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Non-motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment.

Faller J, Scherer R, Friedrich EV, Costa U, Opisso E, Medina J, Müller-Putz GR - Front Neurosci (2014)

Bottom Line: Auto-calibrating and adaptive ERD-based BCIs that users control with motor imagery tasks ("SMR-AdBCI") have proven effective for healthy users.We then simulated both, the SMR-AdBCI and the Auto-AdBCI configuration on the unseen data from the nine participants in Session 2 and compared the results.On the unseen data of Session 2 from individuals with SCI or stroke, we found that automatically selecting a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration (Auto-AdBCI) significantly (p < 0.01) improved classification performance compared to an adaptive ERD-based BCI that only used motor imagery tasks (SMR-AdBCI; average accuracy of 75.7 vs. 66.3%).

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology Graz, Austria.

ABSTRACT
Individuals with severe motor impairment can use event-related desynchronization (ERD) based BCIs as assistive technology. Auto-calibrating and adaptive ERD-based BCIs that users control with motor imagery tasks ("SMR-AdBCI") have proven effective for healthy users. We aim to find an improved configuration of such an adaptive ERD-based BCI for individuals with severe motor impairment as a result of spinal cord injury (SCI) or stroke. We hypothesized that an adaptive ERD-based BCI, that automatically selects a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration ("Auto-AdBCI") could allow for higher control performance than a conventional SMR-AdBCI. To answer this question we performed offline analyses on two sessions (21 data sets total) of cue-guided, five-class electroencephalography (EEG) data recorded from individuals with SCI or stroke. On data from the twelve individuals in Session 1, we first identified three bipolar derivations for the SMR-AdBCI. In a similar way, we determined three bipolar derivations and four mental tasks for the Auto-AdBCI. We then simulated both, the SMR-AdBCI and the Auto-AdBCI configuration on the unseen data from the nine participants in Session 2 and compared the results. On the unseen data of Session 2 from individuals with SCI or stroke, we found that automatically selecting a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration (Auto-AdBCI) significantly (p < 0.01) improved classification performance compared to an adaptive ERD-based BCI that only used motor imagery tasks (SMR-AdBCI; average accuracy of 75.7 vs. 66.3%).

No MeSH data available.


Related in: MedlinePlus

The selected bipolar derivations for the SMR-AdBCI and the Auto-AdBCI system. The annotated numbers show the ranking of the bipolars, with number one performing the best.
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Figure 5: The selected bipolar derivations for the SMR-AdBCI and the Auto-AdBCI system. The annotated numbers show the ranking of the bipolars, with number one performing the best.

Mentions: In the analyses on the data of Session 1 we identified the bipolar derivations at Cz (FCz-CPz), Pz (P1-P2), and P4 (CP4-PO4) (see Figure 5) to produce the highest accuracy. Over these three selected channels we further found the mental tasks Math, Feet, Hand, and Word to perform best, leading us to reject class Nav. When limiting the classes to Hand, and Feet for the SMR-AdBCI we identified the bipolar derivations C3-CP3, again Cz (FCz-CPz), and CP4-P4 (see Figure 5) to produce the highest accuracy.


Non-motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment.

Faller J, Scherer R, Friedrich EV, Costa U, Opisso E, Medina J, Müller-Putz GR - Front Neurosci (2014)

The selected bipolar derivations for the SMR-AdBCI and the Auto-AdBCI system. The annotated numbers show the ranking of the bipolars, with number one performing the best.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4196541&req=5

Figure 5: The selected bipolar derivations for the SMR-AdBCI and the Auto-AdBCI system. The annotated numbers show the ranking of the bipolars, with number one performing the best.
Mentions: In the analyses on the data of Session 1 we identified the bipolar derivations at Cz (FCz-CPz), Pz (P1-P2), and P4 (CP4-PO4) (see Figure 5) to produce the highest accuracy. Over these three selected channels we further found the mental tasks Math, Feet, Hand, and Word to perform best, leading us to reject class Nav. When limiting the classes to Hand, and Feet for the SMR-AdBCI we identified the bipolar derivations C3-CP3, again Cz (FCz-CPz), and CP4-P4 (see Figure 5) to produce the highest accuracy.

Bottom Line: Auto-calibrating and adaptive ERD-based BCIs that users control with motor imagery tasks ("SMR-AdBCI") have proven effective for healthy users.We then simulated both, the SMR-AdBCI and the Auto-AdBCI configuration on the unseen data from the nine participants in Session 2 and compared the results.On the unseen data of Session 2 from individuals with SCI or stroke, we found that automatically selecting a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration (Auto-AdBCI) significantly (p < 0.01) improved classification performance compared to an adaptive ERD-based BCI that only used motor imagery tasks (SMR-AdBCI; average accuracy of 75.7 vs. 66.3%).

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology Graz, Austria.

ABSTRACT
Individuals with severe motor impairment can use event-related desynchronization (ERD) based BCIs as assistive technology. Auto-calibrating and adaptive ERD-based BCIs that users control with motor imagery tasks ("SMR-AdBCI") have proven effective for healthy users. We aim to find an improved configuration of such an adaptive ERD-based BCI for individuals with severe motor impairment as a result of spinal cord injury (SCI) or stroke. We hypothesized that an adaptive ERD-based BCI, that automatically selects a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration ("Auto-AdBCI") could allow for higher control performance than a conventional SMR-AdBCI. To answer this question we performed offline analyses on two sessions (21 data sets total) of cue-guided, five-class electroencephalography (EEG) data recorded from individuals with SCI or stroke. On data from the twelve individuals in Session 1, we first identified three bipolar derivations for the SMR-AdBCI. In a similar way, we determined three bipolar derivations and four mental tasks for the Auto-AdBCI. We then simulated both, the SMR-AdBCI and the Auto-AdBCI configuration on the unseen data from the nine participants in Session 2 and compared the results. On the unseen data of Session 2 from individuals with SCI or stroke, we found that automatically selecting a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration (Auto-AdBCI) significantly (p < 0.01) improved classification performance compared to an adaptive ERD-based BCI that only used motor imagery tasks (SMR-AdBCI; average accuracy of 75.7 vs. 66.3%).

No MeSH data available.


Related in: MedlinePlus