Boosting the Evoked Response of Brain to Enhance the Reference Signals of CCA Method.

Brain-computer interface (BCI) systems can be used to communicate and express desires from people with severe nervous system damage. Among BCI systems based on evoked responses, steady state visual evoked potential (SSVEP) responses are the most widely used. Canonical correlation analysis (CCA)-based methods have been widely used in SSVEP-based online BCIs due to their low computation and high speed, and many methods have been introduced to improve the results. In this research, a method for constructing reference signals used in CCA based on the amplified evoked response of brain is introduced. In the proposed method, after removing the latency in the training signals, to construct reference signals, multilayer perceptron neural networks of the fitting type are used instead of the usual sine/cosine signals. The results show the success of this method in boosting the evoked responses of brain. The detection accuracy in 100-second time windows was 100%, and the information transfer rate in the same period was 240 bits per minute. Making reference signals similar to the recorded electroencephalogram allowed us to make more similarities in the CCA between the signals under consideration, and the reference signals, and to dramatically improve the results.