Artifact detection benchmark
Compare classic and ML methods for removing blinks, jaw clenches and motion artifacts from low-channel EEG, using public datasets or your own recordings.
Possible Outcome
A notebook + short paper-style write-up.
You own what you build. Publish it under your own GitHub account with your name and chosen open-source licence.
Why It Matters for Open Neurotech
In 4- to 16-channel wearable EEG, standard high-density ICA often struggles. Benchmarking lightweight wavelet, ASR, regression, and compact neural methods helps every builder choose the right filter.
Starting Points & Technical Notes
These are open-ended suggestions, not specifications or assigned tasks. Change the scope, combine ideas, or take a completely different approach:
- Evaluate both signal distortion on clean epochs and artifact attenuation on labeled blink/EMG epochs.
- Measure CPU runtime per 250 ms window so builders know which algorithms can run live on Raspberry Pi or in the browser.
- Publish reproducible Jupyter notebooks with MNE-Python and clear SNR metrics.
Independent Open-Source Work
PiEEG Ltd does not take on volunteers, interns or unpaid workers. Any project you start is your own independent activity under your own repository and responsibility. Read the full ground rules →
Safety First
PiEEG devices are not medical devices. Use battery power only when electrodes are on a person, and never connect to mains power during recording. Liability & safety →
More Signal Processing & ML Ideas
Real-time motor-imagery decoder
Build an open, real-time left/right motor-imagery classifier that runs on a Raspberry Pi or laptop and streams from an open EEG board.
SSVEP speller in the browser
A web-based SSVEP speller with flicker targets and CCA detection that works with an open EEG data stream.
Low-channel-count transfer learning
Can models pre-trained on large public EEG datasets be adapted to 8 or 16 consumer-grade channels? A good topic for a thesis or a preprint.