Emotion Detection from EEG using Transfer Learning

• URL: http://arxiv.org/abs/2306.05680v1
• Date: Fri, 9 Jun 2023 05:43:06 GMT
• Title: Emotion Detection from EEG using Transfer Learning
• Authors: Sidharth Sidharth, Ashish Abraham Samuel, Ranjana H, Jerrin Thomas Panachakel, Sana Parveen K
• Abstract summary: We employed transfer learning to overcome the challenge of limited data availability in EEG-based emotion detection. The input to the model was in the form of an image matrix, which comprised Mean Phase Coherence (MPC) and Magnitude Squared Coherence (MSC)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The detection of emotions using an Electroencephalogram (EEG) is a crucial area in brain-computer interfaces and has valuable applications in fields such as rehabilitation and medicine. In this study, we employed transfer learning to overcome the challenge of limited data availability in EEG-based emotion detection. The base model used in this study was Resnet50. Additionally, we employed a novel feature combination in EEG-based emotion detection. The input to the model was in the form of an image matrix, which comprised Mean Phase Coherence (MPC) and Magnitude Squared Coherence (MSC) in the upper-triangular and lower-triangular matrices, respectively. We further improved the technique by incorporating features obtained from the Differential Entropy (DE) into the diagonal, which previously held little to no useful information for classifying emotions. The dataset used in this study, SEED EEG (62 channel EEG), comprises three classes (Positive, Neutral, and Negative). We calculated both subject-independent and subject-dependent accuracy. The subject-dependent accuracy was obtained using a 10-fold cross-validation method and was 93.1%, while the subject-independent classification was performed by employing the leave-one-subject-out (LOSO) strategy. The accuracy obtained in subject-independent classification was 71.6%. Both of these accuracies are at least twice better than the chance accuracy of classifying 3 classes. The study found the use of MSC and MPC in EEG-based emotion detection promising for emotion classification. The future scope of this work includes the use of data augmentation techniques, enhanced classifiers, and better features for emotion classification.

Related papers

• Deep Learning-based Classification of Dementia using Image Representation of Subcortical Signals [4.17085180769512]
  Alzheimer's disease (AD) and Frontotemporal dementia (FTD) are the common forms of dementia, each with distinct progression patterns. This study aims to develop a deep learning-based classification system for dementia by analyzing scout time-series signals from deep brain regions.
  arXiv  Detail & Related papers  (2024-08-20T13:11:43Z)
• A Supervised Information Enhanced Multi-Granularity Contrastive Learning Framework for EEG Based Emotion Recognition [14.199298112101802]
  This study introduces a novel Supervised Info-enhanced Contrastive Learning framework for EEG based Emotion Recognition (SICLEER) We propose a joint learning model combining self-supervised contrastive learning loss and supervised classification loss.
  arXiv  Detail & Related papers  (2024-05-12T11:51:00Z)
• EEG-based Emotion Style Transfer Network for Cross-dataset Emotion Recognition [45.26847258736848]
  We propose an EEG-based Emotion Style Transfer Network (E2STN) to obtain EEG representations that contain the content information of source domain and the style information of target domain. The E2STN can achieve the state-of-the-art performance on cross-dataset EEG emotion recognition tasks.
  arXiv  Detail & Related papers  (2023-08-09T16:54:40Z)
• Unveiling Emotions from EEG: A GRU-Based Approach [2.580765958706854]
  Gated Recurrent Unit (GRU) algorithm is tested to see if it can use EEG signals to predict emotional states. Our publicly accessible dataset consists of resting neutral data as well as EEG recordings from people who were exposed to stimuli evoking happy, neutral, and negative emotions.
  arXiv  Detail & Related papers  (2023-07-20T11:04:46Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)
• EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders [27.3162026528455]
  We explore whether representing neural data, in response to emotional stimuli, in a latent vector space can serve to both predict emotional states. We propose a conditional variational autoencoder based framework, EEG2Vec, to learn generative-discriminative representations from EEG data.
  arXiv  Detail & Related papers  (2022-07-16T19:25:29Z)
• Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition [9.07006689672858]
  We propose Contrast Learning method for Inter-Subject Alignment (ISA) for reliable cross-subject emotion recognition. ISA involves maximizing the similarity in EEG signals across subjects when they received the same stimuli in contrast to different ones. A convolutional neural network with depthwise spatial convolution and temporal convolution layers was applied to learn inter-subject representations from raw EEG signals.
  arXiv  Detail & Related papers  (2021-09-20T14:13:45Z)
• 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)
• 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)
• 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)

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.