A novel EEG-based emotion recognition approach for e-healthcare applications

Abstract

Electroencephalogram (EEG) signals play an important role in e-healthcare systems, especially to recognise the mental state of patients that could need a special care. This paper presents an EEG-based emotion recognition approach to detect the emotional state of patients for Ambient Assisted Living (AAL). The proposed approach combines wavelet energy, modified energy and wavelet entropy features to classify four different classes of emotions. Three different classifiers are used (quadratic discriminant analysis (QDA), knearest neighbor, and support vector machines) to recognise the emotion of patients robustly. The new approach is tested using the EEG database "DEAP" using four electrodes. It shows high performance compared to existing algorithms. An overall classification accuracy of 83.2% is obtained.

A Review of Emotion Recognition Using Physiological Signals

“…Bimodal Deep AutoEncoder network was used to generate high level features in [ 101 ], where the extracted features were then imported into a linear SVM and the highest accuracy rate of 85.2% was achieved. SVM was also used in [ 127 ]. Unlike [ 101 ], the statistical features such as mean, standard deviation, mean value of first order difference and so on were extracted from five bands of EEG signals to recognize four classes.…”