Detection and classification of tongue movements from single-trial EEG

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Abstrakt

: To detect and classify tongue movements from single trial electroencephalography (EEG), so that it can be used as a reliable control signal in a brain computer interface (BCI). Method: Thirteen subjects, all BCI-naïve, performed four different tongue movements (up, down, left and right), which was detected against an idle state using a common spatial pattern filter with a linear discriminant analysis classifier. Furthermore, the movement types were classified in a one-versus all classification scheme. Results: On average, 72-76% of the movements were detected correctly against the idle state. When all movement types were pooled and detected against the idle state, an accuracy of 80% was obtained. A closer investigation showed that the system correctly detected up to 83% of the executed movements, but had a false positive rate of 13%. The movements were classified with an accuracy of 43%. This was increased to 55% when only left, right and up movements were considered. When only left and right movements where considered they were classified with an average accuracy of 71%. Conclusion: Decoding of tongue movements from the EEG can be used as a reliable control state switch in a BCI and is possible to classify the different movements above chance level. Significance: Residual tongue movements, which is not lost after a spinal cord injury, can be used as a reliable control state switch and it is possibly to detect at least four different movement types.
OriginalsprogEngelsk
Titel The 20th IEEE International Conference on BioInformatics And BioEngineering
Antal sider5
ForlagIEEE Computer Society Press
Sider1
StatusAccepteret/In press - 10 aug. 2020

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  • Citationsformater

    Kæseler, R. L., Struijk, L. N. S. A., & Jochumsen, M. (Accepteret/In press). Detection and classification of tongue movements from single-trial EEG. I The 20th IEEE International Conference on BioInformatics And BioEngineering (s. 1). IEEE Computer Society Press.