SAE+LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG

Xiaofen Xing · Zhiyang Li · Tianyuan Xu · Lin Shu · Bin Hu · Xiangmin Xu

269 citations0/0 files storedFrontiers in Neurorobotics

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Emotion and Mood Recognition EEG and Brain-Computer Interfaces Gaze Tracking and Assistive Technology

Abstract

EEG-based automatic emotion recognition can help brain-inspired robots in improving their interactions with humans. This paper presents a novel framework for emotion recognition using multi-channel electroencephalogram (EEG). The framework consists of a linear EEG mixing model and an emotion timing model. Our proposed framework considerably decomposes the EEG source signals from the collected EEG signals and improves classification accuracy by using the context correlations of the EEG feature sequences. Specially, Stack AutoEncoder (SAE) is used to build and solve the linear EEG mixing model and the emotion timing model is based on the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). The framework was implemented on the DEAP dataset for an emotion recognition experiment, where the mean accuracy of emotion recognition achieved 81.10% in valence and 74.38% in arousal, and the effectiveness of our framework was verified. Our framework exhibited a better performance in emotion recognition using multi-channel EEG than the compared conventional approaches in the experiments.

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References

2011DEAP: A Database for Emotion Analysis ;Using Physiological Signals 2010EEG-Based Emotion Recognition in Music Listening 2017Identifying Stable Patterns over Time for Emotion Recognition from EEG 2018A Review of Emotion Recognition Using Physiological Signals 2014Learning Salient Features for Speech Emotion Recognition Using Convolutional Neural Networks 2015Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection 2016Wavelet-based emotion recognition system using EEG signal 2016Emotion recognition from multi-channel EEG data through Convolutional Recurrent Neural Network

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2020Investigating the Use of Pretrained Convolutional Neural Network on Cross-Subject and Cross-Dataset EEG Emotion Recognition 2022Investigating EEG-based functional connectivity patterns for multimodal emotion recognition 2023Emotion recognition in EEG signals using deep learning methods: A review 2023EEG emotion recognition based on the attention mechanism and pre-trained convolution capsule network 2019A Multi-Column CNN Model for Emotion Recognition from EEG Signals

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