TY - GEN
T1 - Hybrid EEG-EOG brain-computer interface system for practical machine control
AU - Punsawad, Yunyong
AU - Wongsawat, Yodchanan
AU - Parnichkun, Manukid
PY - 2010
Y1 - 2010
N2 - Practical issues such as accuracy with various subjects, number of sensors, and time for training are important problems of existing brain-computer interface (BCI) systems. In this paper, we propose a hybrid framework for the BCI system that can make machine control more practical. The electrooculogram (EOG) is employed to control the machine in the left and right directions while the electroencephalogram (EEG) is employed to control the forword, no action, and complete stop motions of the machine. By using only 2-channel biosignals, the average classification accuracy of more than 95% can be achieved.
AB - Practical issues such as accuracy with various subjects, number of sensors, and time for training are important problems of existing brain-computer interface (BCI) systems. In this paper, we propose a hybrid framework for the BCI system that can make machine control more practical. The electrooculogram (EOG) is employed to control the machine in the left and right directions while the electroencephalogram (EEG) is employed to control the forword, no action, and complete stop motions of the machine. By using only 2-channel biosignals, the average classification accuracy of more than 95% can be achieved.
UR - https://www.scopus.com/pages/publications/78650835053
U2 - 10.1109/IEMBS.2010.5626745
DO - 10.1109/IEMBS.2010.5626745
M3 - Conference contribution
C2 - 21096331
AN - SCOPUS:78650835053
SN - 9781424441235
T3 - 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
SP - 1360
EP - 1363
BT - 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
T2 - 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Y2 - 31 August 2010 through 4 September 2010
ER -