The study evaluates how low‑precision compression affects adversarial robustness in EEG decoders used for brain‑computer interfaces. Using BCI Competition IV‑2a data, the authors compare 32‑bit floating‑point models (EEGNet and ShallowConvNet) with models pruned to 50 % and quantized to INT8 via post‑training quantization (PTQ) or quantization‑aware training (QAT). Results show that accuracy‑preserving compression does not improve direct robustness—PGD attack accuracy remains 22–24 % across all variants—yet pruning reduces bidirectional transfer efficiency more than PTQ, indicating that robustness, transferability, and deployment efficiency are distinct properties of compressed EEG decoders.
By Saim Rehman, Muhammad Shafique
ThinkNet is a validation‑controlled framework that uses train‑only normalization, evolutionary search guided by validation, and validation‑gated inference to build compact motor‑imagery EEG decoders that generalize to unseen subjects. In a fixed benchmark on the BCI Competition IV‑2a dataset, the best single model achieved 44.35 % accuracy with only 4.9 k parameters, while a compact ensemble of six families reached 43.31 % accuracy. Across a broader search, models with ≤25 k parameters outperformed larger ones after retraining, but validation reliability remains a bottleneck under subject shift.
By Abdul Basit, Saim Rehman, Muhammad Shafique
arXiv:2608. 07033v1 Announce Type: new Abstract: This work investigates whether Electroencephalograph (EEG) foundation models (EFMs) can be made faster and locally deployable without sacrificing accuracy.
By Lingwei Li, Yirong Kan, Peng Chen, Xu Cao, Zheng Chen, Yasuhiko Nakashima
TriRouter is a new electrode‑space routing framework designed to improve motor‑imagery brain‑computer interface decoding from multichannel EEG when electrode montages are sparse or corrupted. It incorporates discriminative‑preservation, mask‑robustness, and latent‑compensation paths, and uses a validation‑calibrated router that allocates path contributions based on observable channel availability. In the BNCI2014‑001 dataset, TriRouter outperforms standard public backbones and a dynamic‑spatial‑filtering corrupted‑EEG baseline, achieving accuracy gains of 3.68, 4.02, and 6.46 percentage points at 25%, 50%, and 75% missing settings, and remains effective under structured regional missingness and low‑channel evaluations.
By Ying Zhou, Yaoxin, Mengting Liu
arXiv:2609.23924v1 Announce Type: new
Abstract: Pretrained EEG foundation models are increasingly proposed as general-purpose encoders for brain-computer interfaces, yet recent benchmarks disagree ab...
By Kevin Zhou, Sparsh Roy
arXiv:2606. 08594v1 Announce Type: new Abstract: Deep learning EEG denoising architectures have scaled from tens of thousands to tens of millions of parameters, yet no prior study has isolated model capacity as the experimental variable or tested whether reconstruction metrics predict downstream neural-signal utility.
By Jasmeet Singh Bindra, Siddharth Panwar, Shubhajit Roy Chowdhury
arXiv:2606. 00815v1 Announce Type: new Abstract: Electroencephalography (EEG) supports a variety of brain-computer interface (BCI) tasks ranging from brain-state monitoring to human-LLM interactions.
By Ziling Lu, Zongsheng Li, Xinke Shen, Kexin Lou, Yingyue Xin, Xiaoqi Chen, Shinan Wang, Xiang Chen, Jiahao Fan, Chenyu Huang, Xin Xu, Zhoujie Hou, Chen Wei, Quanying Liu
RobustSeiz is an open‑source, model‑agnostic framework designed to benchmark the robustness of EEG seizure detection models under realistic clinical stressors. It standardizes four public scalp‑EEG corpora into BIDS‑EEG trees, applies controlled distribution shifts—including environmental, noise, and adversarial transforms—across predefined hyperparameter grids, and reports comprehensive performance metrics such as sensitivity, precision, F1, false positives per 24 h, onset timing, and predictive agreement. The framework offers a Dockerized GPU pipeline, experiment registry, and both full‑evaluation and research‑subset modes, and demonstrates its utility by evaluating a contemporary detector on TUSZ across the full shift grid.
By Mohammad Mohammadi, Alireza Zarei
EEG-AS is an instance-level algorithm selection framework designed for EEG foundation models. It characterizes each EEG instance using latent embeddings, handcrafted neurophysiological features, and an anchor foundation model, then learns to reconstruct the behaviors of other foundation models from privileged prediction tokens. During inference, EEG-AS estimates these behaviors without running the full model portfolio, enabling efficient selection among seven EEG foundation models and significantly reducing the performance gap between the single best solver and the oracle upper bound across seven public EEG benchmarks.
By Yunzhen Zhang, Ruoxi Piao, Hasan Onur Keles, Mustafa Misir
arXiv:2607. 22397v1 Announce Type: cross Abstract: Frozen EEG encoders proliferate; per-model fine-tune defaults do not scale.
By Sergey Musienko
BRIDGE-EEG is an efficient multi‑task EEG classification pipeline that leverages self‑supervised pretraining while dramatically reducing model size. It maps heterogeneous EEG recordings to a unified 62‑channel time‑frequency representation, pretrains an SE‑ResNet18 teacher with SimCLR, and distills it into smaller SE‑ResNet8 and SE‑ResNet4 students. The compact models achieve accuracy comparable to or better than larger foundation models on abnormality detection and emotion recognition, and they consume up to three times less energy on edge devices, enabling deployment on wearable hardware.
By Meghna Roy Chowdhury, Chengwei Zhou, Haotian Yu, Gourav Datta, Shreyas Sen
arXiv:2609.20904v1 Announce Type: cross
Abstract: Hybrid motor-imagery brain-computer interfaces (MI-BCIs) combining EEG and fNIRS can outperform EEG-only systems by exploiting complementary electrop...
By Boyuan Zhao, Sifan Zhang, Luping Chen