Inter Subject Emotion Recognition Using Spatio-Temporal Features From EEG Signal

• URL: http://arxiv.org/abs/2305.19379v1
• Date: Sat, 27 May 2023 07:43:19 GMT
• Title: Inter Subject Emotion Recognition Using Spatio-Temporal Features From EEG Signal
• Authors: Mohammad Asif, Diya Srivastava, Aditya Gupta, Uma Shanker Tiwary
• Abstract summary: This work is about an easy-to-implement emotion recognition model that classifies emotions from EEG signals subject independently. The model is a combination of regular, depthwise and separable convolution layers of CNN to classify the emotions. The model achieved an accuracy of 73.04%.
• Score: 4.316570025748204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inter-subject or subject-independent emotion recognition has been a challenging task in affective computing. This work is about an easy-to-implement emotion recognition model that classifies emotions from EEG signals subject independently. It is based on the famous EEGNet architecture, which is used in EEG-related BCIs. We used the Dataset on Emotion using Naturalistic Stimuli (DENS) dataset. The dataset contains the Emotional Events -- the precise information of the emotion timings that participants felt. The model is a combination of regular, depthwise and separable convolution layers of CNN to classify the emotions. The model has the capacity to learn the spatial features of the EEG channels and the temporal features of the EEG signals variability with time. The model is evaluated for the valence space ratings. The model achieved an accuracy of 73.04%.

Related papers

• Smile upon the Face but Sadness in the Eyes: Emotion Recognition based on Facial Expressions and Eye Behaviors [63.194053817609024]
  We introduce eye behaviors as an important emotional cues for the creation of a new Eye-behavior-aided Multimodal Emotion Recognition dataset. For the first time, we provide annotations for both Emotion Recognition (ER) and Facial Expression Recognition (FER) in the EMER dataset. We specifically design a new EMERT architecture to concurrently enhance performance in both ER and FER.
  arXiv  Detail & Related papers  (2024-11-08T04:53:55Z)
• Decoding Human Emotions: Analyzing Multi-Channel EEG Data using LSTM Networks [0.0]
  This study aims to understand and improve the predictive accuracy of emotional state classification by applying a Long Short-Term Memory (LSTM) network to analyze EEG signals. Using a popular dataset of multi-channel EEG recordings known as DEAP, we look towards leveraging LSTM networks' properties to handle temporal dependencies within EEG signal data. We obtain accuracies of 89.89%, 90.33%, 90.70%, and 90.54% for arousal, valence, dominance, and likeness, respectively, demonstrating significant improvements in emotion recognition model capabilities.
  arXiv  Detail & Related papers  (2024-08-19T18:10:47Z)
• Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion Recognition [2.1645626994550664]
  We propose a novel Joint Contrastive learning framework with Feature Alignment to address cross-corpus EEG-based emotion recognition. In the pre-training stage, a joint domain contrastive learning strategy is introduced to characterize generalizable time-frequency representations of EEG signals. In the fine-tuning stage, JCFA is refined in conjunction with downstream tasks, where the structural connections among brain electrodes are considered.
  arXiv  Detail & Related papers  (2024-04-15T08:21:17Z)
• 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)
• 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)
• 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)
• EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters [72.19032452642728]
  We propose a novel differentiable EEG decoding pipeline consisting of learnable filters and a pre-determined feature extraction module. We demonstrate the utility of our model towards emotion recognition from EEG signals on the SEED dataset and on a new EEG dataset of unprecedented size. The discovered features align with previous neuroscience studies and offer new insights, such as marked differences in the functional connectivity profile between left and right temporal areas during music listening.
  arXiv  Detail & Related papers  (2021-10-19T14:22:04Z)
• 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)
• Enhancing Cognitive Models of Emotions with Representation Learning [58.2386408470585]
  We present a novel deep learning-based framework to generate embedding representations of fine-grained emotions. Our framework integrates a contextualized embedding encoder with a multi-head probing model. Our model is evaluated on the Empathetic Dialogue dataset and shows the state-of-the-art result for classifying 32 emotions.
  arXiv  Detail & Related papers  (2021-04-20T16:55:15Z)
• 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)
• Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition [8.356765961526955]
  We investigate three functional connectivity network features: strength, clustering, coefficient and eigenvector centrality. The discrimination ability of the EEG connectivity features in emotion recognition is evaluated on three public EEG datasets. We construct a multimodal emotion recognition model by combining the functional connectivity features from EEG and the features from eye movements or physiological signals.
  arXiv  Detail & Related papers  (2020-04-04T16:51:56Z)

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.