A brain computer interface for robust wheelchair control application based on pseudorandom code modulated Visual Evoked Potential
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Standard
A brain computer interface for robust wheelchair control application based on pseudorandom code modulated Visual Evoked Potential. / Mohebbi, Ali; Engelsholm, Signe K.D.; Puthusserypady, Sadasivan; Kjaer, Troels W.; Thomsen, Carsten E.; Sorensen, Helge B.D.
37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC. IEEE, 2015. p. 602-605.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - A brain computer interface for robust wheelchair control application based on pseudorandom code modulated Visual Evoked Potential
AU - Mohebbi, Ali
AU - Engelsholm, Signe K.D.
AU - Puthusserypady, Sadasivan
AU - Kjaer, Troels W.
AU - Thomsen, Carsten E.
AU - Sorensen, Helge B.D.
PY - 2015
Y1 - 2015
N2 - In this pilot study, a novel and minimalistic Brain Computer Interface (BCI) based wheelchair control application was developed. The system was based on pseudorandom code modulated Visual Evoked Potentials (c-VEPs). The visual stimuli in the scheme were generated based on the Gold code, and the VEPs were recognized and classified using subject-specific algorithms. The system provided the ability of controlling a wheelchair model (LEGO® MINDSTORM® EV3 robot) in 4 different directions based on the elicited c-VEPs. Ten healthy subjects were evaluated in testing the system where an average accuracy of 97% was achieved. The promising results illustrate the potential of this approach when considering a real wheelchair application.
AB - In this pilot study, a novel and minimalistic Brain Computer Interface (BCI) based wheelchair control application was developed. The system was based on pseudorandom code modulated Visual Evoked Potentials (c-VEPs). The visual stimuli in the scheme were generated based on the Gold code, and the VEPs were recognized and classified using subject-specific algorithms. The system provided the ability of controlling a wheelchair model (LEGO® MINDSTORM® EV3 robot) in 4 different directions based on the elicited c-VEPs. Ten healthy subjects were evaluated in testing the system where an average accuracy of 97% was achieved. The promising results illustrate the potential of this approach when considering a real wheelchair application.
U2 - 10.1109/EMBC.2015.7318434
DO - 10.1109/EMBC.2015.7318434
M3 - Article in proceedings
C2 - 26736334
AN - SCOPUS:84953247392
SP - 602
EP - 605
BT - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC
PB - IEEE
T2 - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Y2 - 25 August 2015 through 29 August 2015
ER -
ID: 212948236