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Forschungsdatenbank PMU-SQQUID

HD-EEG Based Classification of Motor-Imagery Related Activity in Patients With Spinal Cord Injury
Holler, Y; Thomschewski, A; Uhl, A; Bathke, AC; Nardone, R; Leis, S; Trinka, E; Holler, P
FRONT NEUROL. 2018; 9: 955
Originalarbeiten (Zeitschrift)


Höller Peter
Höller Yvonne
Leis Stefan
Nardone Raffaele
Thomschewski Aljoscha
Trinka Eugen


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 (

Find related publications in this database (Keywords)

spinal cord injury
motor imagery