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• W4385202515 abstract "Human-computer interaction has seen growing interest in emotion detection. To gain deeper insights into the physiological indicators of emotions, researchers have delved into utilizing electroencephalography (EEG) and micro-gestures (MGs). This study assesses the efficacy of EEG and MG features in emotion detection by recruiting 15 participants to gather EEG and MG data in response to diverse figure-based emotional stimuli. To incorporate these features, this article introduces Emo-MG, a multimodal interface that integrates EEG and MG features and employs a long short-term memory (LSTM) model to predict emotional states within the valence-arousal-dominance (VAD) space. This study presents an in-depth analysis of feature importance and correlation results based on EEG and MG features for feature selection in emotion detection tasks. Through accuracy and F1-score metrics, Emo-MG achieves outstanding performance in emotion detection by comparing it to baseline and deep learning models, validating the efficacy of integrating EEG and MG features" @default.
• W4385202515 created "2023-07-25" @default.
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• W4385202515 date "2023-07-23" @default.
• W4385202515 modified "2023-09-23" @default.
• W4385202515 title "Emo-MG Framework: LSTM-based Multi-modal Emotion Detection through Electroencephalography Signals and Micro Gestures" @default.
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• W4385202515 doi "https://doi.org/10.1080/10447318.2023.2228983" @default.
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