Differences in hemodynamic activations between motor imagery and upper limb FES with NIRS
A brain-computer interface (BCI) based on near-infrared spectroscopy (NIRS) could act as a tool for rehabilitation of stroke patients due to the neural activity induced by motor imagery aided by real-time feedback of hemodynamic activation. When combined with functional electrical stimulation (FES)...
| Main Authors: | , , , , , , , , |
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| Format: | Conference Paper |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/99108 http://hdl.handle.net/10220/12618 |
| _version_ | 1826110440277540864 |
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| author | Rana, Mohit Schürholz, Markus Robinson, Neethu Ramos-Murguialday, Ander Rohm, Martin Cho, Woosang Rupp, Rüdiger Birbaumer, Niels Sitaram, Ranganatha |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Rana, Mohit Schürholz, Markus Robinson, Neethu Ramos-Murguialday, Ander Rohm, Martin Cho, Woosang Rupp, Rüdiger Birbaumer, Niels Sitaram, Ranganatha |
| author_sort | Rana, Mohit |
| collection | NTU |
| description | A brain-computer interface (BCI) based on near-infrared spectroscopy (NIRS) could act as a tool for rehabilitation of stroke patients due to the neural activity induced by motor imagery aided by real-time feedback of hemodynamic activation. When combined with functional electrical stimulation (FES) of the affected limb, BCI is expected to have an even greater benefit due to the contingency established between motor imagery and afferent, haptic feedback from stimulation. Yet, few studies have explored such an approach, presumably due to the difficulty in dissociating and thus decoding the hemodynamic response (HDR) between motor imagery and peripheral stimulation. Here, for the first time, we demonstrate that NIRS signals elicited by motor imagery can be reliably discriminated from those due to FES, by first performing a univariate analysis of the NIRS signals, and subsequently by multivariate pattern classification. Our results showing that robust classification of motor imagery from the rest condition is possible support previous findings that imagery could be used to drive a BCI based on NIRS. More importantly, we demonstrate for the first time the successful classification of motor imagery and FES, indicating that it is technically feasible to implement a contingent NIRS-BCI with FES. |
| first_indexed | 2024-10-01T02:34:27Z |
| format | Conference Paper |
| id | ntu-10356/99108 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:34:27Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/991082020-05-28T07:17:31Z Differences in hemodynamic activations between motor imagery and upper limb FES with NIRS Rana, Mohit Schürholz, Markus Robinson, Neethu Ramos-Murguialday, Ander Rohm, Martin Cho, Woosang Rupp, Rüdiger Birbaumer, Niels Sitaram, Ranganatha School of Computer Engineering Annual International Conference of the IEEE Engineering in Medicine and Biology Society (34th : 2012 : San Diego, USA) DRNTU::Engineering::Computer science and engineering A brain-computer interface (BCI) based on near-infrared spectroscopy (NIRS) could act as a tool for rehabilitation of stroke patients due to the neural activity induced by motor imagery aided by real-time feedback of hemodynamic activation. When combined with functional electrical stimulation (FES) of the affected limb, BCI is expected to have an even greater benefit due to the contingency established between motor imagery and afferent, haptic feedback from stimulation. Yet, few studies have explored such an approach, presumably due to the difficulty in dissociating and thus decoding the hemodynamic response (HDR) between motor imagery and peripheral stimulation. Here, for the first time, we demonstrate that NIRS signals elicited by motor imagery can be reliably discriminated from those due to FES, by first performing a univariate analysis of the NIRS signals, and subsequently by multivariate pattern classification. Our results showing that robust classification of motor imagery from the rest condition is possible support previous findings that imagery could be used to drive a BCI based on NIRS. More importantly, we demonstrate for the first time the successful classification of motor imagery and FES, indicating that it is technically feasible to implement a contingent NIRS-BCI with FES. 2013-07-31T06:24:58Z 2019-12-06T20:03:29Z 2013-07-31T06:24:58Z 2019-12-06T20:03:29Z 2012 2012 Conference Paper https://hdl.handle.net/10356/99108 http://hdl.handle.net/10220/12618 10.1109/EMBC.2012.6347023 en |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Rana, Mohit Schürholz, Markus Robinson, Neethu Ramos-Murguialday, Ander Rohm, Martin Cho, Woosang Rupp, Rüdiger Birbaumer, Niels Sitaram, Ranganatha Differences in hemodynamic activations between motor imagery and upper limb FES with NIRS |
| title | Differences in hemodynamic activations between motor imagery and upper limb FES with NIRS |
| title_full | Differences in hemodynamic activations between motor imagery and upper limb FES with NIRS |
| title_fullStr | Differences in hemodynamic activations between motor imagery and upper limb FES with NIRS |
| title_full_unstemmed | Differences in hemodynamic activations between motor imagery and upper limb FES with NIRS |
| title_short | Differences in hemodynamic activations between motor imagery and upper limb FES with NIRS |
| title_sort | differences in hemodynamic activations between motor imagery and upper limb fes with nirs |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/99108 http://hdl.handle.net/10220/12618 |
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