Computer Vision Based Neurology Brain Activity Rejection Architecture and Implementation
Mirrored from arXiv — Machine Learning for archival readability. Support the source by reading on the original site.
Quantitative Biology > Neurons and Cognition
Title: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%.
| Comments: | 7 pages, 10 figures, Conference |
| Subjects: | Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2607.21654 [q-bio.NC] |
| (or arXiv:2607.21654v1 [q-bio.NC] for this version) | |
| https://doi.org/10.48550/arXiv.2607.21654
arXiv-issued DOI via DataCite
|
|
| Journal reference: | ICMLA 2024 |
| Related DOI: | 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
|
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
Current browse context:
References & Citations
Bookmark
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — Machine Learning
-
LoKiFormer: Locality-aware Attention with Decoupled Knowledge Memory for Efficient Large Language Model Pretraining
Aug 14
-
Which Site, and When: A Free-Satellite-Data Test of Himalayan Glacial Lake Bursts, Landslides, and Ice Floods
Aug 14
-
MARCH: Scaling Recurrent Memory with Content-Routed State Anchors
Aug 14
-
Multi-AUV Ad-hoc network-based Target Tracking: A Value Gradient Guidance Multi-Agent Diffusion Reinforcement Learning Approach
Aug 14
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.