TY - GEN
T1 - Eyelid Movement Command Classification Using Machine Learning
AU - Graybill, Philip P.
AU - Kiani, Mehdi
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - The Eyelid Drive System (EDS) is an assistive technology device intended to allow users to wirelessly control other devices, such as power wheelchairs and personal computers, using commands consisting only of blinking and winking. In this paper, four machine learning classifiers are trained on data taken from one subject and validated offline on the training subject plus two additional subjects. The classifiers are assessed for accuracy, computational and memory requirements, and transferability from the "training" subject to the other two subjects. A support vector machine (SVM) achieved the highest level of accuracy (97.5%) while using a potentially prohibitive level of computational and memory resources. A logistic regression classifier also achieved excellent accuracy (96.5%) while using two to three orders of magnitude fewer computational and memory resources than the SVM.
AB - The Eyelid Drive System (EDS) is an assistive technology device intended to allow users to wirelessly control other devices, such as power wheelchairs and personal computers, using commands consisting only of blinking and winking. In this paper, four machine learning classifiers are trained on data taken from one subject and validated offline on the training subject plus two additional subjects. The classifiers are assessed for accuracy, computational and memory requirements, and transferability from the "training" subject to the other two subjects. A support vector machine (SVM) achieved the highest level of accuracy (97.5%) while using a potentially prohibitive level of computational and memory resources. A logistic regression classifier also achieved excellent accuracy (96.5%) while using two to three orders of magnitude fewer computational and memory resources than the SVM.
UR - http://www.scopus.com/inward/record.url?scp=85077894079&partnerID=8YFLogxK
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U2 - 10.1109/EMBC.2019.8857766
DO - 10.1109/EMBC.2019.8857766
M3 - Conference contribution
C2 - 31946664
AN - SCOPUS:85077894079
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3637
EP - 3640
BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Y2 - 23 July 2019 through 27 July 2019
ER -