Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment

• URL: http://arxiv.org/abs/2508.07283v1
• Date: Sun, 10 Aug 2025 10:43:09 GMT
• Title: Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment
• Authors: Bujar Raufi,
• Abstract summary: This study explores the intersection of electroencephalography (EEG) microstates and Large Language Models (LLMs)<n>The research aims to fine-tune LLMs for improved predictions of distinct cognitive states, specifically 'Rest' and 'Load'
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This study explores the intersection of electroencephalography (EEG) microstates and Large Language Models (LLMs) to enhance the assessment of cognitive load states. By utilizing EEG microstate features, the research aims to fine-tune LLMs for improved predictions of distinct cognitive states, specifically 'Rest' and 'Load'. The experimental design is delineated in four comprehensive stages: dataset collection and preprocessing, microstate segmentation and EEG backfitting, feature extraction paired with prompt engineering, and meticulous LLM model selection and refinement. Employing a supervised learning paradigm, the LLM is trained to identify cognitive load states based on EEG microstate features integrated into prompts, producing accurate discrimination of cognitive load. A curated dataset, linking EEG features to specified cognitive load conditions, underpins the experimental framework. The results indicate a significant improvement in model performance following the proposed fine-tuning, showcasing the potential of EEG-informed LLMs in cognitive neuroscience and cognitive AI applications. This approach not only contributes to the understanding of brain dynamics but also paves the way for advancements in machine learning techniques applicable to cognitive load and cognitive AI research.

Related papers

• E^2-LLM: Bridging Neural Signals and Interpretable Affective Analysis [54.763420895859035]
  We present ELLM2-EEG-to-Emotion Large Language Model, first MLLM framework for interpretable emotion analysis from EEG.<n>ELLM integrates a pretrained EEG encoder with Q-based LLMs through learnable projection layers, employing a multi-stage training pipeline.<n>Experiments on the dataset across seven emotion categories demonstrate that ELLM2-EEG-to-Emotion Large Language Model achieves excellent performance on emotion classification.
  arXiv  Detail & Related papers  (2026-01-11T13:21:20Z)
• A Closed-Loop Personalized Learning Agent Integrating Neural Cognitive Diagnosis, Bounded-Ability Adaptive Testing, and LLM-Driven Feedback [5.190121417265426]
  This paper presents an end-to-end personalized learning agent, which integrates a Neural Cognitive Diagnosis model (NCD), a Bounded-Ability Computerized Adaptive Testing strategy (BECAT), and large language models (LLMs)<n> Experiments on the ASSISTments dataset show that the NCD module achieves strong performance on response prediction while yielding interpretable mastery assessments.<n>Overall, the results indicate that the proposed design is effective and practically deployable.
  arXiv  Detail & Related papers  (2025-10-26T07:32:31Z)
• DistilCLIP-EEG: Enhancing Epileptic Seizure Detection Through Multi-modal Learning and Knowledge Distillation [6.879999526049009]
  Most existing deep learning methods for epilepsy detection rely solely on unimodal EEG signals.<n>We propose a novel multimodal model, DistilCLIP-EEG, which integrates both EEG signals and text descriptions to capture comprehensive features of epileptic seizures.
  arXiv  Detail & Related papers  (2025-10-15T12:49:28Z)
• WaveMind: Towards a Conversational EEG Foundation Model Aligned to Textual and Visual Modalities [55.00677513249723]
  EEG signals simultaneously encode both cognitive processes and intrinsic neural states.<n>We map EEG signals and their corresponding modalities into a unified semantic space to achieve generalized interpretation.<n>The resulting model demonstrates robust classification accuracy while supporting flexible, open-ended conversations.
  arXiv  Detail & Related papers  (2025-09-26T06:21:51Z)
• A brain-inspired generative model for EEG-based cognitive state identification [6.187646941506353]
  This article proposes a brain-inspired generative model that merges an impulsive-attention neural network and a variational autoencoder.<n>A hybrid learning method is presented for training the model by integrating gradient-based learning and heteroassociative memory.<n> Experimental results show that the BIG model achieves a classification accuracy above 89%, comparable with state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-05-03T04:29:27Z)
• Large Cognition Model: Towards Pretrained EEG Foundation Model [0.0]
  We propose a transformer-based foundation model designed to generalize across diverse EEG datasets and downstream tasks.<n>Our findings highlight the potential of pretrained EEG foundation models to accelerate advancements in neuroscience, personalized medicine, and BCI technology.
  arXiv  Detail & Related papers  (2025-02-11T04:28:10Z)
• Comparative Analysis of Deep Learning Approaches for Harmful Brain Activity Detection Using EEG [7.766736511616383]
  classification of harmful brain activities, such as seizures and periodic discharges, play a vital role in neurocritical care.<n>EEG provides a non-invasive method for monitoring brain activity, but the manual interpretation of EEG signals are time-consuming and rely heavily on expert judgment.<n>This study presents a comparative analysis of deep learning architectures, including CNNs, Vision Transformers (ViTs), and EEGNet, applied to the classification of harmful brain activities.
  arXiv  Detail & Related papers  (2024-12-10T19:27:19Z)
• Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval [61.70489848327436]
  KARE is a novel framework that integrates knowledge graph (KG) community-level retrieval with large language models (LLMs) reasoning.<n>Extensive experiments demonstrate that KARE outperforms leading models by up to 10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and readmission predictions.
  arXiv  Detail & Related papers  (2024-10-06T18:46:28Z)
• Feature Estimation of Global Language Processing in EEG Using Attention Maps [5.173821279121835]
  This study introduces a novel approach to EEG feature estimation that utilizes the weights of deep learning models to explore this association. We demonstrate that attention maps generated from Vision Transformers and EEGNet effectively identify features that align with findings from prior studies. The application of Mel-Spectrogram with ViTs enhances the resolution of temporal and frequency-related EEG characteristics.
  arXiv  Detail & Related papers  (2024-09-27T22:52:31Z)
• Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making [51.737762570776006]
  LLM-ACTR is a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making. Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations. Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability.
  arXiv  Detail & Related papers  (2024-08-17T11:49:53Z)
• Exploring the Cognitive Knowledge Structure of Large Language Models: An Educational Diagnostic Assessment Approach [50.125704610228254]
  Large Language Models (LLMs) have not only exhibited exceptional performance across various tasks, but also demonstrated sparks of intelligence. Recent studies have focused on assessing their capabilities on human exams and revealed their impressive competence in different domains. We conduct an evaluation using MoocRadar, a meticulously annotated human test dataset based on Bloom taxonomy.
  arXiv  Detail & Related papers  (2023-10-12T09:55:45Z)
• A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation [48.85731427874065]
  This paper proposes a knowledge-driven cross-view contrastive learning framework (KDC2) to extract effective representations from EEG with limited labels. The KDC2 method creates scalp and neural views of EEG signals, simulating the internal and external representation of brain activity. By modeling prior neural knowledge based on neural information consistency theory, the proposed method extracts invariant and complementary neural knowledge to generate combined representations.
  arXiv  Detail & Related papers  (2023-09-21T08:53:51Z)
• EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning [13.404503606887715]
  We present a new solution for the classification of cognitive load using electroencephalogram (EEG) We pre-train our model using self-supervised masked autoencoding on emotion-related EEG datasets. The results of our experiments show that our proposed approach achieves strong results and outperforms conventional single-stage fully supervised learning.
  arXiv  Detail & Related papers  (2023-08-01T02:59:19Z)
• Uncovering the structure of clinical EEG signals with self-supervised learning [64.4754948595556]
  Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG) By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
  arXiv  Detail & Related papers  (2020-07-31T14:34:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.