ABSTRACT
This chapter explores the use of supervised and unsupervised learning techniques to identify individuals and activities using a commercially available wearable headband. It illustrates a method of creating a compact representation of the data streams from multiple channels without losing the essence of the patterns within the data. The chapter also explores the usage of unsupervised and semi-supervised learning techniques with the proposed data representation. It investigates the collection of electroencephelogram (EEG) brain signals from a commercially available wearable, the Muse headband. The chapter demonstrates the viability of supervised, unsupervised, and semi-supervised learning techniques for identifying individuals and activities based on EEG data collected from a commercially available wearable headband. It is shown that the various classification techniques are effective in predicting persons and activities, and that various clustering techniques also provide reasonable results. Finally, the chapter shows that by using histograms of EEG brain signals, one can apply a wide variety of data mining techniques.
