Adaptive neuro-fuzzy based hybrid classification model for emotion recognition from EEG signals

Emotion recognition using physiological signals has gained significant attention in recent years due to its potential applications in various domains, such as healthcare and entertainment. EEG signals have been particularly useful in emotion recognition due to their non-invasive nature and high temporal resolution. However, the development of accurate and efficient algorithms for emotion classification using EEG signals remains a challenging task. This paper proposes a novel hybrid algorithm for emotion classification based on EEG signals, which combines multiple adaptive network models and probabilistic neural networks. The research aims to improve the recognition accuracy of three and four emotions, which has been a challenge for existing approaches. The proposed model consists of N adaptively neuro-fuzzy inference system (ANFIS) classifiers designed in parallel, in which N is the number of emotion classes. The selected features with the most appropriate distribution for classification are given as input vectors to the ANFIS structures, and the system is trained. The outputs of these trained ANFIS models are combined to create a feature vector, which provides the inputs for adaptive networks, and the system is trained to acquire the emotional recognition output. The performance of the proposed model has been evaluated for classification on well-known emotion benchmark datasets, including DEAP and Feeling Emotions. The study results indicate that the model achieves an accuracy rate of 73.49% on the DEAP datasets and 95.97% on the Feeling Emotions datasets. These results demonstrate that the proposed model efficiently recognizes emotions and exhibits a promising classification performance.