# TSception: A Deep Learning Framework for Emotion Detection Using EEG

arXiv: https://arxiv.org/abs/2004.02965 · HTML: https://mlsanity.com/paper/2004.02965/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2020.

## Built on by (most cited first, 14 of 14)

- [TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition](https://mlsanity.com/paper/2104.02935/) (2021)
- [EEG based Emotion Recognition: A Tutorial and Review](https://mlsanity.com/paper/2203.11279/) (2022)
- [LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface](https://mlsanity.com/paper/2105.02786/) (2021)
- [GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition](https://mlsanity.com/paper/2109.03124/) (2021)
- [THD-BAR: Topology Hierarchical Derived Brain Autoregressive Modeling for EEG Generic Representations](https://mlsanity.com/paper/2511.13733/) (2025)
- [A Comprehensive Survey on Affective Computing; Challenges, Trends, Applications, and Future Directions](https://mlsanity.com/paper/2305.07665/) (2023)
- [Enhancing Generalization in PPG-Based Emotion Measurement with a CNN-TCN-LSTM Model](https://mlsanity.com/paper/2507.14173/) (2025)
- [MEEG and AT-DGNN: Improving EEG Emotion Recognition with Music Introducing and Graph-based Learning](https://mlsanity.com/paper/2407.05550/) (2024)
- [A channel attention based MLP-Mixer network for motor imagery decoding with EEG](https://mlsanity.com/paper/2110.10939/) (2021)
- [Dynamic Causal Disentanglement Model for Dialogue Emotion Detection](https://mlsanity.com/paper/2309.06928/) (2023)
- [A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification](https://mlsanity.com/paper/2309.11714/) (2023)
- [Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection](https://mlsanity.com/paper/2501.15062/) (2025)
- [Cortical-SSM: A Deep State Space Model for Motor Imagery Decoding from EEG Signals](https://mlsanity.com/paper/2510.15371/) (2025)
- [Beyond Augmentation: Score-Guided Pathological Prior for EEG-based Depression Detection](https://mlsanity.com/paper/2606.00180/) (2026)

## Abstract (arXiv)

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