PRE-PROCESSING NEUROPHYSIOLOGICAL DATA ALGORITHM
The article is devoted to the development of an algorithm for pre-processing of neurophysiological data obtained using fNIRS (functional near- infrared spectroscopy). The developed algorithm can be used for the picking and systematization of a data set for training and testing deep learning neural networks to identify neurophysiological patterns of human movements. Also, the algorithm can be used for statistical analysis of data obtained experimentally. A distinctive singularity of the developed algorithm is the flexibility of constructing and the adapt ability to the processing requirements presented, depending on the specifics of the problem. The developed algorithm allows to form a data set for neural network training and to recognize patterns of activity of the human wrist.
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