Electroencephalography
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Related Articles from SNS
EEG-Based Multimodal Learning via Hyperbolic Mixture-of-Curvature Experts
arXiv:2604.12579v3 Announce Type: replace Abstract: Electroencephalography (EEG)-based multimodal learning integrates brain signals with complementary modalities to improve mental state assessment, providing great clinical potential. The effectiveness of such paradigms largely depends on the representation learning on heterogeneous modalities. For EEG-based paradigms, one promising approach is to leverage their hierarchical structures, as recent studies have shown that both EEG and...
Quilting the Brain: Whole-Brain iEEG Reconstruction via Incomplete Observation Linear Mixed Models
Mapping human brain function at high spatiotemporal resolution is constrained by the physical limitations of non-invasive imaging and the sparse sampling of invasive electrophysiology. While intracranial electroencephalography (iEEG) captures local field potentials with millimeter precision, clinical implantation strategies result in a ``coverage paradox'': observations are restricted to disjoint, patient-specific patches, leaving most of the cortex unobserved. This study introduces the...
SleepExplain: Explainable Non-Rapid Eye Movement and Rapid Eye Movement Sleep Stage Classification from EEG Signal
arXiv:2606.07351v1 Announce Type: new Abstract: Classification of sleep stages is one of the most important diagnostic approaches for a variety of sleep-related disorders. Electroencephalography (EEG) is regarded as a powerful tool for examining the association between neurological effects and sleep phases since it correctly identifies sleep-related neurological alterations. During Non-Rapid Eye Movement (NREM) and Rapid Eye Movement (REM) sleep phases, a number of nerve and bodily functions...
Brain-CLIPLM: Semantic Compression for EEG-to-Text Decoding
Announce Type: replace Abstract: Decoding natural language from non-invasive electroencephalography (EEG) remains constrained by low signal-to-noise ratio and limited information bandwidth. This raises a central question: can sentence-level language be reliably recovered from such signals? Under realistic information constraints, this direct-recovery assumption may be too strong.
Flexible neural encoding predicts the comprehension of degraded speech
How listeners track a variable and continuous acoustic speech signal and parse it into meaningful linguistic representations is a question central to auditory neuroscience. Moreover, the resilience of this process to acoustic signal degradation is not fully understood. The current study consists of a listening task wherein participants (n = 38) were presented with a naturalistic story whilst undergoing continuous electroencephalography (EEG).
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. Although EEG is widely used for workload assessment, the consistency of region-level EEG contributions across tasks, datasets, and subjects remains unclear. This paper presents a region-level evaluation framework for EEG-based workload prediction in which models are trained and evaluated using...
A Methodological Framework for Explicit Control of the Speed-Accuracy Trade-off in Brain-Computer Interfaces
Announce Type: cross Abstract: Brain-computer interfaces (BCIs) are limited by low signal-to-noise ratio in modalities such as electroencephalography, which requires multiple trials to reliably decode user intentions. This induces a speed-accuracy trade-off, whereby higher accuracy comes at the cost of speed. The speed-accuracy balance is application-dependent, motivating controllable trade-offs.
Invasive and Non-Invasive Neural Decoding of Motor Performance in Parkinson's Disease for Personalized Deep Brain Stimulation
arXiv:2603.27750v2 Announce Type: replace Abstract: Decoding motor performance from brain signals offers promising avenues for adaptive deep brain stimulation (aDBS) for Parkinson's disease (PD). In a two-center cohort of 19 PD patients executing a drawing task, we decoded motor performance from electroencephalography (n=15) and, critically for clinical translation, electrocorticography (n=4). Within each session, patients performed the task under DBS on and DBS off.
EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks
Announce Type: replace Abstract: Electroencephalography (EEG) is the cornerstone of non-invasive brain-computer interfaces (BCIs), yet conventional decoding relies on fragmented, task-specific architectures that severely limit cross-task scalability. While EEG foundation models pre-trained on massive corpora promise universal brain decoding, current post-training depends on task-isolated fine-tuning. This static paradigm restricts knowledge transfer across heterogeneous tasks, hinders model...
EEGDancer: Dynamic Emotion Latent Space Masked Modeling with Reinforcement Learning for EEG Continuous Emotion Prediction
arXiv:2606.05855v1 Announce Type: new Abstract: Continuous electroencephalography (EEG) emotion prediction aims to model the temporal evolution of human emotional states from EEG signals. Unlike conventional discrete emotion recognition, continuous prediction requires capturing long-range temporal dependencies and coherent emotional dynamics.