Seyed (Yahya) Shirazi
@neuromechanist.bsky.social
Project Scientist at SCCN, UCSD | Working on Computational Neuroscience tools and how 🧠and 💪🏼interact | Opinions are my own! | Likes and reposts are bookmarks
This was an excellent experience.
I am glad that I could represent #EEGLAB, one of the largest academic #OpenScience research platforms and communities.
This also sparked an effort for sustainable funding of open science projects via a tiny overhead of research grants. Read more at osc.earth
I am glad that I could represent #EEGLAB, one of the largest academic #OpenScience research platforms and communities.
This also sparked an effort for sustainable funding of open science projects via a tiny overhead of research grants. Read more at osc.earth
Sustainable Funding for Academic Open Science
Sustainable Funding for Academic Open Science
osc.earth
July 15, 2025 at 8:52 PM
This was an excellent experience.
I am glad that I could represent #EEGLAB, one of the largest academic #OpenScience research platforms and communities.
This also sparked an effort for sustainable funding of open science projects via a tiny overhead of research grants. Read more at osc.earth
I am glad that I could represent #EEGLAB, one of the largest academic #OpenScience research platforms and communities.
This also sparked an effort for sustainable funding of open science projects via a tiny overhead of research grants. Read more at osc.earth
𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀:
• Spatial filters (Laplacian) - real-time friendly
• ICA source separation - more accurate
The assumption "electrode X = brain region Y" is wrong. Use source-level analysis.
• Spatial filters (Laplacian) - real-time friendly
• ICA source separation - more accurate
The assumption "electrode X = brain region Y" is wrong. Use source-level analysis.
July 15, 2025 at 3:54 PM
𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀:
• Spatial filters (Laplacian) - real-time friendly
• ICA source separation - more accurate
The assumption "electrode X = brain region Y" is wrong. Use source-level analysis.
• Spatial filters (Laplacian) - real-time friendly
• ICA source separation - more accurate
The assumption "electrode X = brain region Y" is wrong. Use source-level analysis.
𝗞𝗲𝘆 𝗳𝗶𝗻𝗱𝗶𝗻𝗴: A 3x3cm brain patch projected strongest to DISTANT scalp areas, not overhead. Standard deviation & entropy showed inverse patterns with distance.
𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: Channel-level analysis misattributes brain signals due to volume conduction mixing.
𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: Channel-level analysis misattributes brain signals due to volume conduction mixing.
July 15, 2025 at 3:54 PM
𝗞𝗲𝘆 𝗳𝗶𝗻𝗱𝗶𝗻𝗴: A 3x3cm brain patch projected strongest to DISTANT scalp areas, not overhead. Standard deviation & entropy showed inverse patterns with distance.
𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: Channel-level analysis misattributes brain signals due to volume conduction mixing.
𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: Channel-level analysis misattributes brain signals due to volume conduction mixing.