arXiv:2605. 14883v2 Announce Type: replace-cross Abstract: Mild traumatic brain injury (mTBI) is a prevalent condition that remains difficult to diagnose in its early stages.
arXiv:2607. 23554v1 Announce Type: cross Abstract: In this paper, we propose the MAEConformer, a novel self-supervised learning framework that combines the Conformer architecture with the Masked Autoencoder (MAE) paradigm for large-scale representation learning from unlabelled electroencephalography (EEG) and heart rate variability (HRV) signals.
arXiv:2607. 21384v1 Announce Type: new Abstract: Electroencephalography (EEG) models used for epilepsy are often limited to specific datasets and tasks.
The paper presents a data‑driven framework that selects optimal EEG‑EMG channel pairs for hybrid brain‑computer interfaces by formulating the problem as a constrained bi‑objective optimisation. It maximises both the spatial relevance of EEG channels to motor cortex areas and the corticomuscular coupling strength, solved with NSGA‑II. Applied to motor‑imagery data from eight stroke patients, the method achieved an average classification accuracy of 89.6%, indicating improved capture of physiologically meaningful interactions.
arXiv:2606. 25177v1 Announce Type: new Abstract: Cognitive workload monitoring is important for adaptive rehabilitation and assistive interfaces, where task difficulty, pacing, and feedback should be adjusted according to the user's cognitive state to avoid overload and under-challenge.
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...
NeuroStrata is a deep learning framework that analyzes mental stress by modeling the temporal evolution of frequency‑specific directed connectivity in EEG signals using Time‑Varying Partial Directed Coherence (TV‑PDC). It transforms TV‑PDC connectivity maps into deep embeddings with pretrained CNNs and Vision Transformers, then classifies them with lightweight machine learning models. Experiments on the SAM 40 dataset show that beta‑band connectivity yields the highest accuracy (97.3 %) with a ViT backbone, while alpha‑band connectivity remains consistently stable, and that stress‑related connectivity signatures consolidate in mid‑to‑late temporal windows.
arXiv:2608. 15999v1 Announce Type: new Abstract: Automatic emotion assessment can benefit from combining neural and behavioral signals, but many multimodal approaches rely on separate, modality-specific feature-extraction pipelines before fusion.
arXiv:2506.20354v3 Announce Type: replace-cross Abstract: Learning from multi-variate time-series with heterogeneous channel configurations remains a fundamental challenge for deep neural networks, p...
arXiv:2607. 24126v1 Announce Type: cross Abstract: Brain-machine interfaces provide a link between neural activity and external devices, enabling restoration of motor function and advancing human-machine interaction using non-invasive electroencephalography (EEG).
arXiv:2609.36087v1 Announce Type: cross Abstract: Clinicians and neuroscientists have long analyzed intracranial electroencephalography (iEEG) through directly measurable physiological characteristic...
arXiv:2605. 14941v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG) signals are highly susceptible to artifacts, resulting in a low signal-to-noise ratio, which makes extraction of meaningful neural information challenging.
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.