RT Journal Article
T1 Neuromuscular electrical stimulation induced brain patterns to decode motor imagery
A1 Vidaurre, C.
A1 Pascual, J.
A1 Ramos-Murguialday, A.
A1 Lorenz, R.
A1 Blankertz, B.
A1 Birbaumer, N.
A1 Müller, K. R.
AB Objective: Regardless of the paradigm used to implement a brain-computer interface (BCI), all systems suffer from BCI-inefficiency. In the case of patients the inefficiency can be high. Some solutions have been proposed to overcome this problem, however they have not been completely successful yet. Methods: EEG from 10 healthy users was recorded during neuromuscular electrical stimulation (NMES) of hands and feet and during motor imagery (MI) of the same limbs. Features and classifiers were computed using part of these data to decode MI. Results: Offline analyses showed that it was possible to decode MI using a classifier based on afferent patterns induced by NMES and even infer a better model than with MI data. Conclusion: Afferent NMES motor patterns can support the calibration of BCI systems and be used to decode MI. Significance: This finding might be a new way to train sensorimotor rhythm (SMR) based BCI systems for healthy users having difficulties to attain BCI control. It might also be an alternative to train MI-based BCIs for users who cannot perform real movements but have remaining afferents (ALS, stroke patients).
SN 1388-2457
YR 2013
FD 2013-09
LK https://hdl.handle.net/11556/3813
UL https://hdl.handle.net/11556/3813
LA eng
NO Vidaurre , C , Pascual , J , Ramos-Murguialday , A , Lorenz , R , Blankertz , B , Birbaumer , N & Müller , K R 2013 , ' Neuromuscular electrical stimulation induced brain patterns to decode motor imagery ' , Clinical Neurophysiology , vol. 124 , no. 9 , pp. 1824-1834 . https://doi.org/10.1016/j.clinph.2013.03.009
NO We gratefully acknowledge Christian Klauer and Thomas Schauer for letting us use part of their NMES hardware and software and a room to perform the experiments. We thank Stefan Haufe for the production of Fig. 6 . The research leading to these results has received funding from the European Community’s Seventh Framework Program under grant MUNDUS and HUMOUR-ICT-2008-231724, the European Union’s PASCAL 2 Network of Excellence (ICT-216886), the German Government under grants MU 987/3-2, the Bernstein Focus: Neurotechnology BMBF 01GQ0831 and BMBF 01GQ0850, the German Research Foundation DFG project KU 1453-1 and the World Class University Program through the National Research Foundation of Korea funded by the Ministry of Education, Science, and Technology , under Grant R31-10008. This publication only reflects the authors’ views.
DS TECNALIA Publications
RD 26 jul 2024