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An adaptive brain actuated system for augmenting rehabilitation.

Roset SA, Gant K, Prasad A, Sanchez JC - Front Neurosci (2014)

Bottom Line: For people living with paralysis, restoration of hand function remains the top priority because it leads to independence and improvement in quality of life.In approaches to restore hand and arm function, a goal is to better engage voluntary control and counteract maladaptive brain reorganization that results from non-use.By continuously adapting to the user's brain activity, similar adaptive systems could be used to support clinical brain-computer interface neurorehabilitation over multiple days.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, University of Miami Coral Gables, FL, USA.

ABSTRACT
For people living with paralysis, restoration of hand function remains the top priority because it leads to independence and improvement in quality of life. In approaches to restore hand and arm function, a goal is to better engage voluntary control and counteract maladaptive brain reorganization that results from non-use. Standard rehabilitation augmented with developments from the study of brain-computer interfaces could provide a combined therapy approach for motor cortex rehabilitation and to alleviate motor impairments. In this paper, an adaptive brain-computer interface system intended for application to control a functional electrical stimulation (FES) device is developed as an experimental test bed for augmenting rehabilitation with a brain-computer interface. The system's performance is improved throughout rehabilitation by passive user feedback and reinforcement learning. By continuously adapting to the user's brain activity, similar adaptive systems could be used to support clinical brain-computer interface neurorehabilitation over multiple days.

No MeSH data available.


Related in: MedlinePlus

ErrP for SCI and control. Averaged across all trials and error-minus-correct for the SCI and control subjects.
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Figure 5: ErrP for SCI and control. Averaged across all trials and error-minus-correct for the SCI and control subjects.

Mentions: A similar process was used for inputs to the critic. The first row of Figure 4 shows the filtered, 1–12 Hz, EEG from the Cz electrode for the 0.15–0.70 s after the feedback was shown. PSD of the raw EEG was computed from the Cz electrode, shown in the second row. Finally, the inputs to the critic are shown in the third row as z-scores of the PSD from the Cz electrode. The first column shows the filtered EEG and processing after the feedback of “correct” was presented. The second column shows the filtered EEG and processing for feedback of “error.” Notice that the error potential has a biphasic shape characteristic of this neural oscillation. The features for feedback of “correct” correspond to lower power, in general, compared to features of “error;” in the sample trial for the SCI subject, all 1 Hz bins except 1, 8, 11, and 12 Hz. Figure 5 shows the ErrPs generated by the users, the average of error trials minus the average of correct trials. The ErrPs collected from the users are similar to published results (Ferrez and Millan, 2008). Figure 6 shows the averaged PSD for the cue of “open” for both the SCI and control subjects averaged across all trials. The averaged PSD changes after the cue relative to the averaged PSD before the cue.


An adaptive brain actuated system for augmenting rehabilitation.

Roset SA, Gant K, Prasad A, Sanchez JC - Front Neurosci (2014)

ErrP for SCI and control. Averaged across all trials and error-minus-correct for the SCI and control subjects.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4264503&req=5

Figure 5: ErrP for SCI and control. Averaged across all trials and error-minus-correct for the SCI and control subjects.
Mentions: A similar process was used for inputs to the critic. The first row of Figure 4 shows the filtered, 1–12 Hz, EEG from the Cz electrode for the 0.15–0.70 s after the feedback was shown. PSD of the raw EEG was computed from the Cz electrode, shown in the second row. Finally, the inputs to the critic are shown in the third row as z-scores of the PSD from the Cz electrode. The first column shows the filtered EEG and processing after the feedback of “correct” was presented. The second column shows the filtered EEG and processing for feedback of “error.” Notice that the error potential has a biphasic shape characteristic of this neural oscillation. The features for feedback of “correct” correspond to lower power, in general, compared to features of “error;” in the sample trial for the SCI subject, all 1 Hz bins except 1, 8, 11, and 12 Hz. Figure 5 shows the ErrPs generated by the users, the average of error trials minus the average of correct trials. The ErrPs collected from the users are similar to published results (Ferrez and Millan, 2008). Figure 6 shows the averaged PSD for the cue of “open” for both the SCI and control subjects averaged across all trials. The averaged PSD changes after the cue relative to the averaged PSD before the cue.

Bottom Line: For people living with paralysis, restoration of hand function remains the top priority because it leads to independence and improvement in quality of life.In approaches to restore hand and arm function, a goal is to better engage voluntary control and counteract maladaptive brain reorganization that results from non-use.By continuously adapting to the user's brain activity, similar adaptive systems could be used to support clinical brain-computer interface neurorehabilitation over multiple days.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, University of Miami Coral Gables, FL, USA.

ABSTRACT
For people living with paralysis, restoration of hand function remains the top priority because it leads to independence and improvement in quality of life. In approaches to restore hand and arm function, a goal is to better engage voluntary control and counteract maladaptive brain reorganization that results from non-use. Standard rehabilitation augmented with developments from the study of brain-computer interfaces could provide a combined therapy approach for motor cortex rehabilitation and to alleviate motor impairments. In this paper, an adaptive brain-computer interface system intended for application to control a functional electrical stimulation (FES) device is developed as an experimental test bed for augmenting rehabilitation with a brain-computer interface. The system's performance is improved throughout rehabilitation by passive user feedback and reinforcement learning. By continuously adapting to the user's brain activity, similar adaptive systems could be used to support clinical brain-computer interface neurorehabilitation over multiple days.

No MeSH data available.


Related in: MedlinePlus