Human Emotion Recognition using Time-Frequency Analysis of EEG Signals

Human emotion recognition has been a challenging task for researchers due to the complexity of emotions and the lack of a clear definition of what constitutes an emotion. However, with the advancement of technology, researchers have been able to explore the use of electroencephalography (EEG) signals to identify different emotions.

One approach to emotion recognition is the analysis of time-frequency features extracted from multivariate EEG signals. The EEG signals are recorded from multiple electrodes placed on the scalp, and the time-frequency features are extracted using signal processing techniques such as short-time Fourier transform (STFT) or wavelet transform.

The time-frequency features capture the changes in EEG power across different frequency bands over time, which are associated with specific emotional states. For example, studies have shown that increased power in the beta frequency band (13-30 Hz) is associated with positive emotions such as happiness, while increased power in the alpha frequency band (8-12 Hz) is associated with negative emotions such as fear.

Machine learning algorithms such as support vector machines (SVM) or artificial neural networks (ANN) can be trained using the extracted time-frequency features to classify different emotional states. The performance of these algorithms can be evaluated using metrics such as accuracy, sensitivity, and specificity.

Overall, the analysis of time-frequency features extracted from multivariate EEG signals shows promising results for human emotion recognition. However, further research is required to improve the accuracy and robustness of these techniques, particularly in real-world scenarios where EEG signals can be affected by noise and artifacts.