Multi-channel neuro signal classification using Adam-based coyote optimization enabled deep belief network

Abstract

Electroencephalogram (EEG) signals gather the spiking activities of the brain based on its standardized electrodes of the scalp. Classification of EEG signal is a significant task for designing the precise “Brain-Computer Interface” system. Even though numerous studies have included the “time and frequency domain features” for classifying the EEG signals, a few studies combine the “spatial and temporal dimensions” of the EEG signal. Brain dynamics consists of high complexity among various mental tasks and so, it is challenging to build an efficient approach with features using their prior knowledge. Therefore, the main intention of this paper is to deal with the automated multi-channel EEG signal classification concerning different diseases like epilepsy, brain tumor, and Parkinson’s disease. The EEG signals are initially gathered from the different benchmark datasets. Further, the feature extraction of signals is performed by the Haar DWT and spike detector. These features are further subjected to the “Enhanced Deep Belief Network” with RBM layers, in which the parameter tuning of DBN is performed by the Adam-based Coyote Optimization Algorithm for classifying the signal. The experimental results demonstrate the effective performance of the developed neuro signal classification model by reducing the signal complexity while classifying the signals at high accuracy.