Classification of finger vibrotactile input using scalp EEG

Yongtian He; Jose L. Contreras Vidal

doi:10.1109/EMBC.2015.7319447

Classification of finger vibrotactile input using scalp EEG

Yongtian He, Jose L. Contreras Vidal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

While there are many output brain-computer interface (output BCIs) studies, few have examined the input pathway, namely decoding the sensory input. To examine the possibility of building a BCI with sensory input using scalp electroencephalography (EEG), this study builds a classifier based on Local Fisher Discriminant Analysis (LFDA) and Gaussian Mixture Model (GMM) to classify neural activity generated by vibrotactile sensory stimuli delivered to the fingers. Small vibrators were placed on the fingertips of the participant. They vibrated one by one in a random sequence while the participant sat still with eyes closed. EEG data were recorded and later used to classify which finger was vibrated. There were two tasks: one focusing on differentiating between ipsilateral fingers, the other one focusing on differentiating contralateral fingers. Decoding accuracies were high in both tasks: 97.6% and 99.3% respectively. Event-related EEG features in both amplitude and power domain are discussed.

Original language	English (US)
Title of host publication	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4717-4720
Number of pages	4
Volume	2015-November
ISBN (Print)	9781424492718
DOIs	https://doi.org/10.1109/EMBC.2015.7319447
State	Published - Nov 4 2015
Event	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy Duration: Aug 25 2015 → Aug 29 2015

Other

Other	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Country/Territory	Italy
City	Milan
Period	8/25/15 → 8/29/15

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Signal Processing
Biomedical Engineering
Health Informatics

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10.1109/EMBC.2015.7319447

Cite this

Classification of finger vibrotactile input using scalp EEG. / He, Yongtian; Contreras Vidal, Jose L.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Vol. 2015-November Institute of Electrical and Electronics Engineers Inc., 2015. p. 4717-4720 7319447.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

He, Y & Contreras Vidal, JL 2015, Classification of finger vibrotactile input using scalp EEG. in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. vol. 2015-November, 7319447, Institute of Electrical and Electronics Engineers Inc., pp. 4717-4720, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, Milan, Italy, 8/25/15. https://doi.org/10.1109/EMBC.2015.7319447

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abstract = "While there are many output brain-computer interface (output BCIs) studies, few have examined the input pathway, namely decoding the sensory input. To examine the possibility of building a BCI with sensory input using scalp electroencephalography (EEG), this study builds a classifier based on Local Fisher Discriminant Analysis (LFDA) and Gaussian Mixture Model (GMM) to classify neural activity generated by vibrotactile sensory stimuli delivered to the fingers. Small vibrators were placed on the fingertips of the participant. They vibrated one by one in a random sequence while the participant sat still with eyes closed. EEG data were recorded and later used to classify which finger was vibrated. There were two tasks: one focusing on differentiating between ipsilateral fingers, the other one focusing on differentiating contralateral fingers. Decoding accuracies were high in both tasks: 97.6% and 99.3% respectively. Event-related EEG features in both amplitude and power domain are discussed.",

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Classification of finger vibrotactile input using scalp EEG

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