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ABSTRACT: Objective. The non-stationary nature of EEG poses a major challenge to robust operation of brain-computer interfaces (BCIs). The objective of this paper is to propose and investigate a computational method to address non-stationarity in EEG classification. Approach. We developed a novel dynamically weighted ensemble classification (DWEC) framework whereby an ensemble of multiple classifiers are trained on clustered features. The decisions from these multiple classifiers are dynamically combined based on the distances of the cluster centres to each test data sample being classified. Main Results. The clusters of the feature space from the second session spanned a different space compared to the clusters of the feature space from the first session which highlights the processes of session-to-session non-stationarity. The session-to-session performance of the proposed DWEC method was evaluated on two datasets. The results on publicly available BCI Competition IV dataset 2A yielded a significantly higher mean accuracy of 81.48% compared to 75.9% from the baseline support vector machine (SVM) classifier without dynamic weighting. Results on the data collected from our twelve in-house subjects yielded a significantly higher mean accuracy of 73% compared to 69.4% from the baseline SVM classifier without dynamic weighting. Significance. The cluster based analysis provides insight into session-to-session non-stationarity in EEG data. The results demonstrate the effectiveness of the proposed method in addressing non-stationarity in EEG data for the operation of a BCI.
Journal of Neural Engineering 04/2013; 10(3):036007. · 3.84 Impact Factor
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ABSTRACT: Attention deficit hyperactivity disorder (ADHD) symptoms can be difficult to treat. We previously reported that a 20-session brain-computer interface (BCI) attention training programme improved ADHD symptoms. Here, we investigated a new more intensive BCI-based attention training game system on 20 unmedicated ADHD children (16 males, 4 females) with significant inattentive symptoms (combined and inattentive ADHD subtypes). This new system monitored attention through a head band with dry EEG sensors, which was used to drive a feed forward game. The system was calibrated for each user by measuring the EEG parameters during a Stroop task. Treatment consisted of an 8-week training comprising 24 sessions followed by 3 once-monthly booster training sessions. Following intervention, both parent-rated inattentive and hyperactive-impulsive symptoms on the ADHD Rating Scale showed significant improvement. At week 8, the mean improvement was -4.6 (5.9) and -4.7 (5.6) respectively for inattentive symptoms and hyperactive-impulsive symptoms (both p<0.01). Cohen's d effect size for inattentive symptoms was large at 0.78 at week 8 and 0.84 at week 24 (post-boosters). Further analysis showed that the change in the EEG based BCI ADHD severity measure correlated with the change ADHD Rating Scale scores. The BCI-based attention training game system is a potential new treatment for ADHD. TRIAL REGISTRATION: ClinicalTrials.gov NCT01344044.
PLoS ONE 01/2012; 7(10):e46692. · 4.09 Impact Factor
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ABSTRACT: Detecting motor imagery activities versus non-control in brain signals is the basis of self-paced brain-computer interfaces (BCIs), but also poses a considerable challenge to signal processing due to the complex and non-stationary characteristics of motor imagery as well as non-control. This paper presents a self-paced BCI based on a robust learning mechanism that extracts and selects spatio-spectral features for differentiating multiple EEG classes. It also employs a non-linear regression and post-processing technique for predicting the time-series of class labels from the spatio-spectral features. The method was validated in the BCI Competition IV on Dataset I where it produced the lowest prediction error of class labels continuously. This report also presents and discusses analysis of the method using the competition data set.
Frontiers in Neuroscience 01/2012; 6:7.
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ABSTRACT: The Common Spatial Pattern (CSP) algorithm is an effective and popular method for classifying 2-class motor imagery electroencephalogram (EEG) data, but its effectiveness depends on the subject-specific frequency band. This paper presents the Filter Bank Common Spatial Pattern (FBCSP) algorithm to optimize the subject-specific frequency band for CSP on Datasets 2a and 2b of the Brain-Computer Interface (BCI) Competition IV. Dataset 2a comprised 4 classes of 22 channels EEG data from 9 subjects, and Dataset 2b comprised 2 classes of 3 bipolar channels EEG data from 9 subjects. Multi-class extensions to FBCSP are also presented to handle the 4-class EEG data in Dataset 2a, namely, Divide-and-Conquer (DC), Pair-Wise (PW), and One-Versus-Rest (OVR) approaches. Two feature selection algorithms are also presented to select discriminative CSP features on Dataset 2b, namely, the Mutual Information-based Best Individual Feature (MIBIF) algorithm, and the Mutual Information-based Rough Set Reduction (MIRSR) algorithm. The single-trial classification accuracies were presented using 10 × 10-fold cross-validations on the training data and session-to-session transfer on the evaluation data from both datasets. Disclosure of the test data labels after the BCI Competition IV showed that the FBCSP algorithm performed relatively the best among the other submitted algorithms and yielded a mean kappa value of 0.569 and 0.600 across all subjects in Datasets 2a and 2b respectively.
Frontiers in Neuroscience 01/2012; 6:39.
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Pattern Recognition. 01/2012; 45:2137-2144.
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ABSTRACT: Brain-computer interface (BCI) technology has the prospects of helping stroke survivors by enabling the interaction with their environ ment through brain signals rather than through muscles, and restoring motor function by inducing activity-dependent brain plasticity. This paper presents a clinical study on the extent of detectable brain signals from a large population of stroke patients in using EEG-based motor imagery BCI. EEG data were collected from 54 stroke patients whereby finger tapping and motor imagery of the stroke-affected hand were performed by 8 and 46 patients, respectively. EEG data from 11 patients who gave further consent to perform motor imagery were also collected for second calibration and third independent test sessions conducted on separate days. Off-line accuracies of classifying the two classes of EEG from finger tapping or motor imagery of the stroke-affected hand versus the EEG from background rest were then assessed and compared to 16 healthy subjects. The mean off-line accuracy of detecting motor imagery by the 46 patients (mu=0.74) was significantly lower than finger tapping by 8 patients (mu=0.87, p=0.008), but not significantly lower than motor imagery by healthy subjects (mu=0.78, p=0.23). Six stroke patients performed motor imagery at chance level, and no correlation was found between the accuracies of detecting motor imagery and their motor impairment in terms of Fugl-Meyer Assessment (p=0.29). The off-line accuracies of the 11 patients in the second session (mu=0.76) were not significantly different from the first session (mu=0.72, p=0.16), or from the on-line accuracies of the third independent test session (mu=0.82, p=0.14). Hence this study showed that the majority of stroke patients could use EEG-based motor imagery BCI.
Clinical EEG and neuroscience: official journal of the EEG and Clinical Neuroscience Society (ENCS) 10/2011; 42(4):253-8. · 1.73 Impact Factor
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ABSTRACT: Inherent changes that appear in brain signals when transferring from calibration to feedback sessions are a challenging but critical issue in brain-computer interface (BCI) applications. While previous studies have mostly focused on the adaptation of classifiers, in this paper we study the feasibility and the importance of the adaptation of feature extraction in a self-paced BCI paradigm. First, we conduct calibration and feedback training on able-bodied naïve subjects using a new self-paced motor imagery BCI including the idle state. The online results suggest that the feature space constructed from calibration data may become ineffective during feedback sessions. Hence, we propose a new supervised method that learns from a feedback session to construct a more appropriate feature space, on the basis of the maximum mutual information principle between feedback signal, target signal and EEG. Specifically, we formulate the learning objective as maximizing a kernel-based mutual information estimate with respect to the spatial-spectral filtering parameters. We then derive a gradient-based optimization algorithm for the learning task. An experimental study is conducted using offline simulation. The results show that the proposed method is able to construct effective feature spaces to capture the discriminative information in feedback training data and, consequently, the prediction error can be significantly reduced using the new features.
Journal of Neural Engineering 08/2011; 8(4):046035. · 3.84 Impact Factor
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ABSTRACT: This paper proposes a feature extraction method for motor imagery brain-computer interface (BCI) using electroencephalogram. We consider the primary neurophysiologic phenomenon of motor imagery, termed event-related desynchronization, and formulate the learning task for feature extraction as maximizing the mutual information between the spatio-spectral filtering parameters and the class labels. After introducing a nonparametric estimate of mutual information, a gradient-based learning algorithm is devised to efficiently optimize the spatial filters in conjunction with a band-pass filter. The proposed method is compared with two existing methods on real data: a BCI Competition IV dataset as well as our data collected from seven human subjects. The results indicate the superior performance of the method for motor imagery classification, as it produced higher classification accuracy with statistical significance (≥95% confidence level) in most cases.
IEEE Transactions on Neural Networks 02/2011; · 2.95 Impact Factor
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Pattern Recognition. 01/2011; 44:877-885.
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ABSTRACT: While brain-computer interfaces (BCIs) can provide communication to people who are locked-in, they suffer from a very low information transfer rate. Further, using a BCI requires a concentration effort and using it continuously can be tiring. The brain controlled wheelchair (BCW) described in this paper aims at providing mobility to BCI users despite these limitations, in a safe and efficient way. Using a slow but reliable P300 based BCI, the user selects a destination amongst a list of predefined locations. While the wheelchair moves on virtual guiding paths ensuring smooth, safe, and predictable trajectories, the user can stop the wheelchair by using a faster BCI. Experiments with nondisabled subjects demonstrated the efficiency of this strategy. Brain control was not affected when the wheelchair was in motion, and the BCW enabled the users to move to various locations in less time and with significantly less control effort than other control strategies proposed in the literature.
IEEE Transactions on Neural Systems and Rehabilitation Engineering 01/2011; · 3.44 Impact Factor
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ABSTRACT: In this paper, a hybrid EEG-based brain computer interface (BCI) is designed for two-dimensional cursor control. In our approach, two brain activity patterns, i.e., motor imagery and P300 potential, are used for controlling the horizontal and the vertical movements of the cursor respectively. A real-time BCI system based on this approach is implemented and evaluated through an online experiment. Six subjects attending this experiment can perform 2-D cursor control effectively. Our experimental results show that the system has the following merits compared with prior systems: 1) it does not rely on intensive user training; 2) it allows cursor movement between arbitrary positions.
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
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ABSTRACT: This clinical study investigates the ability of hemiparetic stroke patients in operating EEG-based motor imagery brain-computer interface (MI-BCI). It also assesses the efficacy in motor improvements on the stroke-affected upper limb using EEG-based MI-BCI with robotic feedback neurorehabilitation compared to robotic rehabilitation that delivers movement therapy. 54 hemiparetic stroke patients with mean age of 51.8 and baseline Fugl-Meyer Assessment (FMA) 14.9 (out of 66, higher = better) were recruited. Results showed that 48 subjects (89%) operated EEG-based MI-BCI better than at chance level, and their ability to operate EEG-based MI-BCI is not correlated to their baseline FMA (r=0.358). Those subjects who gave consent are randomly assigned to each group (N=11 and 14) for 12 1-hour rehabilitation sessions for 4 weeks. Significant gains in FMA scores were observed in both groups at post-rehabilitation (4.5, 6.2; p=0.032, 0.003) and 2-month post-rehabilitation (5.3, 7.3; p=0.020, 0.013), but no significant differences were observed between groups (p=0.512, 0.550). Hence, this study showed evidences that a majority of hemiparetic stroke patients can operate EEG-based MI-BCI, and that EEG-based MI-BCI with robotic feedback neurorehabilitation is effective in restoring upper extremities motor function in stroke.
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
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ABSTRACT: This paper addresses an important issue in a self-paced brain-computer interface (BCI): constructing subject-specific continuous control signal. To this end, we propose an alternative to the conventional regression/classification-based mechanism for building the transformation from EEG features into a univariate control signal. Based on information theory, the mechanism formulates the optimum transformation as maximizing the mutual information between the control signal and the mental state. We introduce a non-parametric mutual information estimate for general output distribution, and then develop a gradient-based algorithm to optimize the transformation using training data. We conduct an offline simulation study using motor imagery data from the BCI Competition IV Data Set I. The results show that the learning algorithm converged quickly, and the proposed method yielded significantly higher BCI performance than the conventional mechanism.
Journal of Neural Engineering 10/2010; 7(5):056009. · 3.84 Impact Factor
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ABSTRACT: Two-dimensional cursor control is an important and challenging issue in EEG-based brain-computer interfaces (BCIs). To address this issue, here we propose a new approach by combining two brain signals including Mu/Beta rhythm during motor imagery and P300 potential. In particular, a motor imagery detection mechanism and a P300 potential detection mechanism are devised and integrated such that the user is able to use the two signals to control, respectively, simultaneously, and independently, the horizontal and the vertical movements of the cursor in a specially designed graphic user interface. A real-time BCI system based on this approach is implemented and evaluated through an online experiment involving six subjects performing 2-D control tasks. The results attest to the efficacy of obtaining two independent control signals by the proposed approach. Furthermore, the results show that the system has merit compared with prior systems: it allows cursor movement between arbitrary positions.
IEEE transactions on bio-medical engineering 10/2010; 57(10):2495-505. · 2.15 Impact Factor
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ABSTRACT: Optimum linear transformation under mixture of zero-mean Gaussian conditions is an intriguing problem, especially in learning discriminative spatial components in motor imagery EEG for building brain computer interfaces. However, it is not well addressed in the past. In this paper, we study optimum linear transformation under mixture of zero-mean Gaussian. In particular, we formulate optimum transformation as a Bhattacharyya error bound minimization problem, and derive a numerical solution to estimate the bound from training samples. Based on the solution, we develop an algorithm for selecting optimum linear transformation. The proposed method is evaluated, in comparison with the state-of-the-art methods, using a publicly available data set of motor imagery EEG. The results attest to the superiority of the method for detecting motor imagery.
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on; 04/2010 · 4.63 Impact Factor
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20th International Conference on Pattern Recognition, ICPR 2010, Istanbul, Turkey, 23-26 August 2010; 01/2010
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ABSTRACT: Majority of children with attention deficit hyperactivity disorder (ADHD) have significant inattentive symptoms. We developed a progressive series of activities involving brain-computer interface-based games which could train users to improve their concentration. This pilot study investigated if the intervention could be utilized in children and if it could improve inattentive symptoms of ADHD. Ten medication-naive children aged 7 to 12 diagnosed with ADHD (combined or inattentive subtypes) received 20 sessions of therapy over a 10-week period. They were compared with age- and gendermatched controls. Both parent and teacher-rated inattentive score on the ADHD Rating Scale-IV improved more in the intervention group. A larger scale trial is warranted to further investigate the efficacy of our treatment programme in treating ADHD.
Psychopharmacology bulletin 01/2010; 43(1):73-82. · 1.35 Impact Factor
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ABSTRACT: In this paper, a hybrid EEG-based brain computer interface (BCI) is designed for two-dimensional cursor control. In our approach, two brain activity patterns, i.e., motor imagery and P300 potential, are used for controlling the horizontal and the vertical movements of the cursor respectively. A real-time BCI system based on this approach is implemented and evaluated through an online experiment. Six subjects attending this experiment can perform 2-D cursor control effectively. Our experimental results show that the system has the following merits compared with prior systems: 1) it does not rely on intensive user training; 2) it allows cursor movement between arbitrary positions.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2010; 2010:4205-8.
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ABSTRACT: This clinical study investigates the ability of hemiparetic stroke patients in operating EEG-based motor imagery brain-computer interface (MI-BCI). It also assesses the efficacy in motor improvements on the stroke-affected upper limb using EEG-based MI-BCI with robotic feedback neurorehabilitation compared to robotic rehabilitation that delivers movement therapy. 54 hemiparetic stroke patients with mean age of 51.8 and baseline Fugl-Meyer Assessment (FMA) 14.9 (out of 66, higher = better) were recruited. Results showed that 48 subjects (89%) operated EEG-based MI-BCI better than at chance level, and their ability to operate EEG-based MI-BCI is not correlated to their baseline FMA (r=0.358). Those subjects who gave consent are randomly assigned to each group (N=11 and 14) for 12 1-hour rehabilitation sessions for 4 weeks. Significant gains in FMA scores were observed in both groups at post-rehabilitation (4.5, 6.2; p=0.032, 0.003) and 2-month post-rehabilitation (5.3, 7.3; p=0.020, 0.013), but no significant differences were observed between groups (p=0.512, 0.550). Hence, this study showed evidences that a majority of hemiparetic stroke patients can operate EEG-based MI-BCI, and that EEG-based MI-BCI with robotic feedback neurorehabilitation is effective in restoring upper extremities motor function in stroke.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2010; 2010:5549-52.
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ABSTRACT: Non-invasive EEG-based motor imagery brain-computer interface (MI-BCI) holds promise to effectively restore motor control to stroke survivors. This clinical study investigates the effects of MI-BCI for upper limb robotic rehabilitation compared to standard robotic rehabilitation. The subjects are hemiparetic stroke patients with mean age of 50.2 and baseline Fugl-Meyer (FM) score 29.7 (out of 66, higher = better) randomly assigned to each group respectively (N=8 and 10). Each subject underwent 12 sessions of 1-hour rehabilitation for 4 weeks. Significant gains in FM scores were observed in both groups at post-rehabilitation (4.9, p=0.001) and 2-month post-rehabilitation (4.9, p=0.002). The experimental group yielded higher 2-month post-rehabilitation gain than the control (6.0 versus 4.0) but no significance was found (p=0.475). However, among subjects with positive gain (N=6 and 7), the initial difference of 2.8 between the two groups was increased to a significant 6.5 (p=0.019) after adjustment for age and gender. Hence this study provides evidence that BCI-driven robotic rehabilitation is effective in restoring motor control for stroke.
Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE; 10/2009
