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BRIDGE-EEG: Bridging Self-Supervised Pretraining and Efficient Deployment for Cross-Dataset EEG Classification — arXiv:2609.12218

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.12218
License (as archived): CC0-1.0
Archived: 2026-10-16T03:15:02.809Z
SHA-256 of the archived text: 802fae1a8a7fbfdeb0e18891c41f19cf123c391ebfa07715af2c64f0b902aea7
Stable URL of this page: https://metraipolis.com/documento/dc_b9852012a215

How to cite

BRIDGE-EEG: Bridging Self-Supervised Pretraining and Efficient Deployment for Cross-Dataset EEG Classification — arXiv:2609.12218 — arXiv (en), https://arxiv.org/abs/2609.12218 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_b9852012a215 (Metraipolis archive dc_b9852012a215, sha256 802fae1a8a7fbfdeb0e18891c41f19cf123c391ebfa07715af2c64f0b902aea7)

BibTeX

@misc{dcb9852012a215,
  title = {BRIDGE-EEG: Bridging Self-Supervised Pretraining and Efficient Deployment for Cross-Dataset EEG Classification — arXiv:2609.12218},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.12218},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_b9852012a215; sha256 802fae1a8a7fbfdeb0e18891c41f19cf123c391ebfa07715af2c64f0b902aea7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.12218. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The growing use of electroencephalography (EEG) motivates automated analysis that is accurate, transferable, and deployable on constrained hardware. Recent EEG foundation models learn general representations from large-scale pretraining, but their size and computational cost limit edge and wearable deployment. We introduce BRIDGE-EEG, an efficient multi-task EEG classification pipeline that preserves the benefits of pretraining while reducing model size. A unified preprocessing scheme maps heterogeneous recordings with different channel counts, montages, and sampling rates to a device-agnostic 62-channel time--frequency representation. We pretrain an SE-ResNet18 teacher (11.84 M parameters) with SimCLR on unlabeled EEG from five heterogeneous datasets, then compress it into SE-ResNet8 (1.56 M) and SE-ResNet4 (0.48 M) students using task-agnostic and task-specific distillation. We evaluate six benchmarks spanning abnormality detection, motor imagery, and emotion recognition. For abnormality detection and emotion recognition, the students achieve accuracy comparable to or better than several recent EEG foundation models with 10--1,000$\times$ more parameters. Motor imagery shows a remaining representation gap, highlighting the importance of pretraining diversity. Inference profiling on a server GPU, desktop CPU, and NVIDIA Jetson Orin Nano shows up to 3.0$\times$ lower edge energy per inference (15.64 mJ vs. 46.67 mJ). The compact models further support future deployment on MCU-class wearables.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-14 in cs.HC).
Categorie arXiv: Human-Computer Interaction (cs.HC) ; Machine Learning (cs.LG); Signal Processing (eess.SP).
Pagina del lavoro: https://arxiv.org/abs/2609.12218 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T03:05:23.207Z dalla pagina https://arxiv.org/catchup/cs.HC/2026-09-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).