The Experts below are selected from a list of 16689 Experts worldwide ranked by ideXlab platform
Kai Keng Ang - One of the best experts on this subject based on the ideXlab platform.
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optimizing low frequency Common Spatial Pattern features for multi class classification of hand movement directions
International Conference of the IEEE Engineering in Medicine and Biology Society, 2013Co-Authors: Kai Keng Ang, Keng Peng Tee, Cuntai GuanAbstract:Recent studies have demonstrated that hand movement directions can be decoded from low-frequency electroencephalographic (EEG) signals. This paper proposes a novel framework that can optimally select dyadic filter bank Common Spatial Pattern (CSP) features in low-frequency band (0-8 Hz) for multi-class classification of four orthogonal hand movement directions. The proposed framework encompasses EEG signal enhancement, dyadic filter bank CSP feature extraction, fuzzy mutual information (FMI)-based feature selection, and one-versus-rest Fisher's linear discriminant analysis. Experimental results on data collected from seven human subjects show that (1) signal enhancement can boost accuracy by at least 4%; (2) low-frequency band (0-8 Hz) can adequately and effectively discriminate hand movement directions; and (3) dyadic filter bank CSP feature extraction and FMI-based feature selection are indispensable for analyzing hand movement directions, increasing accuracy by 6.06%, from 60.02% to 66.08%.
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EMBC - Optimizing low-frequency Common Spatial Pattern features for multi-class classification of hand movement directions
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2013Co-Authors: Kai Keng Ang, Keng Peng Tee, Cuntai GuanAbstract:Recent studies have demonstrated that hand movement directions can be decoded from low-frequency electroencephalographic (EEG) signals. This paper proposes a novel framework that can optimally select dyadic filter bank Common Spatial Pattern (CSP) features in low-frequency band (0-8 Hz) for multi-class classification of four orthogonal hand movement directions. The proposed framework encompasses EEG signal enhancement, dyadic filter bank CSP feature extraction, fuzzy mutual information (FMI)-based feature selection, and one-versus-rest Fisher's linear discriminant analysis. Experimental results on data collected from seven human subjects show that (1) signal enhancement can boost accuracy by at least 4%; (2) low-frequency band (0-8 Hz) can adequately and effectively discriminate hand movement directions; and (3) dyadic filter bank CSP feature extraction and FMI-based feature selection are indispensable for analyzing hand movement directions, increasing accuracy by 6.06%, from 60.02% to 66.08%.
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a modified wavelet Common Spatial Pattern method for decoding hand movement directions in brain computer interfaces
International Joint Conference on Neural Network, 2012Co-Authors: Neethu Robinso, A. P. Vinod, Kai Keng Ang, Cuntai Gua, Tee Keng PengAbstract:The decoding of hand movement kinematics using non-invasive data acquisition techniques is a recent area of research in Brain Computer Interface (BCI). In this work, we use an Electroencephalography (EEG) based BCI to decode directional information from the brain data collected during an actual hand movement experiment. The objective is to find the discriminative features of movement related potential that can classify any two directions out of the four orthogonal directions in which subject performs right hand movement. The performance using Wavelet-Common Spatial Pattern (W-CSP) algorithm and its variations in terms of Spatial regularization is studied and compared. The work further analyzes the involvement of frontal, parietal and motor regions in carrying movement kinematics information with the help of Spatial plots given by CSP. The performance variability for different directions in various subjects is another important observation in our results. The work aims to provide a more refined movement control command set for BCIs by developing efficient techniques to decode the direction of movement.
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filter bank Common Spatial Pattern algorithm on bci competition iv datasets 2a and 2b
Frontiers in Neuroscience, 2012Co-Authors: Kai Keng Ang, Cuntai Guan, Zheng Yang Chin, Chuanchu Wang, Haihong ZhangAbstract: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 10x10-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.
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multi frequency band Common Spatial Pattern with sparse optimization in brain computer interface
International Conference on Acoustics Speech and Signal Processing, 2012Co-Authors: Mahnaz Arvaneh, Kai Keng Ang, Cuntai Guan, Chai QuekAbstract:In motor imagery-based Brain Computer Interfaces (BCIs), Common Spatial Pattern (CSP) algorithm is widely used for extracting discriminative Patterns from the EEG signals. However, the CSP algorithm is known to be sensitive to noise and artifacts, and its performance greatly depends on the operational frequency band. To address these issues, this paper proposes a novel Sparse Multi-Frequency Band CSP (SMFBCSP) algorithm optimized using a mutual information-based approach. Compared to the use of the cross-validation-based method which finds the regularization parameters by trial and error, the proposed mutual information-based approach directly computes the optimal regularization parameters such that the computational time is substantially reduced. The experimental results on 11 stroke patients showed that the proposed SMFBCSP significantly outperformed three existing algorithms based on CSP, sparse CSP and filter bank CSP in terms of classification accuracy.
Cuntai Guan - One of the best experts on this subject based on the ideXlab platform.
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optimizing low frequency Common Spatial Pattern features for multi class classification of hand movement directions
International Conference of the IEEE Engineering in Medicine and Biology Society, 2013Co-Authors: Kai Keng Ang, Keng Peng Tee, Cuntai GuanAbstract:Recent studies have demonstrated that hand movement directions can be decoded from low-frequency electroencephalographic (EEG) signals. This paper proposes a novel framework that can optimally select dyadic filter bank Common Spatial Pattern (CSP) features in low-frequency band (0-8 Hz) for multi-class classification of four orthogonal hand movement directions. The proposed framework encompasses EEG signal enhancement, dyadic filter bank CSP feature extraction, fuzzy mutual information (FMI)-based feature selection, and one-versus-rest Fisher's linear discriminant analysis. Experimental results on data collected from seven human subjects show that (1) signal enhancement can boost accuracy by at least 4%; (2) low-frequency band (0-8 Hz) can adequately and effectively discriminate hand movement directions; and (3) dyadic filter bank CSP feature extraction and FMI-based feature selection are indispensable for analyzing hand movement directions, increasing accuracy by 6.06%, from 60.02% to 66.08%.
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EMBC - Optimizing low-frequency Common Spatial Pattern features for multi-class classification of hand movement directions
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2013Co-Authors: Kai Keng Ang, Keng Peng Tee, Cuntai GuanAbstract:Recent studies have demonstrated that hand movement directions can be decoded from low-frequency electroencephalographic (EEG) signals. This paper proposes a novel framework that can optimally select dyadic filter bank Common Spatial Pattern (CSP) features in low-frequency band (0-8 Hz) for multi-class classification of four orthogonal hand movement directions. The proposed framework encompasses EEG signal enhancement, dyadic filter bank CSP feature extraction, fuzzy mutual information (FMI)-based feature selection, and one-versus-rest Fisher's linear discriminant analysis. Experimental results on data collected from seven human subjects show that (1) signal enhancement can boost accuracy by at least 4%; (2) low-frequency band (0-8 Hz) can adequately and effectively discriminate hand movement directions; and (3) dyadic filter bank CSP feature extraction and FMI-based feature selection are indispensable for analyzing hand movement directions, increasing accuracy by 6.06%, from 60.02% to 66.08%.
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filter bank Common Spatial Pattern algorithm on bci competition iv datasets 2a and 2b
Frontiers in Neuroscience, 2012Co-Authors: Kai Keng Ang, Cuntai Guan, Zheng Yang Chin, Chuanchu Wang, Haihong ZhangAbstract: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 10x10-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.
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multi frequency band Common Spatial Pattern with sparse optimization in brain computer interface
International Conference on Acoustics Speech and Signal Processing, 2012Co-Authors: Mahnaz Arvaneh, Kai Keng Ang, Cuntai Guan, Chai QuekAbstract:In motor imagery-based Brain Computer Interfaces (BCIs), Common Spatial Pattern (CSP) algorithm is widely used for extracting discriminative Patterns from the EEG signals. However, the CSP algorithm is known to be sensitive to noise and artifacts, and its performance greatly depends on the operational frequency band. To address these issues, this paper proposes a novel Sparse Multi-Frequency Band CSP (SMFBCSP) algorithm optimized using a mutual information-based approach. Compared to the use of the cross-validation-based method which finds the regularization parameters by trial and error, the proposed mutual information-based approach directly computes the optimal regularization parameters such that the computational time is substantially reduced. The experimental results on 11 stroke patients showed that the proposed SMFBCSP significantly outperformed three existing algorithms based on CSP, sparse CSP and filter bank CSP in terms of classification accuracy.
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Spatially sparsed Common Spatial Pattern to improve bci performance
International Conference on Acoustics Speech and Signal Processing, 2011Co-Authors: Mahnaz Arvaneh, Kai Keng Ang, Cuntai Guan, Hiok Chai QuekAbstract:Common Spatial Pattern (CSP) is widely used in discriminating two classes of EEG in Brain Computer Interface applications. However, the performance of the CSP algorithm is affected by noise and artifacts, and the problem is more pronounced in small training data. To overcome these drawbacks, this paper proposes a new Spatially Sparsed CSP (SS-CSP) algorithm by inducing sparsity in the Spatial filters. The proposed algorithm optimizes the Spatial filters to emphasize the regions that have high variances between classes, and attenuates the regions with low or irregular variances which can be due to noise or artifacts. The experimental results on 14 subjects from publicly available BCI competition datasets showed that the proposed SSCSP algorithm significantly improved the performance of the subjects with poor CSP accuracy by an average of 11%. The results also showed that the obtained sparse Spatial filters are more neurophysilogically relevant.
Haihong Zhang - One of the best experts on this subject based on the ideXlab platform.
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filter bank Common Spatial Pattern algorithm on bci competition iv datasets 2a and 2b
Frontiers in Neuroscience, 2012Co-Authors: Kai Keng Ang, Cuntai Guan, Zheng Yang Chin, Chuanchu Wang, Haihong ZhangAbstract: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 10x10-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.
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multi class filter bank Common Spatial Pattern for four class motor imagery bci
International Conference of the IEEE Engineering in Medicine and Biology Society, 2009Co-Authors: Zheng Yang Chin, Kai Keng Ang, Cuntai Guan, Chuanchu Wang, Haihong ZhangAbstract:This paper investigates the classification of multi-class motor imagery for electroencephalogram (EEG)-based Brain-Computer Interface (BCI) using the Filter Bank Common Spatial Pattern (FBCSP) algorithm. The FBCSP algorithm classifies EEG measurements from features constructed using subject-specific temporal-Spatial filters. However, the FBCSP algorithm is limited to binary-class motor imagery. Hence, this paper proposes 3 approaches of multi-class extension to the FBCSP algorithm: One-versus-Rest, Pair-Wise and Divide-and-Conquer. These approaches decompose the multi-class problem into several binary-class problems. The study is conducted on the BCI Competition IV dataset IIa, which comprises single-trial EEG data from 9 subjects performing 4-class motor imagery of left-hand, right-hand, foot and tongue actions. The results showed that the multi-class FBCSP algorithm could extract features that matched neurophysiological knowledge, and yielded the best performance on the evaluation data compared to other international submissions.
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robust filter bank Common Spatial Pattern rfbcsp in motor imagery based brain computer interface
International Conference of the IEEE Engineering in Medicine and Biology Society, 2009Co-Authors: Kai Keng Ang, Haihong Zhang, Zheng Yang Chin, Cuntai GuanAbstract:The Filter Bank Common Spatial Pattern (FBCSP) algorithm performs autonomous selection of key temporal-Spatial discriminative EEG characteristics in motor imagery-based Brain Computer Interfaces (MI-BCI). However, FBCSP is sensitive to outliers because it involves multiple estimations of covariance matrices from EEG measurements. This paper proposes a Robust FBCSP (RFBCSP) algorithm whereby the estimates of the covariance matrices are replaced with the robust Minimum Covariance Determinant (MCD) estimator. The performance of RFBCSP is investigated on a publicly available dataset and compared against FBCSP using 10×10-fold cross-validation accuracies on training data, and session-to-session transfer kappa values on independent test data. The results showed that RFBCSP yielded improvements in certain subjects and slight improvement in overall performance across subjects. Analysis on one subject who improved suggested that outliers were excluded from the robust covariance matrices estimation. These results revealed a promising direction of RFBCSP for robust classifications of EEG measurements in MI-BCI.
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filter bank Common Spatial Pattern fbcsp in brain computer interface
International Joint Conference on Neural Network, 2008Co-Authors: Kai Keng Ang, Zhang Yang Chin, Haihong Zhang, Cuntai GuanAbstract:In motor imagery-based brain computer interfaces (BCI), discriminative Patterns can be extracted from the electroencephalogram (EEG) using the Common Spatial Pattern (CSP) algorithm. However, the performance of this Spatial filter depends on the operational frequency band of the EEG. Thus, setting a broad frequency range, or manually selecting a subject-specific frequency range, are Commonly used with the CSP algorithm. To address this problem, this paper proposes a novel filter bank Common Spatial Pattern (FBCSP) to perform autonomous selection of key temporal-Spatial discriminative EEG characteristics. After the EEG measurements have been bandpass-filtered into multiple frequency bands, CSP features are extracted from each of these bands. A feature selection algorithm is then used to automatically select discriminative pairs of frequency bands and corresponding CSP features. A classification algorithm is subsequently used to classify the CSP features. A study is conducted to assess the performance of a selection of feature selection and classification algorithms for use with the FBCSP. Extensive experimental results are presented on a publicly available dataset as well as data collected from healthy subjects and unilaterally paralyzed stroke patients. The results show that FBCSP, using a particular combination feature selection and classification algorithm, yields relatively higher cross-validation accuracies compared to prevailing approaches.
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IJCNN - Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008Co-Authors: Kai Keng Ang, Zhang Yang Chin, Haihong Zhang, Cuntai GuanAbstract:In motor imagery-based brain computer interfaces (BCI), discriminative Patterns can be extracted from the electroencephalogram (EEG) using the Common Spatial Pattern (CSP) algorithm. However, the performance of this Spatial filter depends on the operational frequency band of the EEG. Thus, setting a broad frequency range, or manually selecting a subject-specific frequency range, are Commonly used with the CSP algorithm. To address this problem, this paper proposes a novel filter bank Common Spatial Pattern (FBCSP) to perform autonomous selection of key temporal-Spatial discriminative EEG characteristics. After the EEG measurements have been bandpass-filtered into multiple frequency bands, CSP features are extracted from each of these bands. A feature selection algorithm is then used to automatically select discriminative pairs of frequency bands and corresponding CSP features. A classification algorithm is subsequently used to classify the CSP features. A study is conducted to assess the performance of a selection of feature selection and classification algorithms for use with the FBCSP. Extensive experimental results are presented on a publicly available dataset as well as data collected from healthy subjects and unilaterally paralyzed stroke patients. The results show that FBCSP, using a particular combination feature selection and classification algorithm, yields relatively higher cross-validation accuracies compared to prevailing approaches.
A. P. Vinod - One of the best experts on this subject based on the ideXlab platform.
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a modified wavelet Common Spatial Pattern method for decoding hand movement directions in brain computer interfaces
International Joint Conference on Neural Network, 2012Co-Authors: Neethu Robinso, A. P. Vinod, Kai Keng Ang, Cuntai Gua, Tee Keng PengAbstract:The decoding of hand movement kinematics using non-invasive data acquisition techniques is a recent area of research in Brain Computer Interface (BCI). In this work, we use an Electroencephalography (EEG) based BCI to decode directional information from the brain data collected during an actual hand movement experiment. The objective is to find the discriminative features of movement related potential that can classify any two directions out of the four orthogonal directions in which subject performs right hand movement. The performance using Wavelet-Common Spatial Pattern (W-CSP) algorithm and its variations in terms of Spatial regularization is studied and compared. The work further analyzes the involvement of frontal, parietal and motor regions in carrying movement kinematics information with the help of Spatial plots given by CSP. The performance variability for different directions in various subjects is another important observation in our results. The work aims to provide a more refined movement control command set for BCIs by developing efficient techniques to decode the direction of movement.
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a new discriminative Common Spatial Pattern method for motor imagery brain computer interfaces
IEEE Transactions on Biomedical Engineering, 2009Co-Authors: Kavitha P Thomas, Cuntai Guan, A. P. VinodAbstract:Event-related desynchronization/synchronization Patterns during right/left motor imagery (MI) are effective features for an electroencephalogram-based brain-computer interface (BCI). As MI tasks are subject-specific, selection of subject-specific discriminative frequency components play a vital role in distinguishing these Patterns. This paper proposes a new discriminative filter bank (FB) Common Spatial Pattern algorithm to extract subject-specific FB for MI classification. The proposed method enhances the classification accuracy in BCI competition III dataset IVa and competition IV dataset IIb. Compared to the performance offered by the existing FB-based method, the proposed algorithm offers error rate reductions of 17.42% and 8.9% for BCI competition datasets III and IV, respectively.
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a new discriminative Common Spatial Pattern method for motor imagery brain computer interfaces
IEEE Transactions on Biomedical Engineering, 2009Co-Authors: Kavitha P Thomas, A. P. Vinod, Cuntai Guan, Chiew Tong Lau, Kai Keng AngAbstract:Event-related desynchronization/synchronization Patterns during right/left motor imagery (MI) are effective features for an electroencephalogram-based brain-computer interface (BCI). As MI tasks are subject-specific, selection of subject-specific discriminative frequency components play a vital role in distinguishing these Patterns. This paper proposes a new discriminative filter bank (FB) Common Spatial Pattern algorithm to extract subject-specific FB for MI classification. The proposed method enhances the classification accuracy in BCI competition III dataset IVa and competition IV dataset IIb. Compared to the performance offered by the existing FB-based method, the proposed algorithm offers error rate reductions of 17.42% and 8.9% for BCI competition datasets III and IV, respectively.
Tee Keng Peng - One of the best experts on this subject based on the ideXlab platform.
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a modified wavelet Common Spatial Pattern method for decoding hand movement directions in brain computer interfaces
International Joint Conference on Neural Network, 2012Co-Authors: Neethu Robinso, A. P. Vinod, Kai Keng Ang, Cuntai Gua, Tee Keng PengAbstract:The decoding of hand movement kinematics using non-invasive data acquisition techniques is a recent area of research in Brain Computer Interface (BCI). In this work, we use an Electroencephalography (EEG) based BCI to decode directional information from the brain data collected during an actual hand movement experiment. The objective is to find the discriminative features of movement related potential that can classify any two directions out of the four orthogonal directions in which subject performs right hand movement. The performance using Wavelet-Common Spatial Pattern (W-CSP) algorithm and its variations in terms of Spatial regularization is studied and compared. The work further analyzes the involvement of frontal, parietal and motor regions in carrying movement kinematics information with the help of Spatial plots given by CSP. The performance variability for different directions in various subjects is another important observation in our results. The work aims to provide a more refined movement control command set for BCIs by developing efficient techniques to decode the direction of movement.