Multitask Autoencoder-Based Two-Phase Framework Using Multilevel Feature Fusion for EEG Emotion Recognition

Changgyun Jin, Chanwoo Shin, Hanul Kim, Seong-Eun Kim
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Abstract

Emotion recognition has emerged as a active research area, gaining relevance from advancements in deep learning. This study focuses on using electroencephalogram (EEG) data for emotion recognition and addresses the challenge of subject-dependent variability in EEG-based emotion recognition by proposing a novel architecture that employs multilevel feature fusion and a multitask autoencoder-based two-phase framework. The first phase generates classspecific data, while the second phase uses these for model training. The proposed model was validated using the SEED dataset and demonstrated state-of-the art perforamnce with an accuracy of 99.4 % in a subject-independent setting.
基于多任务自动编码器的两阶段框架,利用多级特征融合实现脑电图情感识别
情感识别已成为一个活跃的研究领域,并从深度学习的进步中获得了相关性。本研究的重点是利用脑电图(EEG)数据进行情感识别,并通过提出一种采用多级特征融合和基于多任务自动编码器的两阶段框架的新型架构,解决了基于 EEG 的情感识别中的主体依赖性变异性难题。第一阶段生成特定类别的数据,第二阶段利用这些数据进行模型训练。我们使用 SEED 数据集对所提出的模型进行了验证,结果表明,该模型的准确率达到了 99.4%,达到了最先进的水平。
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