The Experts below are selected from a list of 112212 Experts worldwide ranked by ideXlab platform
A. P. Vinod - One of the best experts on this subject based on the ideXlab platform.
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NER - Decoding speed of Hand Movement execution using temporal features of EEG
2017 8th International IEEE EMBS Conference on Neural Engineering (NER), 2017Co-Authors: Neethu Robinson, A. P. VinodAbstract:Electroencephalography (EEG) processing methods mostly focus on extracting its spectral or spatial features, which are proven to discriminate bilateral Hand Movement, Hand Movement directions and speed. The focus of current study is to explore EEG time-domain features that represent neural correlates of Hand Movement execution speed. In this paper, we propose autocorrelation analysis of EEG and features derived from it that utilizes difference in execution time of fast v/s slow tasks. The variation in decay constant of autocorrelation of EEG over execution time is studied, and its application as a potential feature to discriminate Movement speed is explored. The proposed analysis method has been validated on EEG data recorded from 7 subjects performing right Hand Movement at two different speeds. An average classification accuracy of 75.71% and 85.16% is obtained, using features derived from significant time segments in the data.
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SMC - Hand Movement Trajectory Reconstruction from EEG for Brain-Computer Interface Systems
2013 IEEE International Conference on Systems Man and Cybernetics, 2013Co-Authors: Neethu Robinson, A. P. Vinod, Cuntai GuanAbstract:Decoding Hand Movement parameters (for example Movement trajectory, speed etc.) from scalp recordings such as Electroencephalography (EEG) is a challenging and less explored area of research in the field of Brain Computer Interface (BCI) systems. By identifying neural features underlying Movement parameters, a detailed and well defined control command set can be provided to the BCI output device. A continuous control to the output device is better suited for practical BCI systems, and can be achieved by continuous reconstruction of Movement trajectory than discrete brain activity classifications. In this study, we attempt to reconstruct/estimate various parameters of Hand Movement trajectory from multi channel EEG recordings. The data for analysis is collected by performing an experiment that involved centre-out right Hand Movement tasks in four different directions at two different speeds in random order. Multiple linear regression (MLR) strategy that fits the recorded Movement parameters to a set of spatial, spectral and temporal localized neural data set is adopted. We propose a method to define the predictor set for MLR, using wavelet analysis, to decompose the signal into various sub bands. The correlation between recorded and estimated parameters are calculated and an average correlation coefficient of (0.56 ± 0.16) is obtained over estimating six Movement parameters. The promising results achieved using the proposed algorithm, which are better than that of the existing algorithms, indicate the applicability of EEG for continuous motor control.
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multi class eeg classification of voluntary Hand Movement directions
Journal of Neural Engineering, 2013Co-Authors: Neethu Robinson, A. P. Vinod, Kai Keng Ang, Cuntai Guan, Keng Peng TeeAbstract:Objective. Studies have shown that low frequency components of brain recordings provide information on voluntary Hand Movement directions. However, non-invasive techniques face more challenges compared to invasive techniques. Approach. This study presents a novel signal processing technique to extract features from non-invasive electroencephalography (EEG) recordings for classifying voluntary Hand Movement directions. The proposed technique comprises the regularized wavelet-common spatial pattern algorithm to extract the features, mutual information-based feature selection, and multi-class classification using the Fisher linear discriminant. EEG data from seven healthy human subjects were collected while they performed voluntary right Hand center-out Movement in four orthogonal directions. In this study, the Movement direction dependent signal-to-noise ratio is used as a parameter to denote the effectiveness of each temporal frequency bin in the classification of Movement directions. Main results. Significant (p < 0.005) Movement direction dependent modulation in the EEG data was identified largely towards the end of Movement at low frequencies (6 Hz) from the midline parietal and contralateral motor areas. Experimental results on single trial classification of the EEG data collected yielded an average accuracy of (80.24 ± 9.41)% in discriminating the four different directions using the proposed technique on features extracted from low frequency components. Significance. The proposed feature extraction strategy provides very high multi-class classification accuracies, and the results are proven to be more statistically significant than existing methods. The results obtained suggest the possibility of multi-directional Movement classification from single-trial EEG recordings using the proposed technique in low frequency components. (Some figures may appear in colour only in the online journal)
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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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IJCNN - A modified Wavelet-Common Spatial Pattern method for decoding Hand Movement directions in brain computer interfaces
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012Co-Authors: Neethu Robinson, A. P. Vinod, Kai Keng Ang, Cuntai Guan, 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.
Kai Keng Ang - One of the best experts on this subject based on the ideXlab platform.
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multi class eeg classification of voluntary Hand Movement directions
Journal of Neural Engineering, 2013Co-Authors: Neethu Robinson, A. P. Vinod, Kai Keng Ang, Cuntai Guan, Keng Peng TeeAbstract:Objective. Studies have shown that low frequency components of brain recordings provide information on voluntary Hand Movement directions. However, non-invasive techniques face more challenges compared to invasive techniques. Approach. This study presents a novel signal processing technique to extract features from non-invasive electroencephalography (EEG) recordings for classifying voluntary Hand Movement directions. The proposed technique comprises the regularized wavelet-common spatial pattern algorithm to extract the features, mutual information-based feature selection, and multi-class classification using the Fisher linear discriminant. EEG data from seven healthy human subjects were collected while they performed voluntary right Hand center-out Movement in four orthogonal directions. In this study, the Movement direction dependent signal-to-noise ratio is used as a parameter to denote the effectiveness of each temporal frequency bin in the classification of Movement directions. Main results. Significant (p < 0.005) Movement direction dependent modulation in the EEG data was identified largely towards the end of Movement at low frequencies (6 Hz) from the midline parietal and contralateral motor areas. Experimental results on single trial classification of the EEG data collected yielded an average accuracy of (80.24 ± 9.41)% in discriminating the four different directions using the proposed technique on features extracted from low frequency components. Significance. The proposed feature extraction strategy provides very high multi-class classification accuracies, and the results are proven to be more statistically significant than existing methods. The results obtained suggest the possibility of multi-directional Movement classification from single-trial EEG recordings using the proposed technique in low frequency components. (Some figures may appear in colour only in the online journal)
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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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IJCNN - A modified Wavelet-Common Spatial Pattern method for decoding Hand Movement directions in brain computer interfaces
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012Co-Authors: Neethu Robinson, A. P. Vinod, Kai Keng Ang, Cuntai Guan, 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.
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.
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IJCNN - A modified Wavelet-Common Spatial Pattern method for decoding Hand Movement directions in brain computer interfaces
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012Co-Authors: Neethu Robinson, A. P. Vinod, Kai Keng Ang, Cuntai Guan, 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.
Neethu Robinson - One of the best experts on this subject based on the ideXlab platform.
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NER - Decoding speed of Hand Movement execution using temporal features of EEG
2017 8th International IEEE EMBS Conference on Neural Engineering (NER), 2017Co-Authors: Neethu Robinson, A. P. VinodAbstract:Electroencephalography (EEG) processing methods mostly focus on extracting its spectral or spatial features, which are proven to discriminate bilateral Hand Movement, Hand Movement directions and speed. The focus of current study is to explore EEG time-domain features that represent neural correlates of Hand Movement execution speed. In this paper, we propose autocorrelation analysis of EEG and features derived from it that utilizes difference in execution time of fast v/s slow tasks. The variation in decay constant of autocorrelation of EEG over execution time is studied, and its application as a potential feature to discriminate Movement speed is explored. The proposed analysis method has been validated on EEG data recorded from 7 subjects performing right Hand Movement at two different speeds. An average classification accuracy of 75.71% and 85.16% is obtained, using features derived from significant time segments in the data.
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SMC - Hand Movement Trajectory Reconstruction from EEG for Brain-Computer Interface Systems
2013 IEEE International Conference on Systems Man and Cybernetics, 2013Co-Authors: Neethu Robinson, A. P. Vinod, Cuntai GuanAbstract:Decoding Hand Movement parameters (for example Movement trajectory, speed etc.) from scalp recordings such as Electroencephalography (EEG) is a challenging and less explored area of research in the field of Brain Computer Interface (BCI) systems. By identifying neural features underlying Movement parameters, a detailed and well defined control command set can be provided to the BCI output device. A continuous control to the output device is better suited for practical BCI systems, and can be achieved by continuous reconstruction of Movement trajectory than discrete brain activity classifications. In this study, we attempt to reconstruct/estimate various parameters of Hand Movement trajectory from multi channel EEG recordings. The data for analysis is collected by performing an experiment that involved centre-out right Hand Movement tasks in four different directions at two different speeds in random order. Multiple linear regression (MLR) strategy that fits the recorded Movement parameters to a set of spatial, spectral and temporal localized neural data set is adopted. We propose a method to define the predictor set for MLR, using wavelet analysis, to decompose the signal into various sub bands. The correlation between recorded and estimated parameters are calculated and an average correlation coefficient of (0.56 ± 0.16) is obtained over estimating six Movement parameters. The promising results achieved using the proposed algorithm, which are better than that of the existing algorithms, indicate the applicability of EEG for continuous motor control.
-
multi class eeg classification of voluntary Hand Movement directions
Journal of Neural Engineering, 2013Co-Authors: Neethu Robinson, A. P. Vinod, Kai Keng Ang, Cuntai Guan, Keng Peng TeeAbstract:Objective. Studies have shown that low frequency components of brain recordings provide information on voluntary Hand Movement directions. However, non-invasive techniques face more challenges compared to invasive techniques. Approach. This study presents a novel signal processing technique to extract features from non-invasive electroencephalography (EEG) recordings for classifying voluntary Hand Movement directions. The proposed technique comprises the regularized wavelet-common spatial pattern algorithm to extract the features, mutual information-based feature selection, and multi-class classification using the Fisher linear discriminant. EEG data from seven healthy human subjects were collected while they performed voluntary right Hand center-out Movement in four orthogonal directions. In this study, the Movement direction dependent signal-to-noise ratio is used as a parameter to denote the effectiveness of each temporal frequency bin in the classification of Movement directions. Main results. Significant (p < 0.005) Movement direction dependent modulation in the EEG data was identified largely towards the end of Movement at low frequencies (6 Hz) from the midline parietal and contralateral motor areas. Experimental results on single trial classification of the EEG data collected yielded an average accuracy of (80.24 ± 9.41)% in discriminating the four different directions using the proposed technique on features extracted from low frequency components. Significance. The proposed feature extraction strategy provides very high multi-class classification accuracies, and the results are proven to be more statistically significant than existing methods. The results obtained suggest the possibility of multi-directional Movement classification from single-trial EEG recordings using the proposed technique in low frequency components. (Some figures may appear in colour only in the online journal)
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IJCNN - A modified Wavelet-Common Spatial Pattern method for decoding Hand Movement directions in brain computer interfaces
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012Co-Authors: Neethu Robinson, A. P. Vinod, Kai Keng Ang, Cuntai Guan, 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.
Keng Peng Tee - One of the best experts on this subject based on the ideXlab platform.
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multi class eeg classification of voluntary Hand Movement directions
Journal of Neural Engineering, 2013Co-Authors: Neethu Robinson, A. P. Vinod, Kai Keng Ang, Cuntai Guan, Keng Peng TeeAbstract:Objective. Studies have shown that low frequency components of brain recordings provide information on voluntary Hand Movement directions. However, non-invasive techniques face more challenges compared to invasive techniques. Approach. This study presents a novel signal processing technique to extract features from non-invasive electroencephalography (EEG) recordings for classifying voluntary Hand Movement directions. The proposed technique comprises the regularized wavelet-common spatial pattern algorithm to extract the features, mutual information-based feature selection, and multi-class classification using the Fisher linear discriminant. EEG data from seven healthy human subjects were collected while they performed voluntary right Hand center-out Movement in four orthogonal directions. In this study, the Movement direction dependent signal-to-noise ratio is used as a parameter to denote the effectiveness of each temporal frequency bin in the classification of Movement directions. Main results. Significant (p < 0.005) Movement direction dependent modulation in the EEG data was identified largely towards the end of Movement at low frequencies (6 Hz) from the midline parietal and contralateral motor areas. Experimental results on single trial classification of the EEG data collected yielded an average accuracy of (80.24 ± 9.41)% in discriminating the four different directions using the proposed technique on features extracted from low frequency components. Significance. The proposed feature extraction strategy provides very high multi-class classification accuracies, and the results are proven to be more statistically significant than existing methods. The results obtained suggest the possibility of multi-directional Movement classification from single-trial EEG recordings using the proposed technique in low frequency components. (Some figures may appear in colour only in the online journal)
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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%.
