TY - GEN
T1 - Detection of fast and slow hand movements from motor imagery EEG signals
AU - Bhattacharyya, Saugat
AU - Hossain, Munshi Asif
AU - Konar, Amit
AU - Tibarewala, D. N.
AU - Ramadoss, Janarthanan
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Classification of Electroencephalography (EEG) signal is an open area of re-search in Brain-computer interfacing (BCI). The classifiers detect the different mental states generated by a subject to control an external prosthesis. In this study, we aim to differentiate fast and slow execution of left or right hand move-ment using EEG signals. To detect the different mental states pertaining to motor movements, we aim to identify the event related desynchronization/synchronization (ERD/ERS) waveform from the incoming EEG signals. For this purpose, we have used Welch based power spectral density estimates to create the feature vector and tested it on multiple support vector machines, Nave Bayesian, Linear Discriminant Analysis and k-Nearest Neighbor classifiers. The classification accuracies produced by each of the classifiers are more than 75% with naïve Bayesian yielding the best result of 97.1%.
AB - Classification of Electroencephalography (EEG) signal is an open area of re-search in Brain-computer interfacing (BCI). The classifiers detect the different mental states generated by a subject to control an external prosthesis. In this study, we aim to differentiate fast and slow execution of left or right hand move-ment using EEG signals. To detect the different mental states pertaining to motor movements, we aim to identify the event related desynchronization/synchronization (ERD/ERS) waveform from the incoming EEG signals. For this purpose, we have used Welch based power spectral density estimates to create the feature vector and tested it on multiple support vector machines, Nave Bayesian, Linear Discriminant Analysis and k-Nearest Neighbor classifiers. The classification accuracies produced by each of the classifiers are more than 75% with naïve Bayesian yielding the best result of 97.1%.
KW - Brain-computer interfacing
KW - Electroencephalography
KW - Event related desynchronization/synchronization
KW - Motor imagery
KW - Pattern classifiers
UR - http://www.scopus.com/inward/record.url?scp=84906704657&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-07353-8_74
DO - 10.1007/978-3-319-07353-8_74
M3 - Conference contribution
AN - SCOPUS:84906704657
SN - 9783319073521
T3 - Smart Innovation, Systems and Technologies
SP - 645
EP - 652
BT - Advanced Computing, Networking and Informatics - Proceedings of the Second International Conference on Advanced Computing, Networking and Informatics, ICACNI 2014
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Advanced Computing, Networking and Informatics, ICACNI 2014
Y2 - 24 June 2014 through 26 June 2014
ER -