Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting from Helwan, Egypt, by NewsRx journ alists, research stated, "This study explores a novel approach to emotion recogn ition through machine learning, addressing the limitations of previous methods. While deep learning has shown promise in this field, it often requires significa nt computational resources and time." The news correspondents obtained a quote from the research from Helwan University, "In response, we propose a multimodal approach utilizing Feature-level Fusion (FLF) and Decision-level Fusion (DLF) to enhance performance while reducing com plexity. The study focuses on integrating electroencephalogram (EEG), electromyo graphy (EMG), and electrooculogram (EOG) signals. Signal preprocessing involves extracting statistical features, power spectral density (PSD), and incremental e ntropy analysis. Recursive Feature Elimination (RFE) is employed as a feature se lector, facilitating the fusion of different signal features. Three fusion strat egies are explored: EEG with EOG, EEG with EMG, and a combination of EEG with EO G and EMG. For classification, the Bagging Classifier and K-Nearest Neighbors Al gorithm are chosen. Results demonstrate promising accuracy rates, with 95.7% for arousal, 96.41% for valence in subject-dependent classificatio n, 93.68% for arousal, and 93.23% for valence in sub ject-independent classification."
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