TSception: A Deep Learning Framework for Emotion Detection Using EEG (2004.02965v2)

Abstract: In this paper, we propose a deep learning framework, TSception, for emotion detection from electroencephalogram (EEG). TSception consists of temporal and spatial convolutional layers, which learn discriminative representations in the time and channel domains simultaneously. The temporal learner consists of multi-scale 1D convolutional kernels whose lengths are related to the sampling rate of the EEG signal, which learns multiple temporal and frequency representations. The spatial learner takes advantage of the asymmetry property of emotion responses at the frontal brain area to learn the discriminative representations from the left and right hemispheres of the brain. In our study, a system is designed to study the emotional arousal in an immersive virtual reality (VR) environment. EEG data were collected from 18 healthy subjects using this system to evaluate the performance of the proposed deep learning network for the classification of low and high emotional arousal states. The proposed method is compared with SVM, EEGNet, and LSTM. TSception achieves a high classification accuracy of 86.03%, which outperforms the prior methods significantly (p<0.05). The code is available at https://github.com/deepBrains/TSception

• The paper introduces TSception, which integrates temporal and spatial convolutional layers to enhance emotion detection from EEG data.
• It employs multi-scale 1D convolutional kernels and hemisphere-based spatial kernels to extract discriminative features directly from raw signals.
• Experimental results in a VR setting demonstrate significant improvements, achieving 86.03% accuracy versus traditional methods.

TSception: A Deep Learning Framework for Emotion Detection Using EEG

The research paper entitled "TSception: A Deep Learning Framework for Emotion Detection Using EEG" presents a novel approach to emotion detection utilizing electroencephalogram (EEG) signals through a deep learning framework. The proposed model, TSception, seeks to enhance the detection and classification of emotional arousal by addressing and integrating temporal and spatial representation learning.

Overview

TSception is designed with two essential learners: temporal and spatial convolutional layers, which simultaneously learn discriminative representations in time and channel domains. This dual approach allows the capturing of multi-frequency and multi-temporal patterns and considers the asymmetrical properties of emotional responses in the frontal brain regions.

The temporal learner employs multi-scale 1D convolutional kernels with lengths correlated to the EEG signal's sampling rate. These kernels are devised to learn multiple temporal and frequency representations. By doing so, TSception can effectively extract relevant features from raw EEG data without relying on hand-crafted features.

In contrast, the spatial learner leverages psychophysiological evidence that indicates the left and right hemispheres of the frontal brain are differentially associated with specific emotional states and traits. By implementing hemisphere-based kernels, the model learns to differentiate and capture unique spatial patterns corresponding to emotional arousal.

Methodology and Results

The experimental evaluation conducted in an immersive virtual reality environment involved collecting EEG data from 18 healthy subjects. The classification task was to distinguish between low and high emotional arousal states. Compared against traditional methods such as SVM and contemporary deep learning models like EEGNet and LSTM, TSception achieved a notably higher classification accuracy of 86.03%, outperforming these methods significantly with a statistically substantial result ($p < 0.05$).

The research further corroborates that TSception's design is capable of extracting meaningful representations from EEG data, emphasizing its potential to surpass traditional machine learning techniques that heavily depend on pre-extracted or hand-crafted features. The comparative analysis with simplified self-paper models, specifically Tception and Sception, underlines the collective strength of integrating both temporal and spatial learning components.

Implications and Future Prospects

TSception's proficient performance in EEG-based emotion detection provides critical insights into the development of more refined brain-computer interfaces (BCIs), particularly within immersive and interactive environments like virtual reality. By automating feature extraction and capitalizing on EEG's rich temporal and spatial information, TSception shows promise in improving emotional detection systems used in psychological interventions, human-computer interactions, and affective computing.

Although the current paper concentrated on distinguishing emotional arousal states, future work could extend the application of TSception to a broader spectrum of emotional and cognitive state classifications. Additionally, investigating the incorporation of more EEG channels and exploiting more granular subdivisions of the brain's topographical features might further augment classification fidelity.

The future trajectory of TSception could include its adaptation to real-time emotion recognition systems, allowing for immediate applications in therapeutic settings and adaptive personal technology enhancements. Moreover, given its scalable architecture, there exists potential for customization and optimization concerning varied EEG datasets and alternative affective dimensions.

In conclusion, the TSception framework exemplifies a robust example of deep learning applied to the intricate task of emotion detection from EEG signals, heralding a step forward in more responsive and intelligent computational systems.