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Comment by nabla9 | Hacker News Reader

I see the same in surface EEG studies.

Clueless ML researchers claim to read this and that from brains. Do they know or care that muscle and eye artifacts overlap most of the EEG frequency range? Do they realize that skin conductivity changes when people react to events? It's often easier for ML model to lean from side channels and skip brain waves altogether. ML can learn to move cursor on the screen by measuring unconscious muscle tension or tracking eye movements trough scalp electrodes.

ML is great for EEG signal analysis, but you do have to know what you are doing.

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Reminds me of how some fMRI tests may--if not properly calibrated and analyzed--appear to give brain results even when the subject is a dead salmon.

https://www.scientificamerican.com/blog/scicurious-brain/ign...

> It's often easier for ML model to lean from side channels and skip brain waves altogether.

My favorite example of this is a machine learning model for visually recognizing cancers that seemed to do extremely well... Until they realized it had actually learned that cancerous samples were more likely to have a measuring stick in the photo.

I'm reminded of the tank detector which actually detected overcast sky.

Compare https://gwern.net/tank

In theory ML or simpler statistical models like ICA should be able to separate those signals from each other (if they are separable) given the training data contains measurements from all scenarios (moving eyes, muscles, sweating, reacting to different stimuli etc.).

There are multiple techniques to detect and remove surface EEG artifacts, both physiological and non-physiological. My point is that some ML researchers don't even try, or seem to be unaware.

You say 'claim' but then we have clear cases where these techniques work, allowing paralyzed people to control a mouse or play a video game through scalp eeg sensors.

If you quote more than one word, it becomes clear that GP is not disagreeing with you:

> Clueless ML researchers claim to read this and that from brains. [...] ML is great for EEG signal analysis, but you do have to know what you are doing.

Both.

1) If you know what you are doing, it's possible to use ML algorithms to control cursor etc. from EEG coming from brain.

2) It's also possible for ML algorithms to enable paralyzed person (neck down) to to control a mouse or play a video game through scalp eeg sensors using eye and muscle artefacts.

Both are good, but the latter is clueless and would work better with better positioning of electrodes. Track eye and muscle tension directly. Paralyzed people care only that it works.

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