Evaluation in EEG Emotion Recognition: State-of-the-Art Review and Unified Framework

• URL: http://arxiv.org/abs/2505.18175v1
• Date: Wed, 14 May 2025 20:44:39 GMT
• Title: Evaluation in EEG Emotion Recognition: State-of-the-Art Review and Unified Framework
• Authors: Natia Kukhilava, Tatia Tsmindashvili, Rapael Kalandadze, Anchit Gupta, Sofio Katamadze, François Brémond, Laura M. Ferrari, Philipp Müller, Benedikt Emanuel Wirth,
• Abstract summary: Electroencephalography-based Emotion Recognition (EEG-ER) has become a growing research area in recent years.<n>We propose a unified evaluation protocol, EEGain, which enables an easy and efficient evaluation of new methods and datasets.<n>EEGain includes standardized methods for data pre-processing, data splitting, evaluation metrics, and the ability to load the six most relevant datasets.
• Score: 14.019800269775262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electroencephalography-based Emotion Recognition (EEG-ER) has become a growing research area in recent years. Analyzing 216 papers published between 2018 and 2023, we uncover that the field lacks a unified evaluation protocol, which is essential to fairly define the state of the art, compare new approaches and to track the field's progress. We report the main inconsistencies between the used evaluation protocols, which are related to ground truth definition, evaluation metric selection, data splitting types (e.g., subject-dependent or subject-independent) and the use of different datasets. Capitalizing on this state-of-the-art research, we propose a unified evaluation protocol, EEGain (https://github.com/EmotionLab/EEGain), which enables an easy and efficient evaluation of new methods and datasets. EEGain is a novel open source software framework, offering the capability to compare - and thus define - state-of-the-art results. EEGain includes standardized methods for data pre-processing, data splitting, evaluation metrics, and the ability to load the six most relevant datasets (i.e., AMIGOS, DEAP, DREAMER, MAHNOB-HCI, SEED, SEED-IV) in EEG-ER with only a single line of code. In addition, we have assessed and validated EEGain using these six datasets on the four most common publicly available methods (EEGNet, DeepConvNet, ShallowConvNet, TSception). This is a significant step to make research on EEG-ER more reproducible and comparable, thereby accelerating the overall progress of the field.

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