Decrease the time for classification of the incoming signals from BCI

In the recent years the attention to Brain-Computer Interface (BCI) devices and their potential for decoding human brain signals have risen considerably. However, a number of issues related to the classification of received signals still remain unresolved. This study focuses on increasing the speed of classification of data obtained from the Brain Computer Interface (BCI), without significantly affecting the accuracy of processing and classification. Our research team focuses on the possibilities to reduce the number of channels as one of the potential factors for increasing the speed of incoming data classification. Experimental data is obtained by using Emotiv Epoc 14+. For data processing we used Python. The data is classified with K-Neighbors algorithm.