Evolutionary Semi-Supervised Rough Categorization Of Brain Signals From A Wearable Headband

This paper explores the possibility of using distance based semi-supervised learning for creating lower and upper approximations of biometric signals. An evolutionary approach applied to both crisp and rough clustering optimizes both the within cluster scatter and the precision of the classification. The proposed approach is demonstrated through data collected from a wearable headband that recorded EEG brain signals. The brain signals are recorded for a number of participants performing various tasks. The approach identifies medoids that can best identify the participants. The evolutionary semi-supervised crisp and rough clustering is shown to favorably compare with the conventional unsupervised algorithms such as K-means.