TY  - JOUR
AU  - Llera, A.
AU  - Gerven, M.A.J. van
AU  - Gomez Cerda, V.
AU  - Jensen, O.
AU  - Kappen, H.J.
PY  - 2011
UR  - https://hdl.handle.net/2066/98171
AB  - We propose an adaptive classification method for the Brain Computer Interfaces (BCI) which uses Interaction Error Potentials (IErrPs) as a reinforcement signal and adapts the classifier parameters when an error is detected. We analyze the quality of the proposed approach in relation to the misclassification of the IErrPs. In addition we compare static versus adaptive classification performance using artificial and MEG data. We show that the proposed adaptive framework significantly improves the static classification methods.
TI  - On the use of interaction error potentials for adaptive brain computer interfaces
EP  - 1127
SN  - 0893-6080
IS  - iss. 10
SP  - 1120
JF  - Neural Networks
VL  - vol. 24
PS  - 8 p.
DO  - https://doi.org/10.1016/j.neunet.2011.05.006
ER  -