TY - GEN
T1 - Decoding of wrist and finger movement from electroencephalography signal
AU - Pal, Monalisa
AU - Bhattacharyya, Saugat
AU - Konar, Amit
AU - Tibarewala, D. N.
AU - Janarthanan, R.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - The emergence of brain-computer interfacing has made the control of robots through thought a reality. Such real-time application calls for fast processing and accurate classification of brain signals. In this paper, we address the two-level classification of motor imagery signals, where the user differentiates between clockwise/ counter-clockwise movement of wrist and the opening/closing of the fingers. For this purpose, parameters of adaptive autoregressive (AAR) models and Extreme Energy Ratio criterion (EER) are employed as features, which are fed to standard classifiers for comparison. It concludes the features extracted based on EER, selected by sequential forward search and classified using radial basis function kernelized support vector machine, provides optimum performance of the classification process for implementation in real-time scenario, with an average accuracy 90.24% and a time complexity of 8.2449 seconds.
AB - The emergence of brain-computer interfacing has made the control of robots through thought a reality. Such real-time application calls for fast processing and accurate classification of brain signals. In this paper, we address the two-level classification of motor imagery signals, where the user differentiates between clockwise/ counter-clockwise movement of wrist and the opening/closing of the fingers. For this purpose, parameters of adaptive autoregressive (AAR) models and Extreme Energy Ratio criterion (EER) are employed as features, which are fed to standard classifiers for comparison. It concludes the features extracted based on EER, selected by sequential forward search and classified using radial basis function kernelized support vector machine, provides optimum performance of the classification process for implementation in real-time scenario, with an average accuracy 90.24% and a time complexity of 8.2449 seconds.
KW - Adaptive Auto-Regressive Model
KW - Brain-Computer Interfacing
KW - Distance Likelihood Ratio Test
KW - Electroencephalography
KW - Extreme Energy Ratio
KW - Fisher Linear Discriminant
KW - Naïve Bayes
KW - Radial Basis Function based Support Vector Machine
KW - Sequential Forward Feature Selection
UR - http://www.scopus.com/inward/record.url?scp=84900606264&partnerID=8YFLogxK
U2 - 10.1109/CONECCT.2014.6740323
DO - 10.1109/CONECCT.2014.6740323
M3 - Conference contribution
AN - SCOPUS:84900606264
SN - 9781479923175
T3 - IEEE CONECCT 2014 - 2014 IEEE International Conference on Electronics, Computing and Communication Technologies
BT - IEEE CONECCT 2014 - 2014 IEEE International Conference on Electronics, Computing and Communication Technologies
PB - IEEE Computer Society
T2 - 2014 IEEE International Conference on Electronics, Computing and Communication Technologies, IEEE CONECCT 2014
Y2 - 6 January 2014 through 7 January 2014
ER -