilistic model optimized continuous control, significantly enhancing BCV performance.
This paper presents a comprehensive review of the latest developments in fuzzy logic (FL) applications across critical domains which include energy harvesting (EH), ambient conditioning systems (ACS), and robotics and autonomous systems (RAS), highlighting FL's capability to address nonlinearities and uncertainties in diverse technological environments. Through a detailed comparative analysis of research trends over the past decade, it underscores the increasing significance of FL in EH and RAS, contrasting with the sustained interest in ACS. Furthermore, the evaluation of different fuzzy inference systems across domains provides valuable insights into their specific strengths and limitations, aiding researchers and practitioners in making informed decisions aligned with their application needs. Additionally, the paper explores advanced modifications and hybridizations of FL, such as swarm intelligence and integrations with other control strategies, emphasizing the necessity for robust and adaptive FL systems. The review also identifies key open problems and potential research directions, such as the demand for adaptive FL systems in EH and advanced optimization techniques in ACS and RAS. Overall, this state-of-the-art review not only summarizes the current state of FL applications but also outlines a roadmap for future research, offering valuable insights for advancing FL's role in handling uncertainties and nonlinearities in complex systems.
Material classification is pivotal across materials science, engineering, and various industrial sectors. Despite the high accuracy of traditional material classification methods, they often entail large, intricate, and costly setups that demand skilled operators. In this study, we introduce the MCT-array, a newly developed compact RF antenna array system measuring 100×100×2mm, which functions as a transceiver. This device, equipped with 32 receiving antennas and 2 transmitters, leverages dynamic power adjustments to refine material detection accuracy. The study evaluates three machine learning classifiers, namely Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RandF) on twelve different materials. MATLAB simulations are initially conducted to identify optimal transceiver configurations. Following the identification of optimal parameters from these simulations, real-world experiments are conducted with the materials positioned 30 cm away from the antenna. Results demonstrate that RandF achieves a material classification accuracy of 94.84%, followed by SVM at 94.5%, and MLP at 94.1%. Detailed analysis further reveals that RandF is the preferred option for tasks demanding the highest levels of accuracy, SVM strikes an optimal balance between processing speed and accuracy, while MLP stands out for its rapid prediction times, making it especially suitable for real-time applications. Integrating an innovative portable RF transceiver with these machine learning models, achieving an impressive average accuracy of over 94%, represents a scalable and effective solution. This innovation holds significant promise for sectors engaged in material classification, particularly in the realms of robotics and automation.
This study introduces an enhanced algorithm for global path planning of Differential Wheeled Mobile Robots (DWMRs) that merges the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO). This hybrid strategy, termed HWPSO, is designed to leverage WOA's exploration strength with PSO's efficient exploitation, specifically targeting the challenges of non-holonomic constraints in complex terrains. To validate the effectiveness of the proposed algorithm, its performance is evaluated across five diverse environments and compared against PSO, WOA, and Grey Wolf Optimization which is widely used for mobile robot path planning. Moreover, the comparison broadens to encompass four established environments from the literature where algorithms based on firefly, ant colony, A*, and other PSO variants have previously exhibited optimal performance. Additionally, a new environment is introduced to analyze the efficacy of the proposed approach for path planning for two DWMRs. Simulation results consistently demonstrate the superiority of the proposed HWPSO, manifesting performance improvements of up to 19.3% for path length reduction and up to 12.7% for DWMR travel duration reduction when compared to other methods. This underscores the efficacy of the proposed hybrid approach in achieving enhanced path planning outcomes for DWMRs in diverse scenarios.
DC motors are widely used in various industrial applications, and accurately predicting their performance under different payload conditions is crucial for optimal control and efficient system design. In recent years, data-driven modeling techniques have gained significant attention as effective tools for capturing the complex dynamics of DC motors. This study conducts a comparative assessment of diverse data-driven models applied to DC motors operating under different load conditions. A number of model structures have been tested which include continuous-time transfer function, discrete-time transfer function, and Auto-Regressive with eXogenous input (ARX). Results reveal that the ARX and second-order continuous time transfer function models outperform the rest with accuracies of at least 94%.
This paper shows how two microphones in an endfire array configuration was used to perform beamforming. The setup uses two condenser microphones and a sound card to allow multiple sources to be input to the computer at the same time. A cross-correlation calculation was used to determine the time shift between the two mics. Using the Delay and Sum algorithm, the time shift can be corrected, and the mic signals can be added to a superposition.
Environmental noise leads to changes of the people’s brain load, making it necessary for the brain-computer interface (BCI) to adapt to the load changes. Our previous research found BCI performance is influenced by the similarity between the current test load and the past train load. Based on the similarity of the current load to three past loads, this research proposes an adaptive-load classifier to dynamically adapts to the current brain load by adjusting the weights of three load specific classifiers by brain load similarity. Pearson correlation coefficient (PCC) and sample entropy (SE) are separately applied to match the similarity of the brain loads. Then the adaptive-load classifier transforms the similarities to the weights to fuse the load specific classifiers. Ten subjects participated in this study, using 28-channel and 3-channel EEG signals to test the classifier. The PCC and SE reach higher accuracies and ITRs than the load specific classifier with an improvement of 31.07% (
P
=8.63×10-5) and 7.34% (
P
=0.05), 32.15 bits/min (
P
=0.02) and 7.08 bits/min (
P
=0.24) in 28 channels, and reach the accuracies of 99.17% and 91.67%, and the ITR of 38.05 bits/min and 26.14 bits/min in 3 channels. The adaptive-load classifier could adapt to different loads by matching the similarity of the current data and past loads with a large amount or a small number of channels, which is practical for the bCi application in the real environment.
Recent years have seen a dramatic rise in the popularity of autonomous mobile robots (AMRs) due to their practicality and potential uses in the modern world. Path planning is among the most important tasks in AMR navigation since it demands the robot to identify the best route based on desired performance criteria such as safety margin, shortest time, and energy consumption. The complexity of the problem can however become intractable when challenging scenarios are considered, which include navigation in a dynamic environment and solving multi-objective optimizations. Various classical and heuristic techniques have been proposed by researchers to mitigate such issues. The purpose of this paper is to provide a comprehensive and up-to-date literature review of the path planning strategies that have received a considerable attention over the past decade. A systematic analysis of the strengths, shortcomings, and scope of each method is presented. The trends as well as challenges in practical implementation of the strategies are also discussed at the end of this paper. The outcome of this survey provides useful guidance for future research into creating new strategies that can enhance the autonomy level of AMRs while preserving their robustness against unforeseen circumstances in practice.
Visual evoked potential (VEP) has been used as an alternative method to assess visual acuity objectively, especially in non-verbal infants and adults with low intellectual abilities or malingering. By sweeping the spatial frequency of visual stimuli and recording the corresponding VEP, VEP acuity can be defined by analyzing electroencephalography (EEG) signals. This paper presents a review on the VEP-based visual acuity assessment technique, including a brief overview of the technique, the effects of the parameters of visual stimuli, and signal acquisition and analysis of the VEP acuity test, and a summary of the current clinical applications of the technique. Finally, we discuss the current problems in this research domain and potential future work, which may enable this technique to be used more widely and quickly, deepening the VEP and even electrophysiology research on the detection and diagnosis of visual function.
Motion control involving DC motors requires a closed-loop system with a suitable compensator if tracking performance with high precision is desired. In the case where structural model errors of the motors are more dominating than the effects from noise disturbances, accurate system modelling will be a considerable aid in synthesizing the compensator. The focus of this paper is on enhancing the tracking performance of a wheeled mobile robot (WMR), which is driven by two DC motors that are subject to model parametric uncertainties and uncertain deadzones. For the system at hand, the uncertain nonlinear perturbations are greatly induced by the time-varying power supply, followed by behaviour of motion and speed. In this work, the system is firstly modelled, where correlations between the model parameters and different input datasets as well as voltage supply are obtained via polynomial regressions. A robust H∞-fuzzy logic approach is then proposed to treat the issues due to the aforementioned perturbations. Via the proposed strategy, the H∞ controller and the fuzzy logic (FL) compensator work in tandem to ensure the control law is robust against the model uncertainties. The proposed technique was validated via several real-time experiments, which showed that the speed and path tracking performance can be considerably enhanced when compared with the results via the H∞ controller alone, and the H∞ with the FL compensator, but without the presence of the robust control law.
Dry electrode electroencephalogram (EEG) recording combined with wireless data transmission offers an alternative tool to conventional wet electrode EEG systems. However, the question remains whether the signal quality of dry electrode recordings is comparable to wet electrode recordings in the clinical context. We recorded the resting state EEG (rsEEG), the visual evoked potentials (VEP) and the visual P300 (P3) from 16 healthy subjects (age range: 26–79 years) and 16 neurological patients who reported subjective memory impairment (age range: 50–83 years). Each subject took part in two recordings on different days, one with 19 dry electrodes and another with 19 wet electrodes. They reported their preferred EEG system. Comparisons of the rsEEG recordings were conducted qualitatively by independent visual evaluation by two neurologists blinded to the EEG system used and quantitatively by spectral analysis of the rsEEG. The P100 visual evoked potential (VEP) and P3 event-related potential (ERP) were compared in terms of latency, amplitude and pre-stimulus noise. The majority of subjects preferred the dry electrode headset. Both neurologists reported that all rsEEG traces were comparable between the wet and dry electrode headsets. Absolute Alpha and Beta power during rest did not statistically differ between the two EEG systems (p > 0.05 in all cases). However, Theta and Delta power was slightly higher with the dry electrodes (p = 0.0004 for Theta and p < 0.0001 for Delta). For ERPs, the mean latencies and amplitudes of the P100 VEP and P3 ERP showed comparable values (p > 0.10 in all cases) with a similar spatial distribution for both wet and dry electrode systems. These results suggest that the signal quality, ease of set-up and portability of the dry electrode EEG headset used in our study comply with the needs of clinical applications.
Objective:
Recently, electroencephalography (EEG)- based brain-computer interfaces (BCIs) have made tremendous progress in increasing communication speed. However, current BCI systems could only implement a small number of command codes, which hampers their applicability.
Methods:
This study developed a high-speed hybrid BCI system containing as many as 108 instructions, which were encoded by concurrent P300 and steady-state visual evoked potential (SSVEP) features and decoded by an ensemble task-related component analysis method. Notably, besides the frequency-phase-modulated SSVEP and time-modulated P300 features as contained in the traditional hybrid P300 and SSVEP features, this study found two new distinct EEG features for the concurrent P300 and SSVEP features, i.e. time-modulated SSVEP and frequency-phase- modulated P300. Ten subjects spelled in both offline and online cued-guided spelling experiments. Other ten subjects took part in online copy-spelling experiments.
Results:
Offline analyses demonstrate that the concurrent P300 and SSVEP features can provide adequate classification information to correctly select the target from 108 characters in 1.7 seconds. Online cued-guided spelling and copy-spelling tests further show that the proposed BCI system can reach an average information transfer rate (ITR) of 172.46±32.91 bits/min and 164.69±33.32 bits/min respectively, with a peak value of 238.41 bits/min (The demo video of online copy-spelling is enclosed and can be found at https://www.youtube.com/watch?v=EW2Q08oHSBo).
Conclusion:
We expand a BCI instruction set to over 100 command codes with high-speed in an efficient manner, which significantly improves the degree of freedom of BCIs.
Significance:
This study hold promise for broadening the applications of BCI systems.
With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain–computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user’s dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data.
Brain computer interface (BCI) adopts human brain signals to control external devices directly without using normal neural pathway. Recent study has explored many applications, such as controlling a teleoperation robot by electroencephalography (EEG) signals. However, utilizing the motor imagery EEG-based BCI to perform teleoperation for reach and grasp task still has many difficulties, especially in continuous multidimensional control of robot and tactile feedback. In this research, a motor imagery EEG-based continuous teleoperation robot control system with tactile feedback was proposed. Firstly, mental imagination of different hand movements was translated into continuous command to control the remote robotic arm to reach the hover area of the target through a wireless local area network (LAN). Then, the robotic arm automatically completed the task of grasping the target. Meanwhile, the tactile information of remote robotic gripper was detected and converted to the feedback command. Finally, the vibrotactile stimulus was supplied to users to improve their telepresence. Experimental results demonstrate the feasibility of using the motor imagery EEG acquired by wireless portable equipment to realize the continuous teleoperation robot control system to finish the reach and grasp task. The average two-dimensional continuous control success rates for online Task 1 and Task 2 of the six subjects were 78.0% ± 6.1% and 66.2% ± 6.0%, respectively. Furthermore, compared with the traditional EEG triggered robot control using the predefined trajectory, the continuous fully two-dimensional control can not only improve the teleoperation robot system’s efficiency but also give the subject a more natural control which is critical to human–machine interaction (HMI). In addition, vibrotactile stimulus can improve the operator’s telepresence and task performance.
Objective. In recent years, brain–computer interface (BCI) systems based on electroencephalography (EEG) have developed rapidly. However, the decoding of voluntary finger pre-movements from EEG is still a challenge for BCIs. This study aimed to analyze the pre-movement EEG features in time and frequency domains and design an efficient method to decode the movement-related patterns. Approach. In this study, we first investigated the EEG features induced by the intention of left and right finger movements. Specifically, the movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were extracted using discriminative canonical pattern matching (DCPM) and common spatial patterns (CSP), respectively. Then, the two types of features were classified by two fisher discriminant analysis (FDA) classifiers, respectively. Their decision values were further assembled to facilitate the classification. To verify the validity of the proposed method, a private dataset containing 12 subjects and a public dataset from BCI competition II were used for estimating the classification accuracy. Main results. As a result, for the private dataset, the combination of DCPM and CSP achieved an average accuracy of 80.96%, which was 5.08% higher than the single DCPM method (p < 0.01) and 10.23% higher than the single CSP method (p < 0.01). Notably, the highest accuracy could achieve 91.5% for the combination method. The test accuracy of dataset IV of BCI competition II was 90%, which was equal to the best result in the existing literature. Significance. The results demonstrate the MRCP and ERD features of pre-movements contain significantly discriminative information, which are complementary to each other, and thereby could be well recognized by the proposed combination method of DCPM and CSP. Therefore, this study provides a promising approach for the decoding of pre-movement EEG patterns, which is significant for the development of BCIs.
The functional validity of the signal obtained with low-cost electroencephalography (EEG) devices is still under debate. Here, we have conducted an in-depth comparison of the EEG-recordings obtained with a medical-grade golden-cup electrodes ambulatory device, the SOMNOwatch + EEG-6, vs those obtained with a consumer-grade, single dry electrode low-cost device, the NeuroSky MindWave, one of the most affordable devices currently available. We recorded EEG signals at Fp1 using the two different devices simultaneously on 21 participants who underwent two experimental phases: a 12-minute resting state task (alternating two cycles of closed/open eyes periods), followed by 60-minute virtual-driving task. We evaluated the EEG recording quality by comparing the similarity between the temporal data series, their spectra, their signal-to-noise ratio, the reliability of EEG measurements (comparing the closed eyes periods), as well as their blink detection rate. We found substantial agreement between signals: whereas, qualitatively, the NeuroSky MindWave presented higher levels of noise and a biphasic shape of blinks, the similarity metric indicated that signals from both recording devices were significantly correlated. While the NeuroSky MindWave was less reliable, both devices had a similar blink detection rate. Overall, the NeuroSky MindWave is noise-limited, but provides stable recordings even through long periods of time. Furthermore, its data would be of adequate quality compared to that of conventional wet electrode EEG devices, except for a potential calibration error and spectral differences at low frequencies.
In this study, we presented a novel asynchronous speller for Chinese sinogram input by incorporating electroen-cephalography (EOG) into the conventional electroencephalog-raphy (EEG)-based spelling paradigm. An EOG-based brain switch was used to activate a classic row-column P300-based speller only when spelling was needed, enabling asynchronous op-eration of the system. Then, the user could input sinograms by al-ternately performing P300 and double-blink tasks until he or she intended to stop spelling. With the incorporation of an EOG de-tector, the system achieved rapid sinogram input. In addition to asynchronous operation, the performance of the proposed speller was compared with that achieved by a P300-based method alone across eighteen subjects. The proposed system showed a mean communication speed of approximately 2.39 sinograms per minute, an increase of 0.83 sinograms per minute compared with the P300-based method. The preliminary online performance indicated that the proposed paradigm is a very promising approach for practical Chinese sinogram input application. This system may also be ex-panded to users whose languages are written in logographic scripts to serve as an assistive communication tool.
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.
Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application.
Advances in brain science and computer technology in the past decade have led to exciting developments in brain–computer interface (BCI), thereby making BCI a top research area in applied science. The renaissance of BCI opens new methods of neurorehabilitation for physically disabled people (e.g. paralyzed patients and amputees) and patients with brain injuries (e.g. stroke patients). Recent technological advances such as wireless recording, machine learning analysis, and real-time temporal resolution have increased interest in electroencephalographic (EEG) based BCI approaches. Many BCI studies have focused on decoding EEG signals associated with whole-body kinematics/kinetics, motor imagery, and various senses. Thus, there is a need to understand the various experimental paradigms used in EEG-based BCI systems. Moreover, given that there are many available options, it is essential to choose the most appropriate BCI application to properly manipulate a neuroprosthetic or neurorehabilitation device. The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives. For each paradigm, various EEG decoding algorithms and classification methods are evaluated. The applications of these paradigms with targeted patients are summarized. Finally, potential problems with EEG-based BCI systems are discussed, and possible solutions are proposed.
It is a fact that contamination of EEG by ocular artifacts reduces the classification accuracy of a brain-computer interface (BCI) and diagnosis of brain diseases in clinical research. Therefore, for BCI and clinical applications, it is very important to remove/reduce these artifacts before EEG signal analysis. Although, EOG-based methods are simple and fast for removing artifacts but their performance, meanwhile, is highly affected by the bidirectional contamination process. Some studies emphasized that the solution to this problem is low-pass filtering EOG signals before using them in artifact removal algorithm but there is still no evidence on the optimal low-pass frequency limits of EOG signals. In this study, we investigated the optimal EOG signal filtering limits using state-of-the-art artifact removal techniques with fifteen artificially contaminated EEG and EOG datasets. In this comprehensive analysis, unfiltered and twelve different low-pass filtering of EOG signals were used with five different algorithms, namely, simple regression, least mean squares, recursive least squares, REGICA, and AIR. Results from statistical testing of time and frequency domain metrics suggested that a low-pass frequency between 6 and 8 Hz could be used as the most optimal filtering frequency of EOG signals, both to maximally overcome/minimize the effect of bidirectional contamination and to achieve good results from artifact removal algorithms. Furthermore, we also used BCI competition IV datasets to show the efficacy of the proposed framework on real EEG signals. The motor-imagery-based BCI achieved statistically significant high-classification accuracies when artifacts from EEG were removed by using 7 Hz low-pass filtering as compared to all other filterings of EOG signals. These results also validated our hypothesis that low-pass filtering should be applied to EOG signals for enhancing the performance of each algorithm before using them for artifact removal process. Moreover, the comparison results indicated that the hybrid algorithms outperformed the performance of single algorithms for both simulated and experimental EEG datasets.
Visual evoked potential-based brain–computer interfaces (BCIs) have been widely investigated because of their easy system configuration and high information transfer rate (ITR). However, the uncomfortable flicker or brightness modulation of existing methods restricts the practical interactivity of BCI applications. In our study, a flicker-free steady-state motion visual evoked potential (FF-SSMVEP)-based BCI was proposed. Ring-shaped motion checkerboard patterns with oscillating expansion and contraction motions were presented by a high-refresh-rate display for visual stimuli, and the brightness of the stimuli was kept constant. Compared with SSVEPs, few harmonic responses were elicited by FF-SSMVEPs, and the frequency energy of SSMVEPs was concentrative. These FF-SSMVEPs evoked “single fundamental peak” responses after signal processing without harmonic and subharmonic peaks. More stimulation frequencies could thus be selected to elicit more responding fundamental peaks without overlap with harmonic peaks. A 40-target online SSMVEP-based BCI system was achieved that provided an ITR up to 1.52 bits per second (91.2 bits/min), and user training was not required to use this system. This study also demonstrated that the FF-SSMVEP-based BCI system has low contrast and low visual fatigue, offering a better alternative to conventional SSVEP-based BCIs.
In most cases, the movement of wheelchairs is controlled by disabled people using a joystick or by an accompanying person. Significantly disabled patients need alternative control methods without using the wheelchair joystick because it is undesirable or impossible for these patients. In this article, we present the implementation of a robotic wheelchair based on a powered wheelchair that is controlled not by the joystick but by the onboard computer that receives and processes data from the extended brain-computer interface (extended BCI). Under this term we understand the robotic complex control system with simultaneous independent alternative control channels. In this robotic wheelchair version the BCI works with voice and gesture control channels.
People with severe neurological impairments face many challenges in sensorimotor functions and communication with the environment; therefore they have increased demand for advanced, adaptive and personalized rehabilitation. During the last several decades, numerous studies have developed brain–computer interfaces (BCIs) with the goals ranging from providing means of communication to functional rehabilitation. Here we review the research on non-invasive, electroencephalography (EEG)-based BCI systems for communication and rehabilitation. We focus on the approaches intended to help severely paralyzed and locked-in patients regain communication using three different BCI modalities: slow cortical potentials, sensorimotor rhythms and P300 potentials, as operational mechanisms. We also review BCI systems for restoration of motor function in patients with spinal cord injury and chronic stroke. We discuss the advantages and limitations of these approaches and the challenges that need to be addressed in the future.
Goal:
Traditional visual brain-computer interfaces (BCIs) preferred to use large-size stimuli to attract the user's attention and elicit distinct electroencephalography (EEG) features. However, the visual stimuli are of no interest to the users as they just serve as the hidden codes behind the characters. Furthermore, using stronger visual stimuli could cause visual fatigue and other adverse symptoms to users. Therefore, it's imperative for visual BCIs to use small and inconspicuous visual stimuli to code characters.
Methods:
This study developed a new BCI speller based on miniature asymmetric visual evoked potentials (aVEPs), which encodes 32 characters with a space-code division multiple access scheme and decodes EEG features with a discriminative canonical pattern matching algorithm. Notably, the visual stimulus used in this study only subtended 0.5° of visual angle and was placed outside the fovea vision on the lateral side, which could only induce a miniature potential about 0.5 μV in amplitude and about -16.5 dB in signal-to-noise rate. A total of 12 subjects were recruited to use the miniature aVEP speller in both offline and online tests.
Results:
Information transfer rates up to 63.33 b/min could be achieved from online tests (online demo URL: https://www.youtube.com/watch?v=kC7btB3mvGY).
Conclusion:
Experimental results demonstrate the feasibility of using very small and inconspicuous visual stimuli to implement an efficient BCI system, even though the elicited EEG features are very weak.
Significance:
The proposed innovative technique can broaden the category of BCIs and strengthen the brain-computer communication.
The final article will be available by Hindawi - currently accepted and in press ----- Advances in neural interfaces have demonstrated remarkable results in the direction of replacing and restoring lost sensorimotor function in human patients. Non-invasive Brain-Computer Interfaces (BCIs) are popular due to considerable advantages including simplicity, safety and low cost, while recent advances aim at improving past technological and neurophysiological limitations. Taking into account the neurophysiological alterations of disabled individuals, investigating brain connectivity features for implementation of BCI control holds special importance. Off-the-shelf BCI systems are based on fast, reproducible detection of mental activity and can be implemented in neuro-robotic applications. Moreover, social Human-Robot Interaction (HRI) is increasingly important in rehabilitation robotics development. In this paper, we present our progress and goals towards developing off-the-shelf BCI-controlled anthropomorphic robotic arms for assistive technologies and rehabilitation applications. We account for robotics development, BCI implementation and qualitative assessment of HRI characteristics of the system. Furthermore, we present two illustrative experimental applications of the BCI-controlled arms, a study of motor imagery modalities on healthy individuals’ BCI performance and a pilot investigation on spinal cord injured patients’ BCI control and brain connectivity. We discuss strengths and limitations of our design and propose further steps on development and neurophysiological study, including implementation of connectivity features as BCI modality.
This work studies the feasibility of using mental attention to access a computer. Brain activity was measured with an electrode placed at the Fp1 position and the reference on the left ear; seven normally developed people and three subjects with cerebral palsy (CP) took part in the experimentation. They were asked to keep their attention high and low for as long as possible during several trials. We recorded attention levels and power bands conveyed by the sensor, but only the first was used for feedback purposes. All of the information was statistically analyzed to find the most significant parameters and a classifier based on linear discriminant analysis (LDA) was also set up. In addition, 60% of the participants were potential users of this technology with an accuracy of over 70%. Including power bands in the classifier did not improve the accuracy in discriminating between the two attentional states. For most people, the best results were obtained by using only the attention indicator in classification. Tiredness was higher in the group with disabilities (2.7 in a scale of 3) than in the other (1.5 in the same scale); and modulating the attention to access a communication board requires that it does not contain many pictograms (between 4 and 7) on screen and has a scanning period of a relatively high t s c a n ≈ 10 s. The information transfer rate (ITR) is similar to the one obtained by other brain computer interfaces (BCI), like those based on sensorimotor rhythms (SMR) or slow cortical potentials (SCP), and makes it suitable as an eye-gaze independent BCI.
A brain-computer interface (BCI) measures and interprets brain signals enabling people to communicate without the use of peripheral muscles. One of the common BCI paradigms are steady state visual evoked potentials (SSVEPs), brain signals induced by gazing at a constantly flickering target. The choice of stimulation frequencies and the number of simultaneously used stimuli highly influence the performance of such SSVEP-based BCI. In this article, a dictionary-driven four class SSVEP-based spelling application is presented, tested, and evaluated. To enhance classification accuracy, frequencies were determined individually with a calibration software for SSVEP-BCIs, enabling non-experts to set up the system. Forty-one healthy participants used the BCI system to spell English sentences (lengths between 23 and 37 characters). All participants completed the spelling task successfully. A mean accuracy of 97.92% and a mean ITR of 23.84 bits/min were achieved, 18 participants even reached 100% accuracy. On average the number of commands needed to spell the example sentences with four classes, without dictionary support is higher by a factor of 1.92. Thanks to the implemented dictionary the time needed to spell typical everyday sentences can be drastically reduced.
Brain-computer interfaces represent a range of acknowledged technologies that translate brain activity into computer commands. The aim of our research is to develop and evaluate a BCI control application for certain assistive technologies that can be used for remote telepresence or remote driving. The communication channel to the target device is based on the steady-state visual evoked potentials. In order to test the control application, a mobile robotic car (MRC) was introduced and a four-class BCI graphical user interface (with live video feedback and stimulation boxes on the same screen) for piloting the MRC was designed. For the purpose of evaluating a potential real-life scenario for such assistive technology, we present a study where 61 subjects steered the MRC through a predetermined route. All 61 subjects were able to control the MRC and finish the experiment (mean time 207.08 s, SD 50.25) with a mean (SD) accuracy and ITR of 93.03% (5.73) and 14.07 bits/min (4.44), respectively. The results show that our proposed SSVEP-based BCI control application is suitable for mobile robots with a shared-control approach. We also did not observe any negative influence of the simultaneous live video feedback and SSVEP stimulation on the performance of the BCI system.
Brain Computer Interfaces (BCI), is a modern technology which is currently revolutionizing the field of signal processing. BCI helped in the evolution of a new world where man and computer had never been so close. Advancements in cognitive neuro-sciences facilitated us with better brain imaging techniques and thus interfaces between machines and the human brain became a reality. Electroencephalography (EEG), which is the measurement and recording of electric signals using sensors arrayed across the scalp can be used for applications like prosthetic devices, applications in warfare, gaming, virtual reality and robotics upon signal conditioning and processing.
Brain computer interface technology represents a highly growing field of research with application systems. Its contributions in medical fields range from prevention to neuronal rehabilitation for serious injuries. Mind reading and remote communication have their unique fingerprint in numerous fields such as educational, self-regulation, production, marketing, security as well as games and entertainment. It creates a mutual understanding between users and the surrounding systems. This paper shows the application areas that could benefit from brain waves in facilitating or achieving their goals. We also discuss major usability and technical challenges that face brain signals utilization in various components of BCI system. Different solutions that aim to limit and decrease their effects have also been reviewed.
Periodic visual stimulation and analysis of the resulting steady-state visual evoked potentials were first introduced over 80 years ago as a means to study visual sensation and perception. From the first single-channel recording of responses to modulated light to the present use of sophisticated digital displays composed of complex visual stimuli and high-density recording arrays, steady-state methods have been applied in a broad range of scientific and applied settings.The purpose of this article is to describe the fundamental stimulation paradigms for steady-state visual evoked potentials and to illustrate these principles through research findings across a range of applications in vision science.
In this paper, a new acquisition protocol is adopted for identifying individuals from electroencephalogram signals based on eye blinking waveforms. For this purpose, a database of 10 subjects is collected using Neurosky Mindwave headset. Then, the eye blinking signal is extracted from brain wave recordings and used for the identification task. The feature extraction stage includes fitting the extracted eye blinks to auto-regressive model. Two algorithms are implemented for auto-regressive modeling namely; Levinson-Durbin and Burg algorithms. Then, discriminant analysis is adopted for classification scheme. Linear and quadratic discriminant functions are tested and compared in this paper. Using Burg algorithm with linear discriminant analysis, the proposed system can identify subjects with best accuracy of 99.8%. The obtained results in this paper confirm that eye blinking waveform carries discriminant information and is therefore appropriate as a basis for person identification methods.
In recent years, Brain Computer Interface (BCI) systems based on Steady-State Visual Evoked Potential (SSVEP) have received much attention. This study tries to develop a SSVEP based BCI system that can control a wheelchair prototype in five different positions including stop position. In this study four different flickering frequencies in low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using Lab-VIEW. Four stimuli colors, green, red, blue and violet were used to investigate the color influence in SSVEPs. The Electroencephalogram (EEG) signals recorded from the occipital region were segmented into 1 second window and features were extracted by using Fast Fourier Transform (FFT). One-Against-All (OAA), a popular strategy for multiclass SVM, is used to classify SSVEP signals. During stimuli color comparison SSVEP with violet color showed higher accuracy than that with green, red and blue stimuli.
In recent years, research on Brain-Computer Interface (BCI) technology for healthy users has attracted considerable interest, and BCI games are especially popular. This study reviews the current status of, and describes future directions, in the field of BCI games. To this end, we conducted a literature search and found that BCI control paradigms using electroencephalographic signals (motor imagery, P300, steady state visual evoked potential and passive approach reading mental state) have been the primary focus of research. We also conducted a survey of nearly three hundred participants that included researchers, game developers and users around the world. From this survey, we found that all three groups (researchers, developers and users) agreed on the significant influence and applicability of BCI and BCI games, and they all selected prostheses, rehabilitation and games as the most promising BCI applications. User and developer groups tended to give low priority to passive BCI and the whole head sensor array. Developers gave higher priorities to “the easiness of playing” and the “development platform” as important elements for BCI games and the market. Based on our assessment, we discuss the critical point at which BCI games will be able to progress from their current stage to widespread marketing to consumers. In conclusion, we propose three critical elements important for expansion of the BCI game market: standards, gameplay and appropriate integration.
The Where's Waldo problem concerns how individuals can rapidly learn to search a scene to detect, attend, recognize, and look at a valued target object in it. This article develops the ARTSCAN Search neural model to clarify how brain mechanisms across the What and Where cortical streams are coordinated to solve the Where's Waldo problem. The What stream learns positionally-invariant object representations, whereas the Where stream controls positionally-selective spatial and action representations. The model overcomes deficiencies of these computationally complementary properties through What and Where stream interactions. Where stream processes of spatial attention and predictive eye movement control modulate What stream processes whereby multiple view- and positionally-specific object categories are learned and associatively linked to view- and positionally-invariant object categories through bottom-up and attentive top-down interactions. Gain fields control the coordinate transformations that enable spatial attention and predictive eye movements to carry out this role. What stream cognitive-emotional learning processes enable the focusing of motivated attention upon the invariant object categories of desired objects. What stream cognitive names or motivational drives can prime a view- and positionally-invariant object category of a desired target object. A volitional signal can convert these primes into top-down activations that can, in turn, prime What stream view- and positionally-specific categories. When it also receives bottom-up activation from a target, such a positionally-specific category can cause an attentional shift in the Where stream to the positional representation of the target, and an eye movement can then be elicited to foveate it. These processes describe interactions among brain regions that include visual cortex, parietal cortex, inferotemporal cortex, prefrontal cortex (PFC), amygdala, basal ganglia (BG), and superior colliculus (SC).
A Brain-Computer Interface (BCI) is a communication system between the brain and a machine like a computer. Some BCI systems have been used to help people with disabilities and sometimes, with entertainment purposes. In this paper, a BCI-game system is developed. It allows controlling the altitude of a ball inside of a glass pipe according to mental concentration level, which is measured on EEG signals of the user. The system is automatically adjusted to each user, hence, it is not needed any calibration step. Ten subjects participated in the experiments. They achieved effective control of the ball in a few minutes, demonstrating the feasibility of the BCI-game system.
A brain-computer interface (BCI) enables communication without movement based on brain signals measured with electroencephalography (EEG). BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP), steady state visual evoked potential (SSVEP), or event related desynchronization (ERD). Although P300 BCIs were introduced over twenty years ago, the past few years have seen a strong increase in P300 BCI research. This closed-loop BCI approach relies on the P300 and other components of the ERP, based on an oddball paradigm presented to the subject. In this paper, we overview the current status of P300 BCI technology, and then discuss new directions: paradigms for eliciting P300s; signal processing methods; applications; and hybrid BCIs. We conclude that P300 BCIs are quite promising, as several emerging directions have not yet been fully explored and could lead to improvements in bit rate, reliability, usability, and flexibility.
This work presents the development of a brain computer interface as an alternative communication channel to be used in Robotics. It encompasses the implementation of an electroencephalograph (EEG), as well as the development of all computational methods and necessary techniques to identify mental activities. The developed brain computer interface (BCI) is applied to activate the movements of a 120lb mobile robot, associating four different mental activities to robot commands. The interface is based on EEG signal analyses, which extract features that can be classified as specific mental activities. First, a signal preprocessing is performed from the EEG data, filtering noise, using a spatial filter to increase the scalp signal resolution, and extracting relevant features. Then, different classifier models are proposed, evaluated and compared. At last, two implementations of the developed classifiers are proposed to improve the rate of successful commands to the mobile robot. In one of the implementations, a 91% average hit rate is obtained, with only 1.25% wrong commands after 400 attempts to control the mobile robot.
Abstract The Gauss-Newton algorithm is an iterative method regularly used for solving nonlinear least squares problems. It is particularly well-suited to the treatment of very large scale variational data assimilation problems that arise in atmosphere and ocean forecasting. The procedure consists of a sequence of linear least squares approximations to the nonlinear problem, each of which is solved by an ‘inner’ direct or iterative process. In comparison with Newton’s method and its variants, the algorithm is attractive because it does not require the evaluation of second-order derivatives in the Hessian of the objective function. In practice the exact Gauss-Newton method,is too expensive to apply operationally in meteorological forecasting and various approximations are made in order to reduce computational costs and to solve the problems in real time. Here we investigate the efiects on the convergence of the Gauss-Newton method of two types of approximation used commonly,in data assimilation. Firstly, we examine ‘truncated’ Gauss-Newton methods where the ‘inner’ linear least squares problem is not solved exactly, and secondly, we examine ‘perturbed’ Gauss-Newton methods where the true linearized ‘inner’ problem is approximated by a simplifled, or perturbed, linear least squares problem. We give conditions ensuring that the truncated and perturbed Gauss-Newton methods converge and also derive rates of convergence for the iterations. The results are illustrated by a simple numerical example. Keywords Nonlinear least squares problems; approximate Gauss-Newton methods; vari-
Brain-computer interface (BCI) systems based on the steady-state visual evoked potential
(SSVEP) provide higher information throughput and require shorter training than BCI systems
using other brain signals. To elicit an SSVEP, a repetitive visual stimulus (RVS) has to be
presented to the user. The RVS can be rendered on a computer screen by alternating graphical
patterns, or with external light sources able to emit modulated light. The properties of an RVS
(e.g., frequency, color) depend on the rendering device and influence the SSVEP characteristics.
This affects the BCI information throughput and the levels of user safety and comfort. Literature
on SSVEP-based BCIs does not generally provide reasons for the selection of the used rendering
devices or RVS properties. In this paper, we review the literature on SSVEP-based BCIs and
comprehensively report on the different RVS choices in terms of rendering devices, properties,
and their potential influence on BCI performance, user safety and comfort.
Although Bayesian analysis has been in use since Laplace, the Bayesian method of model-comparison has only recently been developed in depth. In this paper, the Bayesian approach to regularization and model-comparison is demonstrated by studying the inference problem of interpolating noisy data. The concepts and methods described are quite general and can be applied to many other data modeling problems. Regularizing constants are set by examining their posterior probability distribution. Alternative regularizers (priors) and alternative basis sets are objectively compared by evaluating the evidence for them. “Occam's razor” is automatically embodied by this process. The way in which Bayes infers the values of regularizing constants and noise levels has an elegant interpretation in terms of the effective number of parameters determined by the data set. This framework is due to Gull and Skilling.
The steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has received extensive attention in research for the less training time, excellent recognition performance, and high information translate rate. At present, most of the powerful SSVEPs detection methods are similarity measurements based on spatial filters and Pearson's correlation coefficient. Among them, the task-related component analysis (TRCA)-based method and its variant, the ensemble TRCA (eTRCA)-based method, are two methods with high performance and great potential. However, they have a defect, that is, they can only suppress certain kinds of noise, but not more general noises. To solve this problem, a novel time filter was designed by introducing the temporally local weighting into the objective function of the TRCA-based method and using the singular value decomposition. Based on this, the time filter and (e)TRCA-based similarity measurement methods were proposed, which can perform a robust similarity measure to enhance the detection ability of SSVEPs. A benchmark dataset recorded from 35 subjects was used to evaluate the proposed methods and compare them with the (e)TRCA-based methods. The results indicated that the proposed methods performed significantly better than the (e)TRCA-based methods. Therefore, it is believed that the proposed time filter and the similarity measurement methods have promising potential for SSVEPs detection.
The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily affected by noise and redundant information that exists in the multi-channel electroencephalogram (EEG). To solve this problem, many temporal and spatial feature based channel selection methods have been proposed. However, temporal and spatial features do not accurately reflect changes in the power of the oscillatory EEG. Thus, spectral features of MI-related EEG signals may be useful for channel selection. Bispectrum analysis is a technique developed for extracting non-linear and non-Gaussian information from non-linear and non-Gaussian signals. The features extracted from bispectrum analysis can provide frequency domain information about the EEG. Therefore, in this study, we propose a bispectrumbased channel selection (BCS) method for MI-based BCI. The proposed method uses the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from bispectrum analysis to select EEG channels without redundant information. Three public BCI competition datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) were used to validate the effectiveness of our proposed method. The results indicate that our BCS method outperforms use of all channels (83.8% vs 69.4%, 86.3% vs 82.9% and 77.8% vs 68.2%, respectively). Furthermore, compared to the other state-of-the-art methods, our BCS method also can achieve significantly better classification accuracies for MI-based BCI (Wilcoxon signed test, p<0.05).
The common spatial pattern (CSP) algorithm is a well-recognized spatial filtering method for feature extraction in motor imagery (MI)-based brain-computer interfaces (BCIs). However, due to the influence of nonstationary in electroencephalography (EEG) and inherent defects of the CSP objective function, the spatial filters, and their corresponding features are not necessarily optimal in the feature space used within CSP. In this work, we design a new feature selection method to address this issue by selecting features based on an improved objective function. Especially, improvements are made in suppressing outliers and discovering features with larger interclass distances. Moreover, a fusion algorithm based on the Dempster-Shafer theory is proposed, which takes into consideration the distribution of features. With two competition data sets, we first evaluate the performance of the improved objective functions in terms of classification accuracy, feature distribution, and embeddability. Then, a comparison with other feature selection methods is carried out in both accuracy and computational time. Experimental results show that the proposed methods consume less additional computational cost and result in a significant increase in the performance of MI-based BCI systems.
Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signal-to-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed.
Objective:
Tactile brain-computer interface (BCI) systems can provide new communication and control options for patients with impairments of eye movements or vision. One of the most common modalities used in these BCIs is the P300 potential. Until now, tactile P300 BCIs have been successfully constructed by situating tactile stimuli at various parts of the human body. This study proposed a novel tactile P300 BCI paradigm for further expanding the tactile stimulation methods.
Methods:
In our proposed paradigm, the spatial target vibrotactile stimuli were delivered to subject's left and right cheeks. To validate the feasibility of our proposed paradigm, a traditional tactile P300 BCI paradigm employing spatial target vibrotactile stimuli to subject's left and right wrists was used for comparison.
Results:
The experimental results of nine healthy subjects demonstrated that the proposed paradigm could obtain significantly higher classification accuracy and information transfer rate than the traditional paradigm (both for p < 0.05). Furthermore, the subjective feedback showed that our proposed paradigm was more favored by the subjects compared to the traditional paradigm, and most subjects reported that the new paradigm helped them easily distinguish between targets and non-targets.
Conclusion:
The proposed tactile P300 BCI paradigm is feasible, and can bring about superior performance and use-evaluation.
Significance:
The new paradigm might lead to many promising applications of such BCIs.
P300-based brain-computer interfaces (BCIs) provide an additional communication channel for individuals with communication disabilities. In general, P300-based BCIs need to be trained, offline, for a considerable period of time, which causes users to become fatigued. This reduces the efficiency and performance of the system. In order to shorten calibration time and improve system performance, we introduce the concept of a generic model set. We used ERP data from 116 participants to train the generic model set. The resulting set consists of ten models, which are trained by weighted linear discriminant analysis (WLDA). Twelve new participants were then invited to test the validity of the generic model set. The results demonstrated that all new participants matched the best generic model. The resulting mean classification accuracy equaled 80% after online training, an accuracy that was broadly equivalent to the typical training model method. Moreover, the calibration time was shortened by 70.7% of the calibration time of the typical model method. In other words, the best matching model method only took 81s to calibrate, while the typical model method took 276s. There were also significant differences in both accuracy and raw bit rate between the best and the worst matching model methods. We conclude that the strategy of combining the generic models with online training is easily accepted and achieves higher levels of user satisfaction (as measured by subjective reports). Thus, we provide a valuable new strategy for improving the performance of P300-based BCI.
Multi-channel EEG data are usually necessary for spatial pattern identification in motor imagery (MI)-based brain computer interfaces (BCIs). To some extent, signals from some channels containing redundant information and noise may degrade BCI performance. We assume that the channels related to MI should contain common information when participants are executing the MI tasks. Based on this hypothesis, a correlation-based channel selection (CCS) method is proposed to select the channels that contained more correlated information in this study. The aim is to improve the classification performance of MI-based BCIs. Furthermore, a novel regularized common spatial pattern (RCSP) method is used to extract effective features. Finally, a support vector machine (SVM) classifier with the Radial Basis Function (RBF) kernel is trained to accurately identify the MI tasks. An experimental study is implemented on three public EEG datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) to validate the effectiveness of the proposed methods. The results show that the CCS algorithm obtained superior classification accuracy (78% versus 56.4% for dataset1, 86.6% versus 76.5% for dataset 2 and 91.3% versus 85.1% for dataset 3) compared to the algorithm using all channels (AC), when CSP is used to extract the features. Furthermore, RCSP could further improve the classification accuracy (81.6% for dataset1, 87.4% for dataset2 and 91.9% for dataset 3), when CCS is used to select the channels.
This work introduces a methodology based on deep convolutional neural networks (DCNN) for motor imagery (MI) tasks recognition in the brain-computer interface (BCI) system. More specifically, the DCNN is used for classification of the right hand (RH) and right foot (RF) MI-tasks based electroencephalogram (EEG) signals. The proposed method first transforms the input EEG signals into images by applying the time-frequency (T-F) approaches. The used T-F approaches are short-time-Fourier-transform (STFT) and continuous-wavelet-transform (CWT). After time frequency transformation the images of MI-tasks EEG signals are applied to the DCNN stage. The pre-trained DCNN model, AlexNet is explored for classification. The efficiency of the proposed method is evaluated on IVa dataset of BCI competition-III. The evaluation metrics such as accuracy, sensitivity, specificity, F1-score and kappa value are used for measuring the proposed method results quantitatively. The obtained results show that the CWT approach yields better results than the STFT approach. In addition, the proposed method obtained 99.35% accuracy score is the best one among the existing methods accuracy scores.
We present a novel method to remove eye blink artifacts from the electroencephalogram (EEG) signals, without using electro-oculogram (EOG) reference electrodes. We first model EEG activity by an autoregressive model and eye blink by an output-error model, and then use Kalman filter to estimate the true EEG based on integrating two models. The performance of the proposed method is evaluated based on two different metrics by using Dataset IIa of BCI competition 2008. For RLS algorithm, artifact removal and EEG distortion metrics are 7.35 and 0.79, while for our proposed method these metrics are 9.53 and 0.84, respectively. The results show that our proposed method removes the EOG artifact more efficiently than RLS algorithm. However, the RLS algorithm causes a little less EEG signal distortion.
The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks. The algorithm is tested on several function approximation problems, and is compared with a conjugate gradient algorithm and a variable learning rate algorithm. It is found that the Marquardt algorithm is much more efficient than either of the other techniques when the network contains no more than a few hundred weights.