GRFNet: a 3,847-parameter PyTorch model for multichannel EEG time-series classification
joekrry · reddit · 2026-11-08
Built from the author's MSc work on EEG decoding, GRFNet is an open-source PyTorch architecture for multichannel time-series classification.
- Design: ROCKET-style random convolutions; learned spatial filters mix input channels (initialized from electrode layouts when sensor positions are given), then a bank of mostly frozen dilated convolutions feeds summary statistics through a learned gate into a small classifier head.
- Size: the default 22-channel, 4-class model has just 3,847 parameters, accepts any input length and adapts to sampling rate.
- Evaluation plan: subject-independent benchmarks on PhysioNet's EEGMMIDB (109 subjects) against EEGNet and ShallowConvNet, with scalp topographies, confusion matrices, saliency maps and speed benchmarks, plus four ablation variants.
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