Classification of SSVEP Signals using Neural Networks for BCI Applications

Brain-Computer-Interface (BCI) is an exceedingly growing field of research where individual communicates to the computer, without physical connection. The natural responses to visual stimulation at a particular frequency of EEG are characterized as Steady-State Visually Evoked Potential (SSVEP) signals. Efficient classification of EEG signals is an important phase in BCI. In this paper, a method is anticipated for classification of SSVEP signals in which the standard dataset and Neural Network (NN) classifier is applied. The improved classification accuracy of 90 % is achieved using the proposed method. This methodology is useful in BCI applications such as assisting people who are suffering from neurodegenerative problems; Amyotrophic Lateral Sclerosis (ALS) for automatic wheelchair navigation-based multimedia applications, etc.