Identifying users and activities with cognitive signal processing 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 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.