Single-trial EEG data to classify type and intensity of facial emotion from P100 and N170

Two aspects of facial emotions are type and intensity level. This study is an attempt to find the relation of electrode location and latencies of EEG data with specific aspects of facial emotions. A neural network was applied to single trial EEG data for classifying the type and intensity level of facial emotions. Images of two types of facial emotions (happiness and disgust) with two intensity levels (high and low) were parametrically controlled and presented as main stimuli to evoke brain signals. Results of this study indicated that data from electrodes over the right-posterior area in the time-range window 80 to 200 ms post stimulus presentation contains P100 and N170 components strongly related to the process of recognition of facial emotions. Relative to other components, N170 was strongly related to the intensity levels of facial emotion, where it could be used for classifying low and high intensity levels of facial emotion. P100 and N170 can be used for classifying the type of facial emotions, with the accuracy when using P100 being slightly higher. Time-range window 200 to 280 ms contains N2, which can also be used for classifying the intensity level of facial emotion, but its accuracy is less than that of N170. Data from time-range window 300 to 360 ms, which contains the P300 component, was not significant for classifying type and intensity level of facial emotion relative to that of other components. This study is the first to show the possibility of using other components besides P300 for EEG-based BCIs. The results also suggest the mechanism of recognition of facial emotion, started by recognizing the type of facial emotion followed by how intense the facial emotion is expressed.