EEG-Based Emotion Recognition via Knowledge-Integrated Interpretable Method
TL;DR: In this article , the authors explore the use of representative interpretable models to analyze the learning behavior of convolutional neural networks (CNN) in EEG-based emotion recognition, and propose a new model by integrating brain science knowledge with the interpretability analysis results in the learning process.
read more
Abstract: Despite achieving success in many domains, deep learning models remain mostly black boxes, especially in electroencephalogram (EEG)-related tasks. Meanwhile, understanding the reasons behind model predictions is quite crucial in assessing trust and performance promotion in EEG-related tasks. In this work, we explore the use of representative interpretable models to analyze the learning behavior of convolutional neural networks (CNN) in EEG-based emotion recognition. According to the interpretable analysis, we find that similar features captured by our model and state-of-the-art model are consistent with previous brain science findings. Next, we propose a new model by integrating brain science knowledge with the interpretability analysis results in the learning process. Our knowledge-integrated model achieves better recognition accuracy on standard EEG-based recognition datasets.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A Customized ECA-CRNN Model for Emotion Recognition Based on EEG Signals
Yan Song,Panfeng Xu +1 more
TL;DR: In this paper , an efficient channel attention (ECA-Net) module was integrated into a modified combination of a customized convolutional neural network (CNN) and gated circulation unit (GRU) to enhance the internal relationship between frequency bands and improve recognition performance.
Optimizing 1D-CNN-Based Emotion Recognition Process through Channel and Feature Selection from EEG Signals
TL;DR: This work utilized a lightweight deep learning method, specifically one-dimensional convolutional neural networks (1D-CNN), to analyze EEG signals and classify emotional states and demonstrated significant potential for the implementation of a cost-effective IoT device to collect EEG signals, thereby enhancing the feasibility and applicability of the data.
8
ST-SHAP: A hierarchical and explainable attention network for emotional EEG representation learning and decoding
Minmin Miao,Liang Jin,Zhenzhen Sheng,Shiluo Xu,Baoguo Xu,Wenjun Hu +5 more
Souping Up Emotions: Interpretable Emotion Recognition from Eeg Signals Using Model Soup and Explainable Ai
Eamin Chaudary,Wajid Mumtaz +1 more
- 01 Jan 2023
TL;DR: Model soup enhances emotion recognition accuracy using EEG signals by averaging weights of fine-tuned deep learning models and improving interpretability.
References
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro,Sameer Singh,Carlos Guestrin +2 more
- 13 Aug 2016
TL;DR: In this article, the authors propose LIME, a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem.
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Ramprasaath R. Selvaraju,Michael Cogswell,Abhishek Das,Ramakrishna Vedantam,Devi Parikh,Dhruv Batra +5 more
- 01 Oct 2017
TL;DR: This work combines existing fine-grained visualizations to create a high-resolution class-discriminative visualization, Guided Grad-CAM, and applies it to image classification, image captioning, and visual question answering (VQA) models, including ResNet-based architectures.
14.7K
Learning Deep Features for Discriminative Localization
Bolei Zhou,Aditya Khosla,Agata Lapedriza,Aude Oliva,Antonio Torralba +4 more
- 27 Jun 2016
TL;DR: This work revisits the global average pooling layer proposed in [13], and sheds light on how it explicitly enables the convolutional neural network (CNN) to have remarkable localization ability despite being trained on imagelevel labels.
Least angle regression
Bradley Efron,Trevor Hastie,Iain M. Johnstone,Robert Tibshirani,Hemant Ishwaran,Keith Knight,Jean-Michel Loubes,Jean-Michel Loubes,Pascal Massart,Pascal Massart,David Madigan,David Madigan,Greg Ridgeway,Greg Ridgeway,Saharon Rosset,Saharon Rosset,Ji Zhu,Robert A. Stine,Berwin A. Turlach,Sanford Weisberg +19 more
TL;DR: A publicly available algorithm that requires only the same order of magnitude of computational effort as ordinary least squares applied to the full set of covariates is described.
Least Angle Regression
TL;DR: Least Angle Regression (LARS) as discussed by the authors is a new model selection algorithm, which is a useful and less greedy version of traditional forward selection methods such as All Subsets, Forward Selection and Backward Elimination.