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This paper studies the supervised classification of electroencephalogram (EEG) brain signals to identify persons and their activities. The brain signals are obtained from a commercially available and modestly priced wearable headband. Such wearable devices generate a large amount of data and due to their attractive pricing structure are becoming increasingly commonplace. As a result, the data generated from such wearables will increase exponentially leading to many interesting data mining opportunities. We propose a representation that reduces variable length signals to a more manageable and uniformly fixed length distributions. These fixed length distributions can then be used with a variety of data mining techniques. The experiments with a number of classification techniques, including decision trees, SVM, neural networks, and random forests show that it is possible to identify both the persons and the activities with a reasonable degree of precision.

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.