EEG Based Emotion Recognition Using Ensemble Models and Laplacian Eigenmaps
Keywords:
Laplacian Eigenmaps, Dimensionality Reduction, Random Forest, eXtreme Gradient Boosting, Eigen values and Eigen Vectors, Ensemble Machine Learning, EEG Emotion RecognitionAbstract
Electroencephalography (EEG) signals provide valuable insights into human brain activity and emotional states. The DEAP dataset is a widely used resource containing EEG data and corresponding emotion labels recorded from participants watching music videos. Extracting meaningful EEG signal features is crucial for the recognition of emotions and other brain-computer interface applications. In this work, we explore the effectiveness of Laplacian Eigenmaps, a nonlinear dimensionality reduction technique, for extracting discriminative features from EEG signals on the DEAP dataset. We present an experimental study where we apply Laplacian Eigenmaps to reduce the high-dimensional EEG data into a lower-dimensional representation. Subsequently, we employ ensemble machine learning classifiers viz., Random Forest (RF) and eXtreme Gradient Boosting (XGB) classifiers for emotion classification based on the reduced features. The results demonstrate the capability of Laplacian Eigenmaps in capturing the underlying structure of EEG data, leading to improved emotion recognition accuracy with RF 98.1% and XGB 98.7% compared to other feature extraction.
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