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Attention Level Measurement During Exoskeleton Rehabilitation Through a BMI System
Abstract
Brain-Machine Interfaces based on wearable robots’ control have been proposed in the research field for rehabilitation purposes. The combination of both systems allow the performance of more natural movements and a higher level of involvement of patients on their therapy. Studies focused on this topic should face several issues related to the integration of these systems. The current work is meant to test the accuracy of a real time Brain-Machine Interface based on the detection of gait attention during lower limb exoskeletal rehabilitation. Four users performed the experiment wearing an ankle exoskeleton. The system provides a coefficient between 0 and 1 depending on the level of attention experienced by the subject. These results show good similitude between real and decoded attention level.
... In the field of rehabilitation, year after year, efforts are increased to create therapies that actively involve the subject in the process [1]. In addition, it is about standardizing these therapies based on biomarkers that allow us to indicate in some way the level of involvement of the subject [2] and the performance of the therapy. Non-invasive brain activity recording technology has proven to be effective in this field [3], proposing a path between the subject's intention and motor action [4]. ...
In the EEG literature, there is a lack of asynchronous intention models that realistically propose interfaces for applications that must operate in real time. In this work, a novel BMI approach to detect in real time the intention to turn is proposed. For this purpose, an offline, pseudo-online and online analysis is presented to validate the EEG as a biomarker for the intention to turn. This article presents a methodology for the creation of a BMI that could differentiate two classes: monotonous walk and intention to turn. A comparison of some of the most popular algorithms in the literature is conducted. To filter the signal, two relevant algorithms are used: H∞ filter and ASR. For processing and classification, the mean of the covariance matrices in the Riemannian space was calculated and then, with various classifiers of different types, the distance of the test samples to each class in the Riemannian space was estimated. This dispenses with power-based models and the necessary baseline correction, which is a problem in realistic scenarios. In the cross-validation for a generic selection (valid for any subject) and a personalized one, the results were, on average, 66.2% and 69.6% with the best filter H∞. For the pseudo-online, the custom configuration for each subject was an average of 40.2% TP and 9.3 FP/min; the best subject obtained 43.9% TP and 2.9 FP/min. In the final validation test, this subject obtained 2.5 FP/min and an accuracy rate of 71.43%, and the turn anticipation was 0.21 s on average.
... Some contributions to the rehabilitative exoskeletons' interfaces field refer to HFE concepts such as usability [8,[24][25][26][27], fatigue [24,26], user engagement [28,29], and workload [30,31]. However, several issues have not been addressed yet. ...
Exoskeletons are wearable robots designed to restore or augment human physical abilities and, indirectly, cognitive functions. These devices can be classified based on the sector of application, the body part they are intended to support or enhance, the degree of assistance, and the source which they gather power from. Regardless of such technical features, exoskeletons are usually equipped with Human-Machine Interfaces (HMIs), allowing users to interact with the system, both physically and cognitively. The current paper critically reviews the state of the art of HMIs, and discusses the future challenges concerning Human Factors issues associated with the experience of utilisation of HMIs for wearable assistive exoskeletons in neuromotor rehabilitation settings. An overview of extant types of rehabilitative exoskeletons’ HMIs is provided, as well as a discussion on novel user experience research questions posed in light of the recent developments in the field.
... Some orthoses do not require an upright standing or walking posture (Xu et al 2014, Zhang et al 2015. Some BMIs do not control robotic devices to walk, although designed in walking context (Salazar-Varas et al 2015, Costa et al 2016a. They were excluded from this review. ...
Objective:
Lower-limb, powered robotics systems such as exoskeletons and orthoses have emerged as novel robotic interventions to assist or rehabilitate people with walking disabilities. These devices are generally controlled by certain physical maneuvers, for example pressing buttons or shifting body weight. Although effective, these control schemes are not what humans naturally use. The usability and clinical relevance of these robotics systems could be further enhanced by brain-machine interfaces (BMIs). A number of preliminary studies have been published on this topic, but a systematic understanding of the experimental design, tasks, and performance of BMI-exoskeleton systems for restoration of gait is lacking.
Approach:
To address this gap, we applied standard systematic review methodology for a literature search in PubMed and EMBASE databases and identified 11 studies involving BMI-robotics systems. The devices, user population, input and output of the BMIs and robot systems respectively, neural features, decoders, denoising techniques, and system performance were reviewed and compared.
Main results:
Results showed BMIs classifying walk versus stand tasks are the most common. The results also indicate that electroencephalography (EEG) is the only recording method for humans. Performance was not clearly presented in most of the studies. Several challenges were summarized, including EEG denoising, safety, responsiveness and others.
Significance:
We conclude that lower-body powered exoskeletons with automated gait intention detection based on BMIs open new possibilities in the assistance and rehabilitation fields, although the current performance, clinical benefits and several key challenging issues indicate that additional research and development is required to deploy these systems in the clinic and at home. Moreover, rigorous EEG denoising techniques, suitable performance metrics, consistent trial reporting, and more clinical trials are needed to advance the field.
Spinal cord injury (SCI) limits life expectancy and causes a restriction of patient's daily activities. In the last years, robotics exoskeletons have appeared as a promising rehabilitation and assistance tool for patients with motor limitations, as people that have suffered a SCI. The usability and clinical relevance of these robotics systems could be further enhanced by brain-machine interfaces (BMIs), as they can be used to foster patients' neuroplasticity. However, there are not many studies showing the use of BMIs to control exoskeletons with patients. In this work we show a case study where one SCI patient has used a BMI based on motor imagery (MI) in order to control a lower limb exoskeleton that assists their gait.
The paper compares different signal processing algorithms and classifiers to evaluate the accuracy of a BMI based on lower-limb motor imagery. The methods were based on the analysis of the peaks of the different processing epochs for the alpha, beta and gamma EEG bands through the Marginal Hilbert Spectrum, Power Spectral Density and Fourier harmonic components. Data were classified and analyzed by three classifiers: Support Vector Machine, Self-Organizing Maps and Linear Discriminator analysis. Results show accuracy is dependent on the subject, but there is not dependency between the subjects and the methods, and classifiers. Best accuracy results were achieved by using the value of the peak of the Hilbert Marginal Spectrum, obtaining the analytical signal with the Stockwell transform. Regarding the classifiers SOM presented lower accuracy values than SVM and LDA.
So far, Brain-Machine Interfaces (BMIs) have been mainly used to study brain potentials during movement-free conditions. Recently, due to the emerging concern of improving rehabilitation therapies, these systems are also being used during gait experiments. Under this new condition, the evaluation of motion artifacts has become a critical point to assure the validity of the results obtained. Due to the high signal to noise ratio provided, the use of wet electrodes is a widely accepted technic to acquire electroencephalographic (EEG signals). To perform these recordings it is necessary to apply a conductive gel between the scalp and the electrodes. This work is focused on the study of gel displacements produced during ambulation and how they affect the amplitude of EEG signals. Data recorded during three ambulation conditions (gait training) and one movement-free condition (BMI motor imagery task) are compared to perform this study. Two phenomenons, manifested as unusual increases of the signals' amplitude, have been identified and characterized during this work. Results suggest that they are caused by abrupt changes on the conductivity between the electrode and the scalp due to gel displacement produced during ambulation and head movements. These artifacts significantly increase the Power Spectral Density (PSD) of EEG recordings at all frequencies from 5 to 90 Hz, corresponding to the main bandwidth of electrocortical potentials. They should be taken into consideration before performing EEG recordings in order to asses the correct gel allocation and to avoid the use of electrodes on certain scalp areas depending on the experimental conditions.
Objective:
Chronic stroke patients with severe hand weakness respond poorly to rehabilitation efforts. Here, we evaluated efficacy of daily brain-machine interface (BMI) training to increase the hypothesized beneficial effects of physiotherapy alone in patients with severe paresis in a double-blind sham-controlled design proof of concept study.
Methods:
Thirty-two chronic stroke patients with severe hand weakness were randomly assigned to 2 matched groups and participated in 17.8 ± 1.4 days of training rewarding desynchronization of ipsilesional oscillatory sensorimotor rhythms with contingent online movements of hand and arm orthoses (experimental group, n = 16). In the control group (sham group, n = 16), movements of the orthoses occurred randomly. Both groups received identical behavioral physiotherapy immediately following BMI training or the control intervention. Upper limb motor function scores, electromyography from arm and hand muscles, placebo-expectancy effects, and functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent activity were assessed before and after intervention.
Results:
A significant group × time interaction in upper limb (combined hand and modified arm) Fugl-Meyer assessment (cFMA) motor scores was found. cFMA scores improved more in the experimental than in the control group, presenting a significant improvement of cFMA scores (3.41 ± 0.563-point difference, p = 0.018) reflecting a clinically meaningful change from no activity to some in paretic muscles. cFMA improvements in the experimental group correlated with changes in fMRI laterality index and with paretic hand electromyography activity. Placebo-expectancy scores were comparable for both groups.
Interpretation:
The addition of BMI training to behaviorally oriented physiotherapy can be used to induce functional improvements in motor function in chronic stroke patients without residual finger movements and may open a new door in stroke neurorehabilitation.
In this paper, a novel independent brain-computer interface (BCI) system based on covert non-spatial visual selective attention of two superimposed illusory surfaces is described. Perception of two superimposed surfaces was induced by two sets of dots with different colors rotating in opposite directions. The surfaces flickered at different frequencies and elicited distinguishable steady-state visual evoked potentials (SSVEPs) over parietal and occipital areas of the brain. By selectively attending to one of the two surfaces, the SSVEP amplitude at the corresponding frequency was enhanced. An online BCI system utilizing the attentional modulation of SSVEP was implemented and a 3-day online training program with healthy subjects was carried out. The study was conducted with Chinese subjects at Tsinghua University, and German subjects at University Medical Center Hamburg-Eppendorf (UKE) using identical stimulation software and equivalent technical setup. A general improvement of control accuracy with training was observed in 8 out of 18 subjects. An averaged online classification accuracy of 72.6 +/- 16.1% was achieved on the last training day. The system renders SSVEP-based BCI paradigms possible for paralyzed patients with substantial head or ocular motor impairments by employing covert attention shifts instead of changing gaze direction.
A broad discussion is presented of spectral estimation techniques and
their implementation. The topics addressed include: reviews of linear
systems, transform theory, matrix algebra, and random process theory;
classical spectral estimation; parametric models of random processes;
autoregressive process and spectrum properties; block data algorithms
and sequential data algorithms in autoregressive spectral estimation.
Also discussed are: autoregressive-moving average spectral estimation;
Prony's method; minimum variance spectral estimation;
eigenanalysis-based frequency estimation; summary of spectral
estimators; multichannel spectral estimation; and two-dimensional
spectral estimation.
Brain-computer interface (BCI) is a developing, novel mode of communication for individuals with severe motor impairments or those who have no other options for communication aside from their brain signals. However, the majority of current BCI systems are based on visual stimuli or visual feedback, which may not be applicable for severe locked-in patients that have lost their eyesight or the ability to control their eye movements. In the present study, we investigated the feasibility of using auditory steady-state responses (ASSRs), elicited by selective attention to a specific sound source, as an electroencephalography (EEG)-based BCI paradigm. In our experiment, two pure tone burst trains with different beat frequencies (37 and 43 Hz) were generated simultaneously from two speakers located at different positions (left and right). Six participants were instructed to close their eyes and concentrate their attention on either auditory stimulus according to the instructions provided randomly through the speakers during the inter-stimulus interval. EEG signals were recorded at multiple electrodes mounted over the temporal, occipital, and parietal cortices. We then extracted feature vectors by combining spectral power densities evaluated at the two beat frequencies. Our experimental results showed high classification accuracies (64.67%, 30 commands/min, information transfer rate (ITR) = 1.89 bits/min; 74.00%, 12 commands/min, ITR = 2.08 bits/min; 82.00%, 6 commands/min, ITR = 1.92 bits/min; 84.33%, 3 commands/min, ITR = 1.12 bits/min; without any artifact rejection, inter-trial interval = 6s), enough to be used for a binary decision. Based on the suggested paradigm, we implemented a first online ASSR-based BCI system that demonstrated the possibility of materializing a totally vision-free BCI system.
Neuronal plasticity allows the central nervous system to learn skills and remember information, to reorganize neuronal networks in response to environmental stimulation, and to recover from brain and spinal cord injuries. Neuronal plasticity is enhanced in the developing brain and it is usually adaptive and beneficial but can also be maladaptive and responsible for neurological disorders in some situations. Basic mechanisms that are involved in plasticity include neurogenesis, programmed cell death, and activity-dependent synaptic plasticity. Repetitive stimulation of synapses can cause long-term potentiation or long-term depression of neurotransmission. These changes are associated with physical changes in dendritic spines and neuronal circuits. Overproduction of synapses during postnatal development in children contributes to enhanced plasticity by providing an excess of synapses that are pruned during early adolescence. Clinical examples of adaptive neuronal plasticity include reorganization of cortical maps of the fingers in response to practice playing a stringed instrument and constraint-induced movement therapy to improve hemiparesis caused by stroke or cerebral palsy. These forms of plasticity are associated with structural and functional changes in the brain that can be detected with magnetic resonance imaging, positron emission tomography, or transcranial magnetic stimulation (TMS). TMS and other forms of brain stimulation are also being used experimentally to enhance brain plasticity and recovery of function. Plasticity is also influenced by genetic factors such as mutations in brain-derived neuronal growth factor. Understanding brain plasticity provides a basis for developing better therapies to improve outcome from acquired brain injuries.
Frequency and number of neighbors study for attention level classification using eeg signals
- A Costa
- E Iáñez
- A Úbeda
- D Planelles
- E Hortal
- J M Azorín