REVIEW 8 cited by
EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
In conventional machine learning (ML) approaches applied to electroencephalography (EEG), this is often a limited focus, isolating specific brain activities occurring across disparate temporal scales (from transient spikes in milliseconds to seizures lasting minutes) and spatial scales (from localized high-frequency oscillations to global sleep activity). This siloed approach limits the development EEG ML models that exhibit multi-scale electrophysiological understanding and classification capabilities. Moreover, typical ML EEG approaches utilize black-box approaches, limiting their interpretability and trustworthiness in clinical contexts. Thus, we propose EEG-GPT, a unifying approach to EEG classification that leverages advances in large language models (LLM). EEG-GPT achieves excellent performance comparable to current state-of-the-art deep learning methods in classifying normal from abnormal EEG in a few-shot learning paradigm utilizing only 2% of training data. Furthermore, it offers the distinct advantages of providing intermediate reasoning steps and coordinating specialist EEG tools across multiple scales in its operation, offering transparent and interpretable step-by-step verification, thereby promoting trustworthiness in clinical contexts.
Forward citations
Cited by 8 Pith papers
-
EasyBCI Agent: Towards Universal Neural Data Preprocessing for Brain-Computer Interfaces
Domain-specific LLM orchestration preserves more task-relevant EEG signal than a manual pipeline or general-purpose coding agents on one small private dataset, and extends to five other modalities.
-
EasyBCI Agent: Towards Universal Neural Data Preprocessing for Brain-Computer Interfaces
An LLM agent with a text-only data fingerprint, quality-gated skill reuse, and expert checkpoints outperforms manual and general-purpose coding pipelines for EEG preprocessing and produces QC-passing pipelines for fiv...
-
Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs
Generative Visual Grounding creates visual proxy images from EEG to enhance MLLM understanding of brain signals beyond text-only alignment.
-
Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs
Generative Visual Grounding creates instance-specific visual proxy images from EEG signals to enhance MLLM understanding of brain activity beyond text-only alignment.
-
LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis
LLM-based refinement of edges in transformer-constructed EEG graphs improves seizure detection accuracy and produces cleaner, more interpretable structures on the TUSZ dataset.
-
Foundation Models Defining A New Era In Sensor-based Human Activity Recognition: A Survey And Outlook
The survey organizes foundation models for sensor-based HAR into a lifecycle taxonomy and identifies three trajectories: HAR-specific models from scratch, adaptation of general time-series models, and integration with...
-
WorldWeaver: Generating Long-Horizon Video Worlds via Rich Perception
WorldWeaver reduces temporal drift in long-horizon video generation by jointly modeling RGB and depth perceptual conditions with segmented noise scheduling.
-
Foundation Models for Cross-Domain EEG Analysis Application: A Survey
A survey that organizes EEG foundation-model research into five output-modality categories: native EEG, text, vision, audio, and multimodal fusion, with a claim to be the first such comprehensive taxonomy.
Discussion (0). Sign in to comment.