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This work proposes a hybrid Brain Computer Interface system for theautomation of a wheelchair for the disabled. Herein a working prototype of aBCI-based wheelchair is detailed that can navigate inside a typical homeenvironment with minimum structural modification and without any visualobstruction and discomfort to the user. The prototype is based on a combinedmechanism of steady-state visually evoked potential and eye blinks. To elicitSSVEP, LEDs flickering at 13Hz and 15Hz were used to select the left and rightdirection, respectively, and EEG data was recorded. In addition, the occurrenceof three continuous blinks was used as an indicator for stopping an ongoingaction. The wavelet packet denoising method was applied, followed by featureextraction methods such as Wavelet Packet Decomposition and CanonicalCorrelation Analysis over narrowband reconstructed EEG signals. Bayesianoptimization was used to obtain 5 fold cross-validations to optimize thehyperparameters of the Support Vector Machine. The resulting new model wastested and the average cross-validation accuracy 89.65% + 6.6% (SD) and testingaccuracy 83.53% + 8.59% (SD) were obtained. The wheelchair was controlled byRaspberryPi through WiFi. The developed prototype demonstrated an average of86.97% success rate for all trials with 4.015s for each command execution. Theprototype can be used efficiently in a home environment without causing anydiscomfort to the user.

Wheelchair automation by a hybrid BCI system using SSVEP and eye blinks