Sub-band-power-based efficient Brain Computer Interface for wheelchair control

An efficient Brain Computer Interface (BCI) is designed and implemented to allow disabled people to control the motion of wheelchairs. It uses a compact portable EEG sensor to capture 14 brain signals and wirelessly feed them to the PC. Four classes of motions are used: Forward, Backward, Left, and Right. The signals are obtained in a free-style manner without compelling users to perform pre-defined mental operations. This led to variations in the results that shed some light on the cognitive aspect of the problem. Principal Component Analysis (PCA) and Sub-Band Powers obtained from the Wavelet Transform are used to reduce the signal dimensionality from nearly 14000 to only 3. A Feed-Forward Neural Network with Back Propagation is used as a classifier. The average classification rate is 91 % on the overall and as high as 97.5 % for some users. The effect of mother wavelet type and user dependence are also investigated.