<p class="jove_content">The paper is centered on EEG-based emotion recognition, which is an emerging domain in neuroscience and emotional computing. The traditional linear methods are insufficient as it is impossible to represent complicated emotional structure in video. To address this, the authors propose a new model, Combined Weighted Feature Correlation (CWFC), that capture the emotional relationships. After band pass filtering, Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT) and Fast Fourier Transform (FFT) are employed by the model to extract non-linear features from EEG. In this case best feature is selected by LSTM and these features are evaluated by Random Forest Classifier. Introduction Generative Adversarial Network (GAN) based data augmentation gives 88% (for valence) and 86% (for Evaluation of Test Results The proposed GAN based arousal) accuracy on the DEAP dataset which is very high in data augmentation has been able to improve the performance when performance. The model validates that CWFC is beneficial across datasets, as it delivers an overall classification accuracy of 89% for emotional states on the SEED dataset.</p>

combined-weighted-feature-correlation-approach-for-enhanced-eeg-based

Institution - National Institute of Technology Patna

Articles Published in JoVE