Real time classification of SSVEP brain activity with adaptive feedforward neural networks

In this study, real time classification of steady state-visual evoked potensials using the adaptive feedforward Neural Networks algorithm is proposed. The classification results is directly used to make a user able of controlling the directions (stop, forward, right, and left with stimuli frequencies of 7,5 10, 15, and 20 Hz, respectively) of a wheelchair based brain computer interface. The data was collected during a session in which eleven subjects with age about 24±2 years were tested. The proposed method showed a significant performance in the classification accuracy level and it gave an accuracy level of around 85%.