I built an AI that learns your brain in 15 minutes — here's every line of code скачать в хорошем качестве

I built an AI that learns your brain in 15 minutes — here's every line of code

Can you train a model to predict what someone is thinking from their brain signals alone? This is motor imagery classification — the core task behind brain-computer interfaces. You imagine moving your left hand, right hand, feet, or tongue. 22 electrodes on your scalp pick up the signal. A model tries to figure out which one. The hard problem: everyone's brain is different. A model trained on 8 people knows essentially nothing about a 9th. I spent 7 iterations trying to fix that. Final result: mean accuracy jumped from 55.67% to 70.41% — a +14.74 percentage point gain — in under 15 minutes per subject on a desktop CPU. Here's everything: the architecture, the data, the code, and the one subject that defeated every approach I tried. ────────────────────────────── WHAT'S COVERED ────────────────────────────── 00:00 The problem — why cross-subject EEG is hard 01:04 7 trials, 7 versions — the iteration story 01:45 Dataset — BNCI 001-2014 (BCI Competition IV 2a) 02:10 Euclidean Alignment — preprocessing that actually matters 03:00 The architecture — modified EEGNet with SE blocks 03:45 Trial 1 vs Trial 7 — what compounding fixes look like 04:47 The three paths — transfer, scratch, multiband 06:10 Training strategy — cosine annealing, SWA, ensemble weighting 06:45 Results — per subject breakdown 07:10 A06 — the subject that didn't respond to anything 07:39 What I'd do differently next time ────────────────────────────── LINKS ────────────────────────────── Dataset (MOABB): https://github.com/NeuroTechX/moabb Article:   / replicate-2-eeg-motor-imagery-transfer-lea...   ────────────────────────────── Built with: Python · TensorFlow · MOABB · NumPy · DaVinci Resolve Dataset: BNCI 001-2014 (publicly available) Hardware: Intel i7-14700K, no GPU #BCI #EEG #MachineLearning #NeuralNetwork #BrainComputerInterface #Python #TensorFlow #DeepLearning