TY - JOUR
T1 - EEG Based Foot Movement Onset Detection with the Probabilistic Classification Vector Machine
AU - Mohammadi, Raheleh
AU - Mahloojifar, Ali
AU - Chen, Huanhuan
AU - Coyle, DH
PY - 2012
Y1 - 2012
N2 - A critical issue in designing a self-paced brain computer interface (BCI) system is onset detection of the mental task from the continuous electroencephalogram (EEG) signal to produce a brain switch. This work shows significant improvement in a movement based self-paced BCI by applying a new sparse learning classification algorithm, probabilistic classification vector machines (PCVMs) to classify EEG signal. Constant-Q filters instead of constant bandwidth filters for frequency decomposition are also shown to enhance the discrimination of movement related patterns from EEG patterns associated with idle state. Analysis of the data recorded from seven subjects executing foot movement using the constant-Q filters and PCVMs shows a statistically significant 17% (p
AB - A critical issue in designing a self-paced brain computer interface (BCI) system is onset detection of the mental task from the continuous electroencephalogram (EEG) signal to produce a brain switch. This work shows significant improvement in a movement based self-paced BCI by applying a new sparse learning classification algorithm, probabilistic classification vector machines (PCVMs) to classify EEG signal. Constant-Q filters instead of constant bandwidth filters for frequency decomposition are also shown to enhance the discrimination of movement related patterns from EEG patterns associated with idle state. Analysis of the data recorded from seven subjects executing foot movement using the constant-Q filters and PCVMs shows a statistically significant 17% (p
UR - http://isrc.ulster.ac.uk/Staff/DCoyle/Publications.html
U2 - 10.1007/978-3-642-34478-7_44
DO - 10.1007/978-3-642-34478-7_44
M3 - Article
SN - 1611-3349
VL - 7666
SP - 356
EP - 363
JO - Lecture Notes in Artificial Intelligence
JF - Lecture Notes in Artificial Intelligence
ER -