TY - GEN
T1 - EEG-based mental task classification
T2 - 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
AU - Akrami, Athena
AU - Solhjoo, Soroosh
AU - Motie-Nasrabadi, Ali
AU - Hashemi-Golpayegani, Mohammad Reza
PY - 2005
Y1 - 2005
N2 - Use of EEG signals as a channel of communication between men and machines represents one of the current challenges in signal theory research. The principal element of such a communication system, known as a "Brain-Computer Interface," is the interpretation of the EEG signals related to the characteristic parameters of brain electrical activity. Our goal in this work was extracting quantitative changes in the EEG due to movement imagination. Subject's EEG was recorded while he performed left or right hand movement imagination. Different feature sets extracted from EEG were used as inputs into linear, Neural Network and HMM classifiers for the purpose of imagery movement mental task classification. The results indicate that applying linear classifier to 5 frequency features of asymmetry signal produced from channel C3 and C4 can provide a very high classification accuracy percentage as a simple classifier with small number of features comparing to other feature sets.
AB - Use of EEG signals as a channel of communication between men and machines represents one of the current challenges in signal theory research. The principal element of such a communication system, known as a "Brain-Computer Interface," is the interpretation of the EEG signals related to the characteristic parameters of brain electrical activity. Our goal in this work was extracting quantitative changes in the EEG due to movement imagination. Subject's EEG was recorded while he performed left or right hand movement imagination. Different feature sets extracted from EEG were used as inputs into linear, Neural Network and HMM classifiers for the purpose of imagery movement mental task classification. The results indicate that applying linear classifier to 5 frequency features of asymmetry signal produced from channel C3 and C4 can provide a very high classification accuracy percentage as a simple classifier with small number of features comparing to other feature sets.
UR - http://www.scopus.com/inward/record.url?scp=33846926332&partnerID=8YFLogxK
U2 - 10.1109/iembs.2005.1615501
DO - 10.1109/iembs.2005.1615501
M3 - Conference contribution
AN - SCOPUS:33846926332
SN - 0780387406
SN - 9780780387409
T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
SP - 4626
EP - 4629
BT - Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 September 2005 through 4 September 2005
ER -