Therefore, we present here an analysis of two different classification methods which are SVM and KNN. Four different types of emotional stimulus were presented ...

The results show that the emotion recognition from EEG brain signals might be possible and an analysis of two different classification methods which are SVM ...

Therefore, we present here an analysis of two different classification methods which are SVM and KNN. Four different types of emotional stimulus were presented ...

The best dynamic feature classifier combination was the SVM with temporal features (63.8%), followed by kNN with temporal features (63.70%) and LDA with ...

Oct 26, 2016 · This paper presents the classification of emotions on EEG signals. One of the key issues in this research is the lack of mental classification ...

May 6, 2024 · This study improves the Convolutional Fuzzy Neural Network (CFNN) for emotion recognition using EEG signals to achieve a reliable detection system.

Support vector machine. (SVM), K-nearest neighbor (KNN) and artificial neural network (ANN) are used to classify emotional states. The cross- validated SVM with ...

Oct 13, 2021 · The best dynamic feature classifier combination was the SVM with temporal features (63.8%), followed by kNN with temporal features (63.70%) and ...

Sep 28, 2023 · Electroencephalogram (EEG) is one of the most reliable physiological signals used for detecting the emotional states of human brain.

This paper reviews the emotional feature extraction methods and classification based on EEG signals proposed in the past five years.