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Selection of temporal features for the detection of movement intention in patients with amyotrophic lateral sclerosis

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  • S. Aliakbaryhosseinabadi, Aalborg University
  • ,
  • S. Dosen, Aalborg University
  • ,
  • J. Blicher
  • N. Mrachacz-Kersting, Dortmund University

Detection of movement intention is a critical step for the development of rehabilitation systems and the induction of plasticity using brain-computer interfacing. The movement-related cortical potential (MRCP), obtained from electroencephalography (EEG) signals, is an attractive modality for movement detection as it can be generated 1-2 s prior to the movement execution. In the present study, monopolar EEG signals were recorded from ten channels in five Amyotrophic Lateral Sclerosis (ALS) patients and ten healthy participants while performing hand movement (hand extension/flexion). Movements were detected offline by using classification (Support Vector Machine) between movement and rest epochs with three different groups of time-domain features. The first group of features were time samples of filtered down-sampled EEG (raw features) while the second group (computed features) included the features calculated from extracted MRCPs and rest epochs (e.g., slope, peak negativity and variations in different time segments). In the third condition, the two groups of features were combined. The results revealed that detection accuracy obtained from raw features (88± 3%) was higher than either computed features (83± 3%) or a combination of the two (84± 3%). Therefore, the time samples of EEG signals seem to be a better choice for movement detection using MRCPs.

Original languageEnglish
Title of host publication2021 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
Number of pages4
Publication year2021
ISBN (Electronic)9781728143378
Publication statusPublished - 2021
Event10th International IEEE/EMBS Conference on Neural Engineering, NER 2021 - Virtual, Online, Italy
Duration: 4 May 20216 May 2021


Conference10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
ByVirtual, Online
SponsorBlackrock Microsystems, et al, Instituto Italiano di Tecnologia, Medtronic, The Biorobotics Institute, Sant'Anna School of Advanced Studies - Pisa, University of Houston, Cullen College of Engineering, Department of Biomedical Engineering
SeriesInternational IEEE/EMBS Conference on Neural Engineering, NER

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