Abstract
Current EEG based brain computer interface (BCI) systems have achieved successful control in up to 3 dimensions; however the current paradigm may be unnatural for many rehabilitative and recreational applications. Therefore there is a great need to find motor imagination (MI) tasks that are realistic for output device control. In this paper we present our results on classifying hand gesture MI tasks, including right hand flexion, extension, supination and pronation using a novel EEG inverse imaging approach. By using both temporal and spatial specificity in the source domain we were able to separate MI tasks with up to 95% accuracy for binary classification of any two tasks compared to a maximum of only 79% in the sensor domain.
Original language | English (US) |
---|---|
Title of host publication | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1314-1317 |
Number of pages | 4 |
ISBN (Electronic) | 9781424479290 |
DOIs | |
State | Published - Nov 2 2014 |
Event | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States Duration: Aug 26 2014 → Aug 30 2014 |
Publication series
Name | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 |
---|
Other
Other | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 |
---|---|
Country/Territory | United States |
City | Chicago |
Period | 8/26/14 → 8/30/14 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.