EEG decoding with conditional identification information

• URL: http://arxiv.org/abs/2403.15489v1
• Date: Thu, 21 Mar 2024 13:38:59 GMT
• Title: EEG decoding with conditional identification information
• Authors: Pengfei Sun, Jorg De Winne, Paul Devos, Dick Botteldooren,
• Abstract summary: 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.
• Score: 7.873458431535408
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. However, DNN still faces challenge in decoding EEG samples of unseen individuals. To address this, this paper introduces a novel approach by incorporating the conditional identification information of each individual into the neural network, thereby enhancing model representation through the synergistic interaction of EEG and personal traits. We test our model on the WithMe dataset and demonstrated that the inclusion of these identifiers substantially boosts accuracy for both subjects in the training set and unseen subjects. This enhancement suggests promising potential for improving for EEG interpretability and understanding of relevant identification features.

Related papers

• CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion [49.1574468325115]
  A key aim in EEG analysis is to extract the underlying neural activation (content) as well as to account for the individual subject variability (style) Inspired by recent advancements in voice conversion technologies, we propose a novel contrastive split-latent permutation autoencoder (CSLP-AE) framework that directly optimize for EEG conversion.
  arXiv  Detail & Related papers  (2023-11-13T22:46:43Z)
• 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)
• Evaluating the structure of cognitive tasks with transfer learning [67.22168759751541]
  This study investigates the transferability of deep learning representations between different EEG decoding tasks. We conduct extensive experiments using state-of-the-art decoding models on two recently released EEG datasets.
  arXiv  Detail & Related papers  (2023-07-28T14:51:09Z)
• 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-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification [0.5616884466478884]
  We propose an end-to-end deep learning architecture called EEG-ITNet. Our model can extract rich spectral, spatial, and temporal information from multi-channel EEG signals. EEG-ITNet shows up to 5.9% improvement in the classification accuracy in different scenarios.
  arXiv  Detail & Related papers  (2022-04-14T13:18:43Z)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition [15.812231441367022]
  We propose a novel data augmentation framework, namely Generative Adversarial Network-based Self-supervised Data Augmentation (GANSER) As the first to combine adversarial training with self-supervised learning for EEG-based emotion recognition, the proposed framework can generate high-quality simulated EEG samples. A transformation function is employed to mask parts of EEG signals and force the generator to synthesize potential EEG signals based on the remaining parts.
  arXiv  Detail & Related papers  (2021-09-07T14:42:55Z)
• EEG-based Cross-Subject Driver Drowsiness Recognition with an Interpretable Convolutional Neural Network [0.0]
  We develop a novel convolutional neural network combined with an interpretation technique that allows sample-wise analysis of important features for classification. Results show that the model achieves an average accuracy of 78.35% on 11 subjects for leave-one-out cross-subject recognition.
  arXiv  Detail & Related papers  (2021-05-30T14:47:20Z)
• 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)
• A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition [51.203579838210885]
  We propose a transferable attention neural network (TANN) for EEG emotion recognition. TANN learns the emotional discriminative information by highlighting the transferable EEG brain regions data and samples adaptively. This can be implemented by measuring the outputs of multiple brain-region-level discriminators and one single sample-level discriminator.
  arXiv  Detail & Related papers  (2020-09-21T02:42:30Z)
• Human brain activity for machine attention [8.673635963837532]
  We are the first to exploit neuroscientific data, namely electroencephalography (EEG), to inform a neural attention model about language processing of the human brain. We devise a method for finding such EEG features to supervise machine attention through combining theoretically motivated cropping with random forest tree splits. We apply these features to regularise attention on relation classification and show that EEG is more informative than strong baselines.
  arXiv  Detail & Related papers  (2020-06-09T08:39:07Z)

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.