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HD-EEG Based Classification of Motor-Imagery Related Activity in Patients With Spinal Cord Injury
VerfasserHöller, Yvonne ; Thomschewski, Aljoscha ; Uhl, Andreas ; Bathke, Arne C. ; Nardone, Raffaele ; Leis, Stefan ; Trinka, Eugen ; Höller, Peter
Erschienen in
Frontiers in Neurology, Lausanne, 2018, Jg. 9, H. article number 955, S. 1-21
ErschienenLausanne : Frontiers, 2018
DokumenttypAufsatz in einer Zeitschrift
Schlagwörter (EN)spinal cord injury / HD-EEG, connectivity / motor imagery / BCI / ffDTF / FFT
URNurn:nbn:at:at-ubs:3-10787 Persistent Identifier (URN)
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HD-EEG Based Classification of Motor-Imagery Related Activity in Patients With Spinal Cord Injury [7.08 mb]
Zusammenfassung (Englisch)

Brain computer interfaces (BCIs) are thought to revolutionize rehabilitation after SCI, e.g., by controlling neuroprostheses, exoskeletons, functional electrical stimulation, or a combination of these components. However, most BCI research was performed in healthy volunteers and it is unknown whether these results can be translated to patients with spinal cord injury because of neuroplasticity. We sought to examine whether high-density EEG (HD-EEG) could improve the performance of motor-imagery classification in patients with SCI. We recorded HD-EEG with 256 channels in 22 healthy controls and 7 patients with 14 recordings (4 patients had more than one recording) in an event related design. Participants were instructed acoustically to either imagine, execute, or observe foot and hand movements, or to rest. We calculated Fast Fourier Transform (FFT) and full frequency directed transfer function (ffDTF) for each condition and classified conditions pairwise with support vector machines when using only 2 channels over the sensorimotor area, full 10-20 montage, high-density montage of the sensorimotor cortex, and full HD-montage. Classification accuracies were comparable between patients and controls, with an advantage for controls for classifications that involved the foot movement condition. Full montages led to better results for both groups (p < 0.001), and classification accuracies were higher for FFT than for ffDTF (p < 0.001), for which the feature vector might be too long. However, full-montage 1020 montage was comparable to high-density configurations. Motor-imagery driven control of neuroprostheses or BCI systems may perform as well in patients as in healthy volunteers with adequate technical configuration. We suggest the use of a whole-head montage and analysis of a broad frequency range.

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