DistilCLIP-EEG: Enhancing Epileptic Seizure Detection Through Multi-modal Learning and Knowledge Distillation

• URL: http://arxiv.org/abs/2510.13497v1
• Date: Wed, 15 Oct 2025 12:49:28 GMT
• Title: DistilCLIP-EEG: Enhancing Epileptic Seizure Detection Through Multi-modal Learning and Knowledge Distillation
• Authors: Zexin Wang, Lin Shi, Haoyu Wu, Junru Luo, Xiangzeng Kong, Jun Qi,
• Abstract summary: 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.
• Score: 6.879999526049009
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Epilepsy is a prevalent neurological disorder marked by sudden, brief episodes of excessive neuronal activity caused by abnormal electrical discharges, which may lead to some mental disorders. Most existing deep learning methods for epilepsy detection rely solely on unimodal EEG signals, neglecting the potential benefits of multimodal information. To address this, we propose a novel multimodal model, DistilCLIP-EEG, based on the CLIP framework, which integrates both EEG signals and text descriptions to capture comprehensive features of epileptic seizures. The model involves an EEG encoder based on the Conformer architecture as a text encoder, the proposed Learnable BERT (BERT-LP) as prompt learning within the encoders. Both operate in a shared latent space for effective cross-modal representation learning. To enhance efficiency and adaptability, we introduce a knowledge distillation method where the trained DistilCLIP-EEG serves as a teacher to guide a more compact student model to reduce training complexity and time. On the TUSZ, AUBMC, and CHB-MIT datasets, both the teacher and student models achieved accuracy rates exceeding 97%. Across all datasets, the F1-scores were consistently above 0.94, demonstrating the robustness and reliability of the proposed framework. Moreover, the student model's parameter count and model size are approximately 58.1% of those of the teacher model, significantly reducing model complexity and storage requirements while maintaining high performance. These results highlight the potential of our proposed model for EEG-based epilepsy detection and establish a solid foundation for deploying lightweight models in resource-constrained settings.

Related papers

• 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)
• AdvKT: An Adversarial Multi-Step Training Framework for Knowledge Tracing [64.79967583649407]
  Knowledge Tracing (KT) monitors students' knowledge states and simulates their responses to question sequences.<n>Existing KT models typically follow a single-step training paradigm, which leads to significant error accumulation.<n>We propose a novel Adversarial Multi-Step Training Framework for Knowledge Tracing (AdvKT) which focuses on the multi-step KT task.
  arXiv  Detail & Related papers  (2025-04-07T03:31:57Z)
• BioSerenity-E1: a self-supervised EEG model for medical applications [0.0]
  BioSerenity-E1 is a family of self-supervised foundation models for clinical EEG applications.<n>It combines spectral tokenization with masked prediction to achieve state-of-the-art performance across relevant diagnostic tasks.
  arXiv  Detail & Related papers  (2025-03-13T13:42:46Z)
• 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)
• CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention [53.539020807256904]
  We introduce a Compact for Representations of Brain Oscillations using alternating attention (CEReBrO)<n>Our tokenization scheme represents EEG signals at a per-channel patch.<n>We propose an alternating attention mechanism that jointly models intra-channel temporal dynamics and inter-channel spatial correlations, achieving 2x speed improvement with 6x less memory required compared to standard self-attention.
  arXiv  Detail & Related papers  (2025-01-18T21:44:38Z)
• CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal Information [61.1904164368732]
  We propose CognitionCapturer, a unified framework that fully leverages multimodal data to represent EEG signals.<n>Specifically, CognitionCapturer trains Modality Experts for each modality to extract cross-modal information from the EEG modality.<n>The framework does not require any fine-tuning of the generative models and can be extended to incorporate more modalities.
  arXiv  Detail & Related papers  (2024-12-13T16:27:54Z)
• Knowledge Distillation for Adaptive MRI Prostate Segmentation Based on Limit-Trained Multi-Teacher Models [4.711401719735324]
  Knowledge Distillation (KD) has been proposed as a compression method and an acceleration technology. KD is an efficient learning strategy that can transfer knowledge from a burdensome model to a lightweight model. We develop a KD-based deep model for prostate MRI segmentation in this work by combining features-based distillation with Kullback-Leibler divergence, Lovasz, and Dice losses.
  arXiv  Detail & Related papers  (2023-03-16T17:15:08Z)
• Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation [65.62538699160085]
  We propose a Directed Acyclic Graph Factorization Machine (KD-DAGFM) to learn the high-order feature interactions from existing complex interaction models for CTR prediction via Knowledge Distillation. KD-DAGFM achieves the best performance with less than 21.5% FLOPs of the state-of-the-art method on both online and offline experiments.
  arXiv  Detail & Related papers  (2022-11-21T03:09:42Z)
• SSD-KD: A Self-supervised Diverse Knowledge Distillation Method for Lightweight Skin Lesion Classification Using Dermoscopic Images [62.60956024215873]
  Skin cancer is one of the most common types of malignancy, affecting a large population and causing a heavy economic burden worldwide. Most studies in skin cancer detection keep pursuing high prediction accuracies without considering the limitation of computing resources on portable devices. This study specifically proposes a novel method, termed SSD-KD, that unifies diverse knowledge into a generic KD framework for skin diseases classification.
  arXiv  Detail & Related papers  (2022-03-22T06:54:29Z)
• A Novel TSK Fuzzy System Incorporating Multi-view Collaborative Transfer Learning for Personalized Epileptic EEG Detection [20.11589208667256]
  We propose a TSK fuzzy system-based epilepsy detection algorithm that integrates multi-view collaborative transfer learning. The proposed method has the potential to detect epileptic EEG signals effectively.
  arXiv  Detail & Related papers  (2021-11-11T12:15:55Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• 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.