WeakEEGSignalCategoryDetection

EEG Weak Signal Category Detection

Research code for detecting weak category-specific signals in EEG data during visual object perception. Vibecoded after months of brain-decoding experiments—detects subtle probability shifts (Δ ~0.3-1.7%) for 38 COCO categories, excluding lab confounds.

Overview

This project explores whether EEG contains subtle info about viewed objects, controlling for lab artifacts (e.g., people/chairs). Using multi-label classification on the Alljoined dataset, we find statistically significant signals for foods/vehicles, with source localization revealing ventral temporal hotspots.

Key Insight: EEG encodes semantics weakly (~200-350ms post-stimulus) in object recognition networks, but effects are small (d<1.0)—exploratory, not production-ready BCI.

Intended Use

• Research: Test weak semantic decoding; localize category signals with MNE.
• Education: Demo EEG-AI integration for neuro classes.
• Not For: Real-time BCI or clinical use (signals too noisy).

Limitations

• Weak effects: Δ=0.3-1.7%; N<20 for rares limits power.
• Confounds: Lab objects inflate signals 3-4x.
• EEG Limits: ~1-2cm resolution; poor for deep structures (e.g., amygdala).
• Data: Assumes COCO annotations + Alljoined HF dataset.

Training Data

• Dataset: Alljoined EEG-Image (Gifford et al., 2022): 6k+ COCO images + 64-ch BioSemi EEG.
  • HF: Alljoined/05_125 (split='test').
  • COCO Annotations: Download 2017 Train/Val (instances_train2017.json).
• Preprocess: 50-350ms window, z-score normalize per channel.
• Categories: 38 non-lab (animals, vehicles, food, outdoor/sports).

Model Details

• Architecture: CNN (3 conv blocks: 128→256→512 ch) + FC classifier. BCE loss, AdamW.
• Params: ~6.7M. Input: (64 ch × 154 tp). Output: 38 sigmoid probs.
• Training: 30 epochs, 80/20 split, cos LR. Val loss: ~0.15.

Evaluation Results

Test on ~700 samples (N≥10/category). Metrics: Mean prob shift (Δ), Cohen's d, t-test p.

• p<0.05, ** p<0.01. 2/35 sig; 70% positive Δ.

Ventral temporal hotspots at 250ms (d=1.027).

Source Localization

• Method: MNE sLORETA on fsaverage (ico4 src, 3-layer BEM).
• Patterns: Early occipital (visual), late temporal (semantics). Food: Ventral bias; animals: Parietal spread.

Usage

Inference

from transformers import pipeline  # Or load custom
# Custom load (PyTorch):
import torch
model = torch.load("clean_signal_detector.pth")  # Load as in App.py
# Run on EEG: probs = torch.sigmoid(model(eeg_tensor))