Identifying users and activities from brain wave signals recorded from a wearable headband

Abstract

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 wear-able devices generate a large amount of data and due to their attractive pricing struc-ture are becoming increasingly commonplace. As a result, the data generated from such wearables will increase exponentially, leading to many interesting data mining opportunities. This paper proposes a representation that reduces variable length signals to more manageable and uniformly fixed length distributions, and then explores the effectiveness of a variety of data mining techniques on the biometric signals. 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 experiments use a number of classification and clustering techniques, including decision trees, SVM, neural networks, random forests, K-means clustering, and semi-supervised crisp and rough K-medoid clustering. The results show that it is possible to identify both the persons and the activities with a reasonable degree of precision. Furthermore, for identifying persons the evolutionary semi-supervised crisp and rough K-medoid clustering is shown to favourably compare with the conventional unsupervised algorithms such as K-means.