EEG-based emotion recognition approach for e-healthcare applications

Emotions play an extremely important role in how we make a decision, planning, reasoning and other human mental states. The recognition of these emotions is becoming a vital task for e-healthcare systems. Using bio-sensors such as Electroencephalogram (EEG) to recognise the mental state of patients that could need a special care offers an important feedback for Ambient Assisted Living (AAL). This paper presents an EEG-based emotion recognition approach to detect the emotional state of patients. The proposed approach combines wavelet energy, modified energy, wavelet entropy and statistical features to classify four emotion states. Three different classifiers are used (quadratic discriminant analysis, k-nearest neighbor, and support vector machines) to recognise the emotion of patients robustly. The new approach is tested based on the EEG database “DEAP” using four electrodes. It shows high performance compared to existing algorithms. An overall classification accuracy of 83.87% is obtained.