arXiv Machine Learning

Beyond Local Power: Functional Connectivity Analysis for Subject-Independent Learning Style Recognition

arXiv:2608. 12000v1 Announce Type: cross Abstract: Identifying individual learning styles optimizes pedagogical efficacy.

arXiv Machine Learning

Jul 7

Stacked LoRA for Subject-Adaptive EEG Foundation Models in Motor Imagery Decoding

arXiv:2607. 03094v1 Announce Type: new Abstract: Electroencephalography (EEG) decoding for brain-computer interfaces (BCIs) faces a major challenge: substantial inter-subject variability limits effective cross-subject generalization.

By Aymen Sarhane, Fouad Lbakali, Mouad Souissi, Jonathan Lys, Giulia Lioi

fine-tuning benchmarks

arXiv Machine Learning

Aug 4

EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models

arXiv:2601. 17883v3 Announce Type: replace Abstract: Electroencephalography (EEG) foundation models (FMs) have recently emerged as a promising paradigm for brain-computer interfaces, aiming to learn transferable neural representations from large-scale heterogeneous recordings.

By Dingkun Liu, Yuheng Chen, Zhu Chen, Zhenyao Cui, Yaozhi Wen, Jiayu An, Jingwei Luo, Dongrui Wu

fine-tuning benchmarks

arXiv Machine Learning

Aug 4

SingLEM: Single-Channel Large EEG Model

arXiv:2509. 17920v2 Announce Type: replace Abstract: Current deep learning models for electroencephalography (EEG) are often task-specific and depend on large labeled datasets, limiting their adaptability.

By Jamiyan Sukhbaatar, Satoshi Imamura, Ibuki Inoue, Shoya Murakami, Kazi Mahmudul Hassan, Seungwoo Han, Ingon Chanpornpakdi, Toshihisa Tanaka

llms

arXiv Machine Learning

Sep 16

Subject-Specific Analysis of Self-Initiated Attention Shifts from EEG with Controlled Internal and External Attention Conditions

arXiv:2605.18251v2 Announce Type: replace-cross Abstract: Self-initiated attention shifts play a critical role in voluntary behavior but are difficult to study due to the absence of explicit temporal...

By Yuwen Zeng, Dengzhe Hou, Zhang Zhang, Sai Sun, Yongsong Huang, Chia-huei Tseng, Satoshi Shioiri

arXiv AI

Sep 25

Decoding Imagined Speech: A Strictly Subject-Independent Approach Using EEG

This study evaluates imagined speech decoding from EEG using a strictly subject‑independent framework. Two pipelines—time‑domain statistical features and frequency‑domain spectral bandpower—were compared with a random forest classifier; the spectral approach achieved higher trial‑wise accuracy (49.03 % vs. 37.97 %). Feature selection revealed that only a few frequency bands carried most discriminative information, providing a solid baseline for future cross‑subject BCI research.

By Frederik M{\o}llskov Trier, Xiaopeng Mao, Sadasivan Puthusserypady

arXiv Machine Learning

Sep 22

Leakage-Safe Empirical Benchmarking of EEG-Based Machine Learning Pipelines for Dementia Classification

arXiv:2609.22092v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a low-cost and non-invasive signal source for dementia screening, yet existing EEG-based studies remain difficult to...

By Haitian Wang, Chamara Madarasingha, Redowan Mahmud, Aneesh Krishna, Ryu Takechi

computer-vision multimodal benchmarks

arXiv AI

Jun 16

EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models

arXiv:2508. 17742v3 Announce Type: replace-cross Abstract: Electroencephalography foundation models (EEG-FMs) have advanced brain signal analysis, but the lack of standardized evaluation benchmarks impedes model comparison and scientific progress.

By Wei Xiong, Jiangtong Li, Jie Li, Kun Zhu, Changjun Jiang

fine-tuning benchmarks safety

arXiv AI

Jun 16

Learning aligned EEG representations with subject-specific encoders

arXiv:2606. 16462v1 Announce Type: cross Abstract: Cross-subject EEG decoding promises more training data, but it also exposes neural networks to strong inter-subject distribution shifts.

By Bruna J. Lopes, Gabriel Schwartz, Sylvain Chevallier, Raphael Y. de Camargo, Bruno Aristimunha

safety

arXiv Machine Learning

4d ago

Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning

arXiv:2609.36609v1 Announce Type: cross Abstract: Electroencephalography (EEG) analysis requires careful choices in preprocessing, statistical modeling, and machine learning because EEG signals are h...

By Parsa Razmara, Woojae Jeong, Aditya Kommineni, Raymundo Cassani, Richard Leahy, Takfarinas Medani

benchmarks

arXiv Machine Learning

4d ago

Representational and Functional Robustness to Electrode Montages in EEG Foundation Models

arXiv:2609.36288v1 Announce Type: new Abstract: EEG foundation models (EEG-FMs) are intended to generalize across different datasets by learning representations that, ideally, are invariant to datase...

By Jakob Steglich, Justus Meyer zu Bexten, Shakiba Moradi, Laure Ciernik, Simon M. Hofmann, Mina Jamshidi Idaji

rag

arXiv AI

Jun 8

LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling

arXiv:2603. 19100v2 Announce Type: replace Abstract: Electroencephalography (EEG) enables non-invasive monitoring of brain activity across clinical and neurotechnology applications, yet building foundation models for EEG remains challenging due to differing electrode topologies and computational scalability, as Transformer architectures incur quadratic sequence complexity.

By Dana\'e Broustail, Anna Tegon, Thorir Mar Ingolfsson, Yawei Li, Luca Benini

llms rag

arXiv AI

Jul 24

MSBraM: A Multi-scale Self-supervised Brain Foundation Model for Hierarchical EEG Dynamics Learning

arXiv:2607. 21402v1 Announce Type: new Abstract: Self-supervised foundation models have recently shown strong potential for electroencephalogram (EEG)-based analysis.

By Tao Zhou, Jing Han, Lingyu Shu, Zixing Zhang

nlp efficiency benchmarks

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