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Title: | Segmentation and labelling of EEG for brain computer interfaces |
Authors: | Camilleri, Tracey A. Camilleri, Kenneth P. Fabri, Simon G. |
Keywords: | Electroencephalography Brain-computer interfaces Computer interfaces |
Issue Date: | 2015 |
Publisher: | Springer Verlag |
Citation: | Camilleri, T. A., Camilleri, K. P., & Fabri, S. G. (2015). Segmentation and labelling of EEG for brain computer interfaces. 16th International Conference on Computer Analysis of Images and Patterns, Valletta. 288-299. |
Abstract: | Segmentation and labelling of time series is a common requirement for several applications. A brain computer interface (BCI) is achieved by classification of time intervals of the electroencephalo- graphic (EEG) signal and thus requires EEG signal segmentation and labelling. This work investigates the use of an autoregressive model, extended to a switching multiple modelling framework, to automatically segment and label EEG data into distinct modes of operation that may switch abruptly and arbitrarily in time. The applicability of this app- roach to BCI systems is illustrated on an eye closure dependent BCI and on a motor imagery based BCI. Results show that the proposed autore- gressive switching multiple model approach offers a unified framework of detecting multiple modes, even in the presence of limited training data. |
URI: | https://www.um.edu.mt/library/oar//handle/123456789/18640 |
Appears in Collections: | Scholarly Works - FacEngSCE |
Files in This Item:
File | Description | Size | Format | |
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Chapter - Segmentation and Labelling of EEG for Brain Computer Interfaces.pdf Restricted Access | 888.93 kB | Adobe PDF | View/Open Request a copy |
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