PhysioWave-T-EEG: multi-route pretrained EEG encoder

One of the three PhysioWave-T encoders (EEG, ECG, sEMG); the RALF fusion model combines them. Code: https://github.com/ForeverBlue816/PhysioWave.

Masked dual-reconstruction pretraining on multi-montage EEG. One shared transformer serves four electrode layouts through route-specific wavelet frontends, and channel identity enters as a learned embedding of the channel NAME rather than of its index.

Trained for 60 epochs (46080 optimizer updates) on a mixture of public EEG corpora, 30.3 M parameters.

Objective

0.5*L_spec + 0.5*L_raw + 0.001*L_foldKL

Two heads at equal weight. One predicts folded wavelet patches, the other predicts the preprocessed EEG waveform of the same masked patches. Masking is applied BEFORE the wavelet frontend (True), at ratio 0.7, and the spec target is per-patch normalised (True).

Training curves

Pretraining ran for 60 epochs (46,080 optimizer updates) on 16 GPUs over the seven corpora. Validation follows training on every term to the last epoch, so the model is not over-fitting its 15 M windows; the released checkpoint is the epoch with the lowest validation total loss.

Train and validation per epoch: the total loss, the two reconstruction terms (folded wavelet patch, raw waveform) and the masked spectral correlation.

Validation total loss per route (left) and per corpus (right). All four routes converge; the dense 64-channel route reaches the lowest loss and the 19-channel clinical route (TUEG, the largest corpus) the highest. The dashed line is the route-macro mean, which weights the routes equally.

Validation, final epoch

metric	value	what 1.0 would mean
total loss	0.2488	—
spec MSE	0.3046	—
spec NMSE	0.305	predicting zero
spec Pearson r	0.831	perfect
raw SmoothL1	0.1930	—
raw NMSE	0.278	predicting zero
raw Pearson r	0.847	perfect
route-macro total	0.2403	—

NMSE is dimensionless: MSE(prediction, target) / MSE(0, target), so 1.0 is what predicting zero scores on that head's own target. The two losses are NOT comparable to each other -- one is an MSE on normalised wavelet coefficients, the other a SmoothL1 on z-scored volts. The columns that compare the heads are the correlations and the NMSEs.

The route-macro row weights the four routes equally. The plain rows weight by sample count, and validation is dominated by the two largest corpora, so a model specialising toward the smaller ones moves the two rows in opposite directions.

Files

Which file do you want

your situation	download
fine-tune on any montage	eeg_c1_encoder.pth
your montage IS one of the four routes below	pretrain_eeg_c1_moe_best.pth, then cut the route out (one command, below)
continue pretraining, or take the model apart yourself	pretrain_eeg_c1_moe_best.pth

pretrain_eeg_c1_moe_best.pth is the complete model -- all four wavelet frontends, both patchers, both reconstruction decoders. Nothing is held back; only the optimiser state was stripped, which no downstream use needs. Take it if you want to keep or strip pieces yourself.

eeg_c1_encoder.pth is the same weights minus everything route-bound and pretraining-only. It exists because it cannot be loaded wrong.

file	what it is
eeg_c1_encoder.pth	encoder alone: channel embedding, gate, transformer
pretrain_eeg_c1_moe_best.pth	complete model, lowest validation total loss (epoch 57)
pretrain_eeg_c1_moe_final.pth	complete model, last epoch (epoch 60)
pretrain_eeg_c1_moe_best_spec.pth	complete model, lowest spec MSE (epoch 59)
pretrain_eeg_c1_moe_best_raw.pth	complete model, lowest raw SmoothL1 (epoch 57)
metrics_epoch.jsonl	the full training curve
config_resolved.yaml	every resolved hyperparameter

Why several checkpoints

Each is the minimum of a different validation metric, and they are published together so a downstream comparison can ask which criterion transfers instead of assuming the one that names best.pth. The four bars are within validation noise of each other here, which is exactly why the question is worth asking and why reconstruction loss cannot answer it.

best.pth is selected on the TOTAL loss -- the loss that was trained. It is not the best spec loss, and not the best raw loss.

pretrain_eeg_c1_moe_best_macro.pth would have been byte-identical to pretrain_eeg_c1_moe_best.pth -- the same epoch won both bars -- so it is not published separately.

What is in the complete model

component	parameters	share
shared transformer	25.22 M	83.2%	encoder, always transfers
channel-name embedding	0.02 M	0.1%	always transfers
channel projection	0.03 M	0.1%	always transfers
wavelet frontends (all 4 routes)	3.37 M	11.1%	route-bound; downstream usually builds its own
patchers (both rates)	0.20 M	0.7%	route-bound
spec decoder	0.72 M	2.4%	pretraining only
raw decoder	0.72 M	2.4%	pretraining only
total	30.30 M	100%

The shared transformer is 83% of the model and the four frontends together are 11%, which is why there is no per-route encoder file here: four of them would duplicate that transformer four times to deliver frontends that are a small fraction of it. Cut the one you want out of the complete checkpoint instead.

What transfers, and what does not

downstream.py splits a checkpoint into two sets. The channel embedding, its gate and the shared transformer always load. The wavelet frontend and the patcher load only when your montage is the route they were trained for -- same electrode count, same patch length -- and are skipped otherwise rather than reshaped.

So a fresh frontend is the normal case, not a fallback. Both downstream tasks in the source repository build their own.

The four routes the shared transformer was pretrained across:

route	channels	rate
E128_512	128	512 Hz
E19_256	19	256 Hz
E32_512	32	512 Hz
E64_256	64	256 Hz

To fine-tune ON one of them exactly, take that route's frontend and patcher out of the complete checkpoint:

python scripts/export_eeg_pretrained_encoder.py \
    --checkpoint pretrain_eeg_c1_moe_best.pth --route E64_256 \
    --output encoder_E64_256.pth

Omit --route and you get eeg_c1_encoder.pth again. Both pretraining decoders are dropped either way: fine-tuning against the pretext objective by accident is the failure that prevents.

best.pth and the final checkpoint are within validation noise of each other. Which one transfers better is a downstream question, not a reconstruction-loss question -- measure both.

The channel vocabulary travels with the weights

An embedding row means whatever channel held that id when the row was learned. Every checkpoint here carries the vocabulary and its hash. Resolving channel names against a different vocabulary silently trains on relabelled electrodes, which is why the hash is checked rather than assumed.

Architecture

embed_dim   512      depth 8      heads 8
norm        rmsnorm     ffn swiglu     qk_norm True
wavelet     max_level 3, kernel 16
channel     id embedding, injected at the token site
mask        frequency_guided, ratio 0.7

Loading

EEGC1Downstream builds the frontend and patcher for your montage and loads this file into everything else. Nothing here is route-bound, so the montage, the window length and the patch length are yours to choose.

import torch
from physiowave.eeg_c1.downstream import EEGC1Downstream

ENC = "eeg_c1_encoder.pth"
ck = torch.load(ENC, map_location="cpu", weights_only=False)
mc = ck["model_config"]                  # the width the weights were trained at
CH = ["Fz", "Cz", "Pz", "Oz", "C3", "C4", "P3", "P4"]   # your electrodes

model = EEGC1Downstream(
    in_channels=len(CH), window_samples=1024, sampling_rate=256.0,
    patch_samples=64, num_classes=2, channel_names=CH,
    embed_dim=mc["embed_dim"], depth=mc["depth"], num_heads=mc["num_heads"],
    channel_embed_dim=mc.get("channel_embed_dim", 64),
    channel_vocab_size=ck["channel_vocab_size"])

report = model.load_pretrained(ENC)
print(model.describe_transfer(report))
# loaded: transformer N, channel encoder N, gate yes, frontend 0, patcher 0

class Meta:                               # or your dataset's ChannelMeta
    channel_names = CH
    channel_mask = None

out = model(torch.randn(2, len(CH), 1024), Meta())   # {"logits": [2, 2]}

frontend 0, patcher 0 is the expected line, not a warning: those are the two things your montage supplies.

The frontend you build is not randomly initialised. Its filters are seeded from real wavelet families -- sym4, sym5, db6, sym8, db8 -- and learned from there during fine-tuning. What a fresh frontend costs you is the adaptation those filters underwent during pretraining, not the wavelet prior itself.

Channel names, not indices, are what this encoder and your data agree on. An embedding row means whichever electrode held it when it was learned, so pass real names and check channel_vocab_sha256 -- load_pretrained refuses a mismatch rather than silently training on relabelled electrodes.

Code: https://github.com/ForeverBlue816/PhysioWave

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Prepared by scripts/prepare_hf_release.py from pretrain_eeg_c1_moe on 2026-09-09.