Enhancing Low-Density EEG-Based Brain-Computer Interfaces with Similarity-Keeping Knowledge Distillation

• URL: http://arxiv.org/abs/2212.03329v1
• Date: Tue, 6 Dec 2022 21:22:02 GMT
• Title: Enhancing Low-Density EEG-Based Brain-Computer Interfaces with Similarity-Keeping Knowledge Distillation
• Authors: Xin-Yao Huang, Sung-Yu Chen, Chun-Shu Wei
• Abstract summary: Loss of EEG decoding performance is often inevitable due to reduced number of electrodes and coverage of scalp regions of a low-density EEG montage. We introduce knowledge distillation (KD), a learning mechanism developed for transferring knowledge/information between neural network models. Our framework consistently improves motor-imagery EEG decoding accuracy when number of electrodes deceases for the input EEG data.
• Score: 0.966840768820136
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Electroencephalogram (EEG) has been one of the common neuromonitoring modalities for real-world brain-computer interfaces (BCIs) because of its non-invasiveness, low cost, and high temporal resolution. Recently, light-weight and portable EEG wearable devices based on low-density montages have increased the convenience and usability of BCI applications. However, loss of EEG decoding performance is often inevitable due to reduced number of electrodes and coverage of scalp regions of a low-density EEG montage. To address this issue, we introduce knowledge distillation (KD), a learning mechanism developed for transferring knowledge/information between neural network models, to enhance the performance of low-density EEG decoding. Our framework includes a newly proposed similarity-keeping (SK) teacher-student KD scheme that encourages a low-density EEG student model to acquire the inter-sample similarity as in a pre-trained teacher model trained on high-density EEG data. The experimental results validate that our SK-KD framework consistently improves motor-imagery EEG decoding accuracy when number of electrodes deceases for the input EEG data. For both common low-density headphone-like and headband-like montages, our method outperforms state-of-the-art KD methods across various EEG decoding model architectures. As the first KD scheme developed for enhancing EEG decoding, we foresee the proposed SK-KD framework to facilitate the practicality of low-density EEG-based BCI in real-world applications.

Related papers

• EEG_GLT-Net: Optimising EEG Graphs for Real-time Motor Imagery Signals Classification [7.9035081192335115]
  Graph Neural Networks (GCN) have been increasingly applied for classifying EEG Motor Imagery signals. Recent advances in real-time EEG MI signal classification utilised Pearson Coefficient Correlation (PCC) for constructing adjacency matrices. This paper introduces the EEG Graph Lottery Ticket (EEG_GLT) algorithm, an innovative technique for constructing adjacency matrices for EEG channels.
  arXiv  Detail & Related papers  (2024-04-17T05:16:12Z)
• hvEEGNet: exploiting hierarchical VAEs on EEG data for neuroscience applications [3.031375888004876]
  Two main issues challenge the existing DL-based modeling methods for EEG. High variability between subjects and low signal-to-noise ratio make it difficult to ensure a good quality in the EEG data. We propose two variational autoencoder models, namely vEEGNet-ver3 and hvEEGNet, to target the problem of high-fidelity EEG reconstruction.
  arXiv  Detail & Related papers  (2023-11-20T15:36:31Z)
• vEEGNet: learning latent representations to reconstruct EEG raw data via variational autoencoders [3.031375888004876]
  We propose vEEGNet, a DL architecture with two modules, i.e., an unsupervised module based on variational autoencoders to extract a latent representation of the data, and a supervised module based on a feed-forward neural network to classify different movements. We show state-of-the-art classification performance, and the ability to reconstruct both low-frequency and middle-range components of the raw EEG.
  arXiv  Detail & Related papers  (2023-11-16T19:24:40Z)
• EKGNet: A 10.96{\mu}W Fully Analog Neural Network for Intra-Patient Arrhythmia Classification [79.7946379395238]
  We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification. We propose EKGNet, a hardware-efficient and fully analog arrhythmia classification architecture that archives high accuracy with low power consumption.
  arXiv  Detail & Related papers  (2023-10-24T02:37:49Z)
• 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)
• DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection [49.196182908826565]
  Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input.
  arXiv  Detail & Related papers  (2023-09-07T13:43:46Z)
• EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG [0.0]
  One of the main challenges in electroencephalogram (EEG) based brain-computer interface (BCI) systems is learning the subject/session invariant features to classify cognitive activities. We propose a novel end-to-end machine learning pipeline, EEG-NeXt, which facilitates transfer learning.
  arXiv  Detail & Related papers  (2022-12-08T10:15:52Z)
• MAtt: A Manifold Attention Network for EEG Decoding [0.966840768820136]
  We propose a novel geometric learning (GDL)-based model for EEG decoding, featuring a manifold attention network (mAtt) The evaluation of MAtt on both time-synchronous and -asyncronous EEG datasets suggests its superiority over other leading DL methods for general EEG decoding.
  arXiv  Detail & Related papers  (2022-10-05T02:26:31Z)
• EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification [123.93460670568554]
  This paper proposes a novel convolutional neural network (CNN) architecture for accurate and robust EEG-based motor imagery (MI) classification. The proposed CNN model, namely EEG-Inception, is built on the backbone of the Inception-Time network. The proposed network is an end-to-end classification, as it takes the raw EEG signals as the input and does not require complex EEG signal-preprocessing.
  arXiv  Detail & Related papers  (2021-01-24T19:03:10Z)
• ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks [69.25956542388653]
  Deep learning (DL) algorithms are gaining weight in academic and industrial settings. We demonstrate DL can be successfully applied to low interpretative tasks by embedding ECG detection and delineation onto a segmentation framework. The model was trained using PhysioNet's QT database, comprised of 105 ambulatory ECG recordings.
  arXiv  Detail & Related papers  (2020-05-11T16:29:12Z)
• EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications [65.32004302942218]
  Brain-Computer Interface (BCI) is a powerful communication tool between users and systems. Recent technological advances have increased interest in electroencephalographic (EEG) based BCI for translational and healthcare applications.
  arXiv  Detail & Related papers  (2020-01-28T10:36:26Z)

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.