arXiv Machine Learning August 19, 2026 By Philipp Bomatter, Henry Gouk

OOD Detection for EEG-based Machine Learning in High-Risk Environments

Read the original on arXiv Machine Learning →

arXiv:2608. 17620v1 Announce Type: new Abstract: Machine learning models for electroencephalography (EEG) analysis show great promise across a wide range of applications, but their deployment in high-risk domains is hindered by their vulnerability to distribution shifts.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 24

Backpropagation-Free Test-Time Adaptation for Lightweight EEG-Based Brain-Computer Interfaces

arXiv:2601. 07556v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability, signal non-stationarity, and computational constraints.

By Siyang Li, Jiayi Ouyang, Zhenyao Cui, Ziwei Wang, Tianwang Jia, Feng Wan, Dongrui Wu

safety

arXiv AI

Jul 7

Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts

arXiv:2604. 16926v2 Announce Type: replace-cross Abstract: Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet their clinical deployment is hindered by distribution shifts across clinical settings, devices, and populations.

By Gabriel Jason Lee, Jathurshan Pradeepkumar, Jimeng Sun

benchmarks safety

arXiv Machine Learning

Jun 3

Making Brain-Computer Interfaces More Secure

arXiv:2606. 02597v1 Announce Type: new Abstract: The development of brain-computer interfaces (BCIs) based on electroencephalograms (EEGs) has advanced significantly mainly to machine learning.

By Md Fahimul Kabir Chowdhury, Gahangir Hossain

safety

arXiv Machine Learning

Jun 25

NeuroShield: A Device-Agnostic Foundation Model for EEG Authentication

arXiv:2606. 20673v2 Announce Type: replace Abstract: A central challenge in EEG authentication is that models are typically tied to the acquisition settings in which they are trained.

By Matin Fallahi, Patricia Arias-Cabarcos, Thorsten Strufe

llms rag fine-tuning

arXiv Machine Learning

Jun 3

Assessing Region-Level EEG Contributions to Cognitive Workload Prediction

arXiv:2606. 02598v1 Announce Type: new Abstract: Accurate and generalizable estimation of cognitive workload from electroencephalography (EEG) is critical for human-centered and safety-critical systems.

By Jacob Wong, Sohan Singh, Prannaya Gupta, Jin Xing Ang, Kritika Johari, U-Xuan Tan

safety

arXiv AI

Jun 4

ADAPTOOD: Uncertainty-Aware Fine-Tuning for Out-of-Distribution ECG Time Series Models

arXiv:2606. 04164v1 Announce Type: cross Abstract: Data samples used for training often differ from those encountered during fine-tuning and deployment, and while ML models show promise, their performance remains limited when only small annotated datasets are available.

By Sotirios Vavaroutas, Yu Yvonne Wu, Ali Etemad, Cecilia Mascolo

fine-tuning

Related stories

Backpropagation-Free Test-Time Adaptation for Lightweight EEG-Based Brain-Computer Interfaces

Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts

Making Brain-Computer Interfaces More Secure

NeuroShield: A Device-Agnostic Foundation Model for EEG Authentication

Assessing Region-Level EEG Contributions to Cognitive Workload Prediction

ADAPTOOD: Uncertainty-Aware Fine-Tuning for Out-of-Distribution ECG Time Series Models