计算机科学
脑电图
人工智能
语音识别
模式识别(心理学)
心理学
神经科学
作者
Purnata Saha,Ali K. Ansaruddin Kunju,Molla E. Majid,Saad Bin Abul Kashem,Mohammad Nashbat,Azad Ashraf,Mazhar Hasan,Amith Khandakar,Md Shafayet Hossain,Abdulrahman Alqahtani,Muhammad E. H. Chowdhury
标识
DOI:10.1016/j.bspc.2024.106002
摘要
Emotion Recognition Systems (ERS) play a pivotal role in facilitating naturalistic Human-Machine Interactions (HMI). The research has utilized a dataset with diverse physiological signals, including Electroencephalogram (EEG), Photoplethysmography (PPG), and Electrocardiogram (ECG), to detect emotions evoked by video stimuli. The study has addressed challenges with EEG data, particularly prefrontal channels contaminated by eye blink artifacts. To tackle this, a novel 1D deep learning model, MultiResUNet3p, effectively generated clean EEG signals. Extensive features have been extracted from each modality (TD, FD, TFD), and the study identified that combining 112 features from EEG and ECG achieved the highest accuracy. The emotion classification task encompassed six emotions, and the model demonstrates outstanding performance with 96.12% accuracy in binary classification (Positive vs. Negative) and 94.25% accuracy in a complex multiclass classification of six emotions (Happy, Anger, Disgust, Fear, Neutral, and Sad). This research underscores the potential of integrating multiple physiological signals and advanced techniques to significantly improve emotion recognition accuracy, particularly in real-world scenarios involving naturalistic Human-Machine Interactions (HMI).
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