CNN Brain Label-Maker: Computer Vision Based ICA Rejection EEG based System Architecture

Abstract

The electroencephalogram (EEG) is a practical and reasonably applied tool for researching brain disorders and behavior changes. EEG offers a minimally restricted and non-invasive method, where the significant difficulties in utilizing EEG in studies on cognitive development are in the temporal resolution, the outbound signal sources, and the EEG Artifacts. Making an informed judgment about the application of EEG technology requires careful consideration of these factors. Independent component analysis (ICA) has been demonstrated to isolate the various source generator processes underlying simultaneously recorded signals from multiple, near-adjacent EEG scalp electrode channels. ICs generated by ICA decomposition must be manually inspected, chosen, and interpreted, but this process takes time and experience. Automated IC category classification of ICs can be achieved sufficiently accurately, which expedites the analysis of large-scale EEG research and permits the use of ICA decomposition in near-real-time applications. At the same time, for many such classifiers, the next step of Brain activity rejection is necessary for medical specialists. Thus, this work presents an automated convolution neural network-based brain activity labeling for ICA rejection using the data from the well-used and widely utilized by neurologists and Scientists such as ICLabel MATLAB, EEGLab tools, etc. Replacing the manual task via an atoms system, which makes the proposed system reduces the processing time by 7200x and accuracy of 89.45%. The proposed system was trained, verified, and tested using CCHMC clinical data, using a 128-channel HydroCel electrode net (Magstim EGI, Eugene, OR) and an EGI NetAmp 400 at a 1000Hz sampling rate.