CROSS-CORPUS EEG-BASED EMOTION RECOGNITION

Soheil Rayatdoost · Mohammad Soleymani

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

Abstract

Lack of generalization is a common problem in automatic emotion recognition. The present study aims to explore the suitability of the existing EEG features for emotion recognition and investigate the performance of emotion recognition methods across different corpora. We introduce a novel dataset which includes spontaneous emotions and was analyzed in addition to the existing datasets for cross-corpus evaluation. We demonstrate that the performance of the existing methods significantly decreases when evaluated across different corpora. The best results are obtained by a convolutional neural network fed by spectral topography maps from different bands. We provide some evidence that stimuli-related sensory information is learned by machine learning models for emotion recognition using EEG signals.

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References

2011DEAP: A Database for Emotion Analysis ;Using Physiological Signals 2015Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks 2011A Multimodal Database for Affect Recognition and Implicit Tagging 2014Feature Extraction and Selection for Emotion Recognition from EEG 2017Identifying Stable Patterns over Time for Emotion Recognition from EEG 2009Emotion Recognition From EEG Using Higher Order Crossings 2015Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection 1995Observations: SAM: The Self-Assessment Manikin An Efficient Cross-Cultural Measurement Of Emotional Response 1

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2022Affect recognition from scalp-EEG using channel-wise encoder networks coupled with geometric deep learning and multi-channel feature fusion 2022Joint Temporal Convolutional Networks and Adversarial Discriminative Domain Adaptation for EEG-Based Cross-Subject Emotion Recognition

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