Physiological/Non-physiological artifacts classification using EEG signals based on CNN

Electroencephalographic signals have been shown to be extremely susceptible to noise from both external and internal sources. In this way, electroencephalography (EEG) can be considered a tool for analyzing the electrical activity of the brain. In the literature, there are numerous methods for detecting artifacts in EEG signals. Nevertheless, these approaches not attain a high level of accuracy in artifact detection. New techniques are needed to increase the accuracy level. Predictive Preventive and Personalized Medicine (PPPM) is becoming the focus of healthcare initiatives to guarantee the early identification of various types of artifacts. In this research, a binary classification is developed as physiological and non-physiological artifact detection. Various techniques are targeted for artifact classification as deep learning (DL) which is the most efficient strategy used for image and signals classification. Based on the convolutional neural network (CNN) model, we have classified the various artifact types. The Nihon Kohden device was used to acquire the available EEG data, which is collected from 32 channels. Our approach is validated with a CNN architecture for binary artifact classification. With a maximum accuracy of 99.55%, the proposed methodology delivers powerful classification findings.