Article

Discrimination of Motor Imagery-Induced EEG Patterns in Patients with Complete Spinal Cord Injury

Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria.
Computational Intelligence and Neuroscience (Impact Factor: 0.6). 02/2009; 2009:104180. DOI: 10.1155/2009/104180
Source: PubMed

ABSTRACT

EEG-based discrimination between different motor imagery states has been subject of a number of studies in healthy subjects. We investigated the EEG of 15 patients with complete spinal cord injury during imagined right hand, left hand, and feet movements. In detail we studied pair-wise discrimination functions between the 3 types of motor imagery. The following classification accuracies (mean +/- SD) were obtained: left versus right hand 65.03% +/- 8.52, left hand versus feet 68.19% +/- 11.08, and right hand versus feet 65.05% +/- 9.25. In 5 out of 8 paralegic patients, the discrimination accuracy was greater than 70% but in only 1 out of 7 tetraplagic patients. The present findings provide evidence that in the majority of paraplegic patients an EEG-based BCI could achieve satisfied results. In tetraplegic patients, however, it is expected that extensive training-sessions are necessary to achieve a good BCI performance at least in some subjects.

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    • "In another study, authors compared the BCI performance of 15 end users with complete SCI, eight of them paraplegic and seven tetraplegic (Pfurtscheller et al., 2009). It was found that five of the paraplegic individuals had a mean accuracy above 70% but only one tetraplegic person achieved this performance level. "
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    • "Firstly, although the larger magnitude might probably improve the BCI performance in SCI patients, the prolonged rebound should be treated carefully with a long interval between trials. On the other hand, SCI PNP have weaker ERD than the able-bodied volunteers (Vuckovic et al., 2014), resulting in reduced BCI classification accuracy (Pfurtscheller et al., 2009). This implies that for SCI patients, BCI systems which relay on MRCP might have better classification accuracy, with greater consistency among patients. "
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    • "This is even though our system provided online feedback after only minutes of auto-calibration, using only two sensors. In comparison, the high classification efficacy in [20] was achieved by offline analysis, using sophisticated spatial filtering (CSP) after manual outlier rejection on data recorded from 15 electrodes. "
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