NDLI: Robustness of movement detection techniques from motor execution: Single trial movement related cortical potential

Content Provider	IEEE Xplore Digital Library
Author	Aliakbaryhosseinabadi, S. Jiang, N. Petrini, L. Farina, D. Dremstrup, K. Mrachacz-Kersting, N.
Copyright Year	2015
Description	Author affiliation: Dept. of Neurorehabilitation Eng., Univ. Med. Center Gottingen, Gottingen, Germany (Jiang, N.; Farina, D.) \|\| Dept. of Health Sci. & Technol., Aalborg Univ., Aalborg, Denmark (Aliakbaryhosseinabadi, S.; Petrini, L.; Dremstrup, K.; Mrachacz-Kersting, N.)
Abstract	Alterations in attention are known to modify excitability of underlying cortical structures and thus the activity recorded during non-invasive electroencephalography (EEG). Brain-Computer-Interface systems for neuromodulation are based on reliable detection of intended movements from continuous EEG signals, thereby generating real time feedback to induce neuroplasticity. We have shown in past studies that the movement related cortical potential (MRCP), a slow negative potential commencing 1-2 s prior to movement, can be reliably detected in real time within a single session and used to drive an external device that reproduces the intended movement. In the present study, our main objective was to characterize movement detection performance of two algorithms, when the subject's attention is altered. In nine healthy participants the auditory oddball paradigm was used to modulate attention. All subjects completed a set of movement executions prior to and following the oddball paradigm. The locality preserving projection followed by the linear discriminant analysis (LPP-LDA) and the matched-filter (MF) technique were applied offline for detection of movement. Results show that LPP-LDA significantly outperformed MF. The robustness of the LPP-LDA method was demonstrated by a higher true positive rate (TPR), lower false positive rate (FPR) and a shorter detection latency when attention levels were altered.
Starting Page	13
Ending Page	16
File Size	640961
Page Count	4
File Format	PDF
ISBN	9781467363891
DOI	10.1109/NER.2015.7146548
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-22
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electric potential Neuroscience Accuracy Feature extraction Electroencephalography Robustness Brain-computer interfaces
Content Type	Text
Resource Type	Article

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