# Inter Subject Emotion Recognition Using Spatio-Temporal Features From EEG Signal

arXiv: https://arxiv.org/abs/2305.19379 · HTML: https://mlsanity.com/paper/2305.19379/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 18 references that are in the map.
- First posted on arXiv 27 May 2023.

## Builds on (most cited first, 1 of 1)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)

## Built on by (most cited first, 3 of 3)

- [Proactive Emotion Tracker: AI-Driven Continuous Mood and Emotion Monitoring](https://mlsanity.com/paper/2401.13722/) (2024)
- [Adaptive Progressive Attention Graph Neural Network for EEG Emotion Recognition](https://mlsanity.com/paper/2501.14246/) (2025)
- [Technological Advances in Detecting and Managing Cognitive Impairment in Older Adults: Trends, Challenges, and Future Directions](https://mlsanity.com/paper/2607.28687/) (2026)

## Abstract (arXiv)

Inter-subject or subject-independent emotion recognition has been a challenging task in affective computing. This work is about an easy-to-implement emotion recognition model that classifies emotions from EEG signals subject independently. It is based on the famous EEGNet architecture, which is used in EEG-related BCIs. We used the Dataset on Emotion using Naturalistic Stimuli (DENS) dataset. The dataset contains the Emotional Events -- the precise information of the emotion timings that participants felt. The model is a combination of regular, depthwise and separable convolution layers of CNN to classify the emotions. The model has the capacity to learn the spatial features of the EEG channels and the temporal features of the EEG signals variability with time. The model is evaluated for the valence space ratings. The model achieved an accuracy of 73.04%.