TY - GEN
T1 - Signal classification algorithm in motor imagery based on asynchronous brain-computer interface
AU - Jiang, Yu
AU - He, Jingyan
AU - Li, Dandan
AU - Jin, Jing
AU - Shen, Yi
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - In asynchronous Brain-Computer Interface (BCI), the subjects imagine autonomously to control devices via the system analyzing the electroencephalogram (EEG) recorded from the scalp. The continuous EEG of motor imagery (MI) based asynchronous BCI is processed, associated spatial filter is constructed to extract the EEG features. The classification result at each time is combined with the results of neighboring period, preventing the instabilities of the final classification resulting from short durations of the imagery tasks. When extended to the condition of two kinds of events for purpose of practical use, it reaches that in asynchronous BCI, detection sensitivity of left-right hand MI is above 90%, classification accuracy is above 83%, and false positive rate is below 32%; detection sensitivity of hand-foot MI is above 87%, classification accuracy is above 91%, and false positive rate is below 44%, which can guide the practical use in asynchronous BCI classification.
AB - In asynchronous Brain-Computer Interface (BCI), the subjects imagine autonomously to control devices via the system analyzing the electroencephalogram (EEG) recorded from the scalp. The continuous EEG of motor imagery (MI) based asynchronous BCI is processed, associated spatial filter is constructed to extract the EEG features. The classification result at each time is combined with the results of neighboring period, preventing the instabilities of the final classification resulting from short durations of the imagery tasks. When extended to the condition of two kinds of events for purpose of practical use, it reaches that in asynchronous BCI, detection sensitivity of left-right hand MI is above 90%, classification accuracy is above 83%, and false positive rate is below 32%; detection sensitivity of hand-foot MI is above 87%, classification accuracy is above 91%, and false positive rate is below 44%, which can guide the practical use in asynchronous BCI classification.
KW - Associated common spatial patterns
KW - Asynchronous brain-computer interface
KW - Motor imagery
KW - Roc curve
UR - https://www.scopus.com/pages/publications/85072819631
U2 - 10.1109/I2MTC.2019.8826883
DO - 10.1109/I2MTC.2019.8826883
M3 - 会议稿件
AN - SCOPUS:85072819631
T3 - I2MTC 2019 - 2019 IEEE International Instrumentation and Measurement Technology Conference, Proceedings
BT - I2MTC 2019 - 2019 IEEE International Instrumentation and Measurement Technology Conference, Proceedings
T2 - 2019 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2019
Y2 - 20 May 2019 through 23 May 2019
ER -