The article reviews how inter- and intra-subject variability in EEG affects reliability and reproducibility across resting-state, ERP, and task-related/BCI paradigms. It finds that inter-subject differences are generally larger than within-subject fluctuations, with stability varying by feature—alpha-band measures are more reliable than higher-frequency or connectivity metrics, and P300 components show moderate-to-good reliability. The review catalogs biological, state-related, technical, and analytical sources of variability, discusses quantification methods such as ICC, CV, SNR, and generalizability theory, and offers design and reporting recommendations to manage and leverage variability for precision neuroscience and robust neurotechnology.
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...
NeuroAtlas is the largest EEG benchmark to date, comprising 42 datasets and 260,000 hours of clinical EEG data across epilepsy, sleep medicine, brain age estimation, and brain‑computer interfaces. The study evaluates foundation models (FMs) for EEG against supervised baselines and generic time‑series FMs, finding that EEG‑specific FMs do not consistently outperform generic ones. It also demonstrates that standard machine‑learning metrics are inadequate for clinical relevance, advocating for task‑specific measures such as event‑level decision quality, hypnogram features, and brain‑age gap.
EEG-HealthLLM introduces a hierarchical adaptation framework that unifies heterogeneous EEG health assessments using large language models. Task‑specific EEG adapters preserve unique spatio‑temporal dynamics, while a shared LoRA module in the LLM backbone captures common high‑level assessment semantics. The new EEG‑HealthBench benchmark reorganizes five EEG datasets into five health‑assessment tasks, and experiments show EEG‑HealthLLM achieves the highest average balanced accuracy and macro‑F1, improving performance while reducing adapter size by 80%.
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.
EEG-Xplain introduces a unified attribution framework to interpret EEG foundation models such as BIOT, LaBraM, and EEGMamba. The framework combines gradient, perturbation, and activation-based methods to analyze model behavior across spatial, temporal, and frequency dimensions, identifying critical channels, decision-relevant signal segments, and contributions of canonical EEG rhythms. It evaluates explanation reliability with population-level metrics and uses large language models to convert structured attributions into natural-language reports, demonstrating consistency with known neurophysiological markers on benchmark datasets.
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.
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.
arXiv:2608.24597v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a widely used window into human brain function, but most EEG models remain tied to a one-dataset-one-model supervised...
arXiv:2607. 02670v1 Announce Type: new Abstract: Electroencephalography (EEG) offers a noninvasive approach for examining neurophysiological correlates of dimensional psychopathology, yet systematic evidence across EEG paradigms and feature granularities remains limited.
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.
arXiv:2608. 13676v1 Announce Type: new Abstract: Objective: Foundation models represent the next advancement in AI for EEG analysis; however current explainable AI techniques provide attribution scores in the time-channel input space, which is mismatched to clinical intuition about EEG.
The paper investigates how EEG embeddings from signal processing and foundation models encode trait and state information. Using a hierarchy metric and kNN purity on a BCI dataset, it finds that most embeddings form a macro‑trait hierarchy with micro‑states inside, which hampers state discrimination. Normalizing embeddings reduces trait leakage and improves state classification accuracy by about 10%, and fine‑tuning shifts the hierarchy toward macro‑state organization, enhancing decoding performance.