arXiv AI
Jun 29
What the LLM Should Not Say: Boundary-Aware Context Grounding for A Seven-Channel EEG Agent
arXiv:2606. 26519v2 Announce Type: replace Abstract: Large language models (LLMs) can make scientific software easier to use.
Boundary-Aware Context Grounding for A Low-Channel EEG Agent
arXiv:2606. 26519v1 Announce Type: new Abstract: Large language models (LLMs) can make scientific software easier to use.
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arXiv AI
Aug 6
BrainBench: Benchmarking Large Language Models for Comprehensive EEG Understanding
arXiv:2608. 04156v1 Announce Type: new Abstract: Electroencephalography (EEG) analysis extends beyond assigning predefined labels to recordings; it requires workflows connecting natural-language instructions, signal processing, quantitative evidence, and scientific interpretation.
arXiv AI
Jun 26
NeuraDock Visual Cognitive Load Agent Tutorial: A Quality-Gated Open-Source EEG Workflow for Alpha Dynamics and Real-Time Applications
arXiv:2606. 26518v1 Announce Type: new Abstract: This tutorial paper provides a step-by-step, reproducible walkthrough of NeuraDock Agent, an open-source EEG agent focused on Alpha dynamics and visual cognitive-load analysis.
Hugging Face Trending Papers
Jun 25
NeuraDock Visual Cognitive Load Agent Tutorial: A Quality-Gated Open-Source EEG Workflow for Alpha Dynamics and Real-Time Applications
This tutorial paper provides a step-by-step, reproducible walkthrough of NeuraDock Agent, an open-source EEG agent focused on Alpha dynamics and visual cognitive-load analysis. The goal is practical: a reader should be able to install the agent, run EEG preprocessing and quality control, generate Alpha dynamics figures, perform within-subject Rest/Task visual cognitive-load comparison, run the public mini-dataset analyses and compare them with the reference validation summary, start an online dashboard, call the real-time API from an external application, and use the LLM interpretation layer to explain quality risks.
arXiv AI
Jul 7
EEG-SpikeAgent: Agentic Closed-Loop Program Synthesis for Automated EEG Spike Detection
arXiv:2607. 04558v1 Announce Type: cross Abstract: Automated detection of interictal epileptiform discharges in scalp electroencephalography (EEG) is clinically important, but recent high-performing deep-learning models often trade interpretability for accuracy.
Hugging Face Trending Papers
Jul 6
EEG-SpikeAgent: Agentic Closed-Loop Program Synthesis for Automated EEG Spike Detection
Automated detection of interictal epileptiform discharges in scalp electroencephalography (EEG) is clinically important, but recent high-performing deep-learning models often trade interpretability for accuracy. We introduce EEG-SpikeAgent, a closed-loop program-synthesis framework that uses a large language model (LLM) agentic system to generate signal-processing features for spike detection in scalp EEG.
arXiv AI
Jul 29
CogEEGAgent: Toward Autonomous Cognitive EEG Analysis with Grounded Execution and Selection-Aware Verification
arXiv:2607. 25045v1 Announce Type: new Abstract: Electroencephalography (EEG) analysis in cognitive studies requires specialized expertise and involves many defensible choices over contrasts, channels, time windows, and statistical tests.
arXiv Machine Learning
Jul 22
Is EEG-to-Text Feasible in Real-World Scenarios? An In-Depth Analysis Using a Neuropsychology-Inspired Benchmark
arXiv:2607. 18749v1 Announce Type: new Abstract: Translating brain signals into text could restore communication for people with severe paralysis, yet practically usable systems to date rely on invasive electrocorticography (ECoG).
arXiv Machine Learning
Jun 2
OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models
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 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.
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.
arXiv Machine Learning
2d ago
NeurDuo-EEG: A Long-Sequence EEG Foundation Model with Persistent State and Explicit Memory
NeurDuo-EEG is a causal EEG foundation model that introduces channel‑resolved persistent memory and multi‑timescale memory management, allowing it to model continuous EEG with a fixed‑size state. Trained on 3,955 hours of EEG from 17 public datasets, it outperforms baselines on most short‑window and long‑sequence tasks, notably improving seizure detection AUC‑PR from 0.285 to 0.471. The Small variant achieves these gains with only 4.7 M parameters and supports efficient streaming inference with constant per‑chunk latency even as history grows to one hour.