braindecode-native re-host of the official PopulationTransformer (PopT) popt_brainbert_stft checkpoint, remapped so it loads directly with braindecode.models.PopulationTransformer.

PopT is a self-supervised population model for intracranial recordings (iEEG/sEEG). It does not encode a raw time signal: each electrode is represented by a feature vector — typically the frozen embedding of a per-channel foundation model such as BrainBERT (stft features, 768-d) — and PopT aggregates across the electrode population. Each electrode feature is linearly projected and given a fixed sinusoidal spatial position encoding built from its integer anatomical coordinates; a CLS token is prepended, a stack of Transformer encoder layers mixes the population, and the CLS output is the pooled representation used downstream.

Provenance


Original code	https://github.com/czlwang/PopulationTransformer
Original weights	https://huggingface.co/PopulationTransformer/popt_brainbert_stft
Paper	Chau et al. (2024), Population Transformer: Learning Population-level Representations of Neural Activity, arXiv:2406.03044

These weights are a format conversion only of the authors' released checkpoint — no re-training. The input embedding, the spatial positional encoding and the 6-layer Transformer encoder are carried over bit-for-bit (verified: all 82 mapped tensors are identical to the source, and the ported encoder reproduces the upstream PtModelCustom output to < 1e-5). The upstream masked-modelling heads (cls_head, token_cls_head) are not carried; the braindecode classification head (final_layer) is randomly initialised and must be trained/fine-tuned for your task.

Configuration

param	value
`hidden_dim`	512
`ffn_dim`	2048
`n_layers`	6
`n_heads`	8
`n_times` (feature dim)	768
`max_len` (coord table)	5000
activation	GELU
parameters (encoder + spec head)	~20.6M

Usage

python

import torch
from braindecode.models import PopulationTransformer

# n_chans = number of electrodes; n_outputs = your task's classes.
model = PopulationTransformer.from_pretrained(
    "braindecode/popt-pretrained", n_outputs=2
)

# input = per-electrode features (e.g. frozen BrainBERT stft embeddings),
# shape (batch, n_electrodes, 768). Electrode coordinates are read from
# chs_info when available, otherwise fall back to sequential indices.
x = torch.randn(4, 64, 768)
logits = model(x)                       # (4, n_outputs)
cls = model(x, return_features=True)    # {"features": ..., "cls_token": ...}

The classification head is task-specific: pass your own n_outputs (the head is re-initialised) and fine-tune. Electrode positions can be provided through chs_info (their loc) so the spatial encoding reflects the real montage.

Licensing

The upstream PopulationTransformer repository ships no explicit license file, so the license of these weights is marked unknown. They are re-hosted here for research use with attribution; if you use them, cite the original work and respect any terms the authors may later publish. The braindecode code is BSD-3-Clause.

Citation

bibtex

@article{chau2024population,
  title={Population Transformer: Learning Population-level Representations of Neural Activity},
  author={Chau, Geeling and Wang, Christopher and Talukder, Sabera and Subramaniam, Vighnesh and Soedarmadji, Saraswati and Yue, Yisong and Katz, Boris and Barbu, Andrei},
  journal={arXiv preprint arXiv:2406.03044},
  year={2024}
}