Abstract
The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG) signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN) as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN) as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN), and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction method is suitable for use in an EEG-based emotion recognition system.
Highlights
IntroductionIt is widely believed that psychological factors can affect a patient’s recovery process; positive emotions, for example, affect the progression of recovery, and a patient’s emotional response to his or her illness could affect the type and amount of medication prescribed by doctors [1]
We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is increased to improve the recognition rate of the system
We have utilized a probabilistic neural network (PNN) as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN), and the results show that the PNN produces a comparable recognition rate and lower computational costs
Summary
It is widely believed that psychological factors can affect a patient’s recovery process; positive emotions, for example, affect the progression of recovery, and a patient’s emotional response to his or her illness could affect the type and amount of medication prescribed by doctors [1]. The difficulty in utilizing the psychological approach in the patient recovery process stems from the patient’s ability to hide emotions or the inability to express an emotional condition despite his or her desires, such as cases where patients experience facial nerve paralysis or adhere to certain cultural dynamics. When these situations occur, even social approaches through various communication techniques or observations of body language still pose a challenge, as the nurse or the patient’s family members might not be able to accompany the patient at all times. A solution can be found in an automatic emotion recognition tool or system
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