Emotion Recognition in EEG Signals with Wavelet Transform and CNN
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https://doi.org/10.5281/zenodo.15088482Keywords:
Electroencephalography, machine learning, emotion recognition, convolutional neural networkAbstract
In this study, different Wavelet Transform methods were used. Emotion recognition was performed on EEG signals using artificial neural networks and convolutional neural networks with the features obtained by using Wavelet Transform coefficients. A dataset of EEG signals belonging to three different emotions taken from four people was used. It was used to classify stressful, neutral and relaxed emotions. By comparing the results obtained with Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) for 1D and 2D and Synchrosqueezed Wavelet Transform (SSWT), an appropriate wavelet transform was tried to be determined for emotion recognition on EEG signals. It was found that Synchrosqueezed Wavelet Transform (SSWT) was the most effective algorithm for emotion classification with the highest accuracy, precision, sensitivity, specificity and F1-score.
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