2024/12/18 by Zag ElSayed, Nathan Suer, Grace Westerkamp +5 · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Neuroscience · Physics and Astronomy · Psychology · #Architecture #Artificial intelligence #Brain Tumor Detection and Classification #Computer science #Computer vision #Human–computer interaction #Medical Imaging and Analysis #Neurology #Neuroscience #Psychology #cs.LG #physics.data-an #q-bio.NC #q-bio.QM
paper · pdf · doi:10.1109/icmla61862.2024.00229
openalex publication_date 2024/12/18 · openalex created_date 2025/10/10 · arxiv published 2026/07/22 · arxiv updated 2026/07/22 · openalex updated_date 2026/08/05
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