Computer Vision Based Neurology Brain Activity Rejection Architecture and Implementation

Abstract:The electroencephalogram (EEG) is a valuable and widely applied tool for investigating brain disorders and behavioral changes. It offers a minimally restrictive and non-invasive method. However, challenges in using EEG for cognitive development studies include temporal resolution, signal source localization, and EEG artifacts. Careful consideration of these factors is essential for informed application of EEG technology. Independent component analysis (ICA) effectively isolates source generator processes from signals recorded by multiple, adjacent EEG scalp electrodes. Although ICA decomposition requires manual inspection, selection, and interpretation of independent components (ICs), this process is time consuming and demands expertise. Automated IC classification can achieve sufficient accuracy, expediting large scale EEG research and enabling near real time applications in conjunction with brain activity rejection tasks, which are crucial for medical specialists. This study introduces an automated computer vision based ICA rejection labeling tool compatible with widely used software interfaces like ICLabel and EEGLab. By automating the manual task, the proposed system reduces processing time by 7200 fold and achieves an accuracy of 89.45%.

Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an); Quantitative Methods (q-bio.QM)

arXiv:2607.21654 [q-bio.NC]

(or arXiv:2607.21654v1 [q-bio.NC] for this version)

https://doi.org/10.1109/ICMLA61862.2024.00229 https://doi.org/10.1109/ICMLA61862.2024.00229 https://doi.org/10.1109/ICMLA61862.2024 DOI(s) linking to related resources