TY - GEN
T1 - A bacterial foraging optimization and learning automata based feature selection for motor imagery EEG classification
AU - Pal, Monalisa
AU - Bhattacharyya, Saugat
AU - Roy, Shounak
AU - Konar, Amit
AU - Tibarewala, D. N.
AU - Janarthanan, R.
PY - 2014/12/12
Y1 - 2014/12/12
N2 - Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.
AB - Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.
KW - Bacterial Foraging Optimization Algorithm
KW - Brain-Computer Interfacing
KW - Discrete Wavelet Transform
KW - Distance Likelihood Ratio Test
KW - Learning Automata
UR - http://www.scopus.com/inward/record.url?scp=84920747210&partnerID=8YFLogxK
U2 - 10.1109/SPCOM.2014.6983926
DO - 10.1109/SPCOM.2014.6983926
M3 - Conference contribution
AN - SCOPUS:84920747210
T3 - 2014 International Conference on Signal Processing and Communications, SPCOM 2014
BT - 2014 International Conference on Signal Processing and Communications, SPCOM 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Signal Processing and Communications, SPCOM 2014
Y2 - 22 July 2014 through 25 July 2014
ER -