TY - GEN
T1 - Single-trial EEG discrimination between wrist and finger movement imagery and execution in a sensorimotor BCI
AU - Mohamed, A. K.
AU - Marwala, T.
AU - John, L. R.
PY - 2011
Y1 - 2011
N2 - Brain-computer interface (BCI) may be used to control a prosthetic or orthotic hand using neural activity from the brain. The core of this sensorimotor BCI lies in the interpretation of the neural information extracted from electroencephalogram (EEG). It is desired to improve on the interpretation of EEG to allow people with neuromuscular disorders to perform daily activities. This paper investigates the possibility of discriminating between the EEG associated with wrist and finger movements. The EEG was recorded from test subjects as they executed and imagined five essential hand movements using both hands. Independent component analysis (ICA) and time-frequency techniques were used to extract spectral features based on event-related (de)synchronisation (ERD/ERS), while the Bhattacharyya distance (BD) was used for feature reduction. Mahalanobis distance (MD) clustering and artificial neural networks (ANN) were used as classifiers and obtained average accuracies of 65 % and 71 % respectively. This shows that EEG discrimination between wrist and finger movements is possible. The research introduces a new combination of motor tasks to BCI research.
AB - Brain-computer interface (BCI) may be used to control a prosthetic or orthotic hand using neural activity from the brain. The core of this sensorimotor BCI lies in the interpretation of the neural information extracted from electroencephalogram (EEG). It is desired to improve on the interpretation of EEG to allow people with neuromuscular disorders to perform daily activities. This paper investigates the possibility of discriminating between the EEG associated with wrist and finger movements. The EEG was recorded from test subjects as they executed and imagined five essential hand movements using both hands. Independent component analysis (ICA) and time-frequency techniques were used to extract spectral features based on event-related (de)synchronisation (ERD/ERS), while the Bhattacharyya distance (BD) was used for feature reduction. Mahalanobis distance (MD) clustering and artificial neural networks (ANN) were used as classifiers and obtained average accuracies of 65 % and 71 % respectively. This shows that EEG discrimination between wrist and finger movements is possible. The research introduces a new combination of motor tasks to BCI research.
KW - Brain-computer Interface (BCI)
KW - electroencephalogram (EEG)
KW - event-related (de)synchronisation (ERD/ERS)
KW - independent component analysis (ICA)
UR - http://www.scopus.com/inward/record.url?scp=84055183363&partnerID=8YFLogxK
U2 - 10.1109/IEMBS.2011.6091552
DO - 10.1109/IEMBS.2011.6091552
M3 - Conference contribution
C2 - 22255776
AN - SCOPUS:84055183363
SN - 9781424441211
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 6289
EP - 6293
BT - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
T2 - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Y2 - 30 August 2011 through 3 September 2011
ER -