An Autocoder of the Electrical Activity of the Human Brain

The authors identify hidden parameters of the function describing the electrical activity of the human brain, obtained using electroencephalography (EEG), with the help of an artificial neural network and deep machine learning. The compression of applied information, necessary to reduce the dimensionality of the feature space of the data in order to obtain a model of an artificial neural network– an autoencoder is formulated. The novelty of the general solution and the theoretical aspects and problems of existing compression methods are described. An experimental study is carried out, which consists in obtaining an autoencoder model using applied data EEG sequences containing visual evoked potentials. The compression problem is solved by decreasing the dimensionality of the multidimensional vector associated with the sample. The autoencoder encodes the original multi-dimensional vector into a vector of smaller dimensionality. Using deep machine learning, a coding function is found such that reverse decoding into the original vector can be performed. As a result of the empirical selection of the vector dimensionality, the best experimental model of the autoencoder was chosen, which compresses the feature space of dimensionality equal to 1260 (in the initial sense EEG signals of duration 0,2 s) to a 24-dimensional space, with the possibility of the reconstruction of the initial signal with losses of not more than 10 %.