A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition

• URL: http://arxiv.org/abs/2307.03068v1
• Date: Thu, 6 Jul 2023 15:35:14 GMT
• Title: A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition
• Authors: Shadi Sartipi and Mastaneh Torkamani-Azar and Mujdat Cetin
• Abstract summary: We introduce a deep neural network that acquires interpretable representations by a hybrid structure of network-temporal encoding and recurrent attention blocks. We demonstrate that our proposed architecture exceeds state-of-the-art results for emotion classification on the publicly available DEAP dataset.
• Score: 1.6328866317851187
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recently, physiological data such as electroencephalography (EEG) signals have attracted significant attention in affective computing. In this context, the main goal is to design an automated model that can assess emotional states. Lately, deep neural networks have shown promising performance in emotion recognition tasks. However, designing a deep architecture that can extract practical information from raw data is still a challenge. Here, we introduce a deep neural network that acquires interpretable physiological representations by a hybrid structure of spatio-temporal encoding and recurrent attention network blocks. Furthermore, a preprocessing step is applied to the raw data using graph signal processing tools to perform graph smoothing in the spatial domain. We demonstrate that our proposed architecture exceeds state-of-the-art results for emotion classification on the publicly available DEAP dataset. To explore the generality of the learned model, we also evaluate the performance of our architecture towards transfer learning (TL) by transferring the model parameters from a specific source to other target domains. Using DEAP as the source dataset, we demonstrate the effectiveness of our model in performing cross-modality TL and improving emotion classification accuracy on DREAMER and the Emotional English Word (EEWD) datasets, which involve EEG-based emotion classification tasks with different stimuli.

Related papers

• MEEG and AT-DGNN: Advancing EEG Emotion Recognition with Music and Graph Learning [5.766036473197785]
  We introduce the MEEG dataset, a comprehensive multi-modal music-induced electroencephalogram (EEG) dataset and a novel framework for EEG-based emotion recognition. The MEEG dataset captures a wide range of emotional responses to music, enabling an in-depth analysis of patterns in musical contexts. The AT-DGNN combines an attention-based temporal learner with a dynamic graph neural network (DGNN) to accurately model the local and global graph dynamics of EEG data.
  arXiv  Detail & Related papers  (2024-07-08T01:58:48Z)
• A Comparative Study of Data Augmentation Techniques for Deep Learning Based Emotion Recognition [11.928873764689458]
  We conduct a comprehensive evaluation of popular deep learning approaches for emotion recognition. We show that long-range dependencies in the speech signal are critical for emotion recognition. Speed/rate augmentation offers the most robust performance gain across models.
  arXiv  Detail & Related papers  (2022-11-09T17:27:03Z)
• 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)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation [56.264157127549446]
  Speech emotion recognition (SER) is a challenging task that plays a crucial role in natural human-computer interaction. One of the main challenges in SER is data scarcity. We propose a transfer learning strategy combined with spectrogram augmentation.
  arXiv  Detail & Related papers  (2021-08-05T10:39:39Z)
• Subject Independent Emotion Recognition using EEG Signals Employing Attention Driven Neural Networks [2.76240219662896]
  A novel deep learning framework capable of doing subject-independent emotion recognition is presented. A convolutional neural network (CNN) with attention framework is presented for performing the task. The proposed approach has been validated using publicly available datasets.
  arXiv  Detail & Related papers  (2021-06-07T09:41:15Z)
• ScalingNet: extracting features from raw EEG data for emotion recognition [4.047737925426405]
  We propose a novel convolutional layer allowing to adaptively extract effective data-driven spectrogram-like features from raw EEG signals. The proposed neural network architecture based on the scaling layer, references as ScalingNet, has achieved the state-of-the-art result across the established DEAP benchmark dataset.
  arXiv  Detail & Related papers  (2021-02-07T08:54:27Z)
• 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)
• Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks [82.54695985117783]
  We investigate the suitability of state-of-the-art deep learning architectures for continuous emotion recognition using long video sequences captured in-the-wild. We have developed and evaluated convolutional recurrent neural networks combining 2D-CNNs and long short term-memory units, and inflated 3D-CNN models, which are built by inflating the weights of a pre-trained 2D-CNN model during fine-tuning.
  arXiv  Detail & Related papers  (2020-11-18T13:42:05Z)
• Uncovering the structure of clinical EEG signals with self-supervised learning [64.4754948595556]
  Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG) By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
  arXiv  Detail & Related papers  (2020-07-31T14:34:47Z)

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.