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Emotion Recognition and Classification Using Inception EfficientNet Based On Electroencephalography Signals by IAES International Journal of Robotics and Automation is a document available to read on EtoBox.
The document presents a novel EEG-based emotion recognition and classification model (EEG-EMRE) that utilizes quantum signal processing for noise reduction and an Inception EfficientNet architecture for feature extraction. The model classifies five emotional states (happy, sad, anger, scared, and anxiety) using a bidirectional-k nearest neighbors classifier, achieving improved accuracy compared to existing methods. The proposed approach addresses challenges in EEG signal processing and enhances the performa
- Author
- IAES International Journal of Robotics and Automation
- Language
- EN