EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG

• URL: http://arxiv.org/abs/2212.04951v1
• Date: Thu, 8 Dec 2022 10:15:52 GMT
• Title: EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG
• Authors: Andac Demir, Iya Khalil, Bulent Kiziltan
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 within an end-to-end discriminative setting. We propose a novel end-to-end machine learning pipeline, EEG-NeXt, which facilitates transfer learning by: i) aligning the EEG trials from different subjects in the Euclidean-space, ii) tailoring the techniques of deep learning for the scalograms of EEG signals to capture better frequency localization for low-frequency, longer-duration events, and iii) utilizing pretrained ConvNeXt (a modernized ResNet architecture which supersedes state-of-the-art (SOTA) image classification models) as the backbone network via adaptive finetuning. On publicly available datasets (Physionet Sleep Cassette and BNCI2014001) we benchmark our method against SOTA via cross-subject validation and demonstrate improved accuracy in cognitive activity classification along with better generalizability across cohorts.

Related papers

• MEEG and AT-DGNN: Improving EEG Emotion Recognition with Music Introducing and Graph-based Learning [8.561375293735733]
  We present the MEEG dataset, a multi-modal collection of music-induced electroencephalogram (EEG) recordings. We introduce the Attention-based Temporal Learner with Dynamic Graph Neural Network (AT-DGNN), a novel framework for EEG-based emotion recognition.
  arXiv  Detail & Related papers  (2024-07-08T01:58:48Z)
• Evaluating Fast Adaptability of Neural Networks for Brain-Computer Interface [0.23020018305241333]
  We use motor movement and imaginary signals as input to Convolutional Neural Networks (CNN) based classifier for experiments. We empirically show that layer-normalization can improve the adaptability of CNN-based EEG classifiers with not more than ten fine-tuning steps.
  arXiv  Detail & Related papers  (2024-04-14T22:36:53Z)
• EEG decoding with conditional identification information [7.873458431535408]
  Decoding EEG signals is crucial for unraveling human brain and advancing brain-computer interfaces. Traditional machine learning algorithms have been hindered by the high noise levels and inherent inter-person variations in EEG signals. Recent advances in deep neural networks (DNNs) have shown promise, owing to their advanced nonlinear modeling capabilities.
  arXiv  Detail & Related papers  (2024-03-21T13:38:59Z)
• Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets [53.367212596352324]
  We propose an unsupervised approach leveraging EEG signal physics. We map EEG channels to fixed positions using field, source-free domain adaptation. Our method demonstrates robust performance in brain-computer interface (BCI) tasks and potential biomarker applications.
  arXiv  Detail & Related papers  (2024-03-07T16:17:33Z)
• Enhancing Low-Density EEG-Based Brain-Computer Interfaces with Similarity-Keeping Knowledge Distillation [0.966840768820136]
  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.
  arXiv  Detail & Related papers  (2022-12-06T21:22:02Z)
• 2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets [89.84774119537087]
  We design two transfer learning challenges around diagnostics and Brain-Computer-Interfacing (BCI) Task 1 is centred on medical diagnostics, addressing automatic sleep stage annotation across subjects. Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets.
  arXiv  Detail & Related papers  (2022-02-14T12:12:20Z)
• CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNet [0.2936007114555107]
  Motor imagery (MI)-based brain-computer interface (BCI) systems are being increasingly employed to provide alternative means of communication and control. accurately classifying MI from brain signals is essential to obtain reliable BCI systems. Deep learning approaches have started to emerge as valid alternatives to standard machine learning techniques.
  arXiv  Detail & Related papers  (2021-05-17T14:57:13Z)
• 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)
• Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks [81.74442855155843]
  We introduce a new classification system that utilizes brain connectivity with a CNN and validate its effectiveness via the emotional video classification. The level of concentration of the brain connectivity related to the emotional property of the target video is correlated with classification performance.
  arXiv  Detail & Related papers  (2021-01-18T13:28:08Z)
• 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.