Publications

We propose an efficient mixture classification technique, which uses electroencephalography (EEG) signals for establishing a communication channel for the physically challenged or immobilized people, by the usage of the brain signals. In order to identify the emotion expressions by an immobilized person, we introduce a novel approach for emotion recognition based on the generalized mixture distribution model. The main benefit of utilizing this model is that it is an asymmetric distribution, which helps to extract the EEG signals, which are either in symmetric or asymmetric form. The skew Gaussian distribution helps to identify the small duration EEG signal sample and helps toward better recognition of emotions in both clean and noisy EEG signals. The proposed method is particularly well suited for the high variability of the EEG signal allowing the emotions to be identified appropriately.