  This work proposes a hybrid Brain Computer Interface system for the
automation of a wheelchair for the disabled. Herein a working prototype of a
BCI-based wheelchair is detailed that can navigate inside a typical home
environment with minimum structural modification and without any visual
obstruction and discomfort to the user. The prototype is based on a combined
mechanism of steady-state visually evoked potential and eye blinks. To elicit
SSVEP, LEDs flickering at 13Hz and 15Hz were used to select the left and right
direction, respectively, and EEG data was recorded. In addition, the occurrence
of three continuous blinks was used as an indicator for stopping an ongoing
action. The wavelet packet denoising method was applied, followed by feature
extraction methods such as Wavelet Packet Decomposition and Canonical
Correlation Analysis over narrowband reconstructed EEG signals. Bayesian
optimization was used to obtain 5 fold cross-validations to optimize the
hyperparameters of the Support Vector Machine. The resulting new model was
tested and the average cross-validation accuracy 89.65% + 6.6% (SD) and testing
accuracy 83.53% + 8.59% (SD) were obtained. The wheelchair was controlled by
RaspberryPi through WiFi. The developed prototype demonstrated an average of
86.97% success rate for all trials with 4.015s for each command execution. The
prototype can be used efficiently in a home environment without causing any
discomfort to the user.
