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

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dc.contributor.advisor Lingras, Pawan
dc.creator Wiechert, Glavin
dc.date.accessioned 2017-05-08T13:32:58Z
dc.date.available 2017-05-08T13:32:58Z
dc.date.issued 2016
dc.identifier.uri http://library2.smu.ca/handle/01/26922
dc.description 1 online resource (xii, 82 p.) : ill.
dc.description Includes abstract.
dc.description Includes bibliographical references (p. 79-82).
dc.description.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. en_CA
dc.description.provenance Submitted by Greg Hilliard (greg.hilliard@smu.ca) on 2017-05-08T13:32:58Z No. of bitstreams: 1 Wiechert_Glavin_Honours_2016.pdf: 2168571 bytes, checksum: 570cc5de3b5d9772312f9bc4c55f0627 (MD5) en
dc.description.provenance Made available in DSpace on 2017-05-08T13:32:58Z (GMT). No. of bitstreams: 1 Wiechert_Glavin_Honours_2016.pdf: 2168571 bytes, checksum: 570cc5de3b5d9772312f9bc4c55f0627 (MD5) Previous issue date: 2016-04-27 en
dc.language.iso en en_CA
dc.publisher Halifax, N.S. : Saint Mary's University
dc.title Identifying users and activities from brain wave signals recorded from a wearable headband en_CA
dc.type Text en_CA
thesis.degree.name Bachelor of Science (Honours Computing Science)
thesis.degree.level Undergraduate
thesis.degree.discipline Mathematics and Computing Science
thesis.degree.grantor Saint Mary's University (Halifax, N.S.)
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