Abstract
In this paper identification of electroencephalogram (EEG) based brain-computer interface (BCI) for motor imagery (MI) task is planned by an efficient adaptive neuro-fuzzy classifier (NFC). The linguistic hedge (LH) is used for proper elicitation and pruning of the fuzzy rules and network is trained using scaled conjugate gradient (SCG) and speeding up SCG (SSCG) techniques. The performance of the proposed classifier, NFCSSCGLH (NFC using SSCG as training algorithm and is powered by LH)is compared with four different NFCs for classifying two class MI-based tasks. We observed a shortening of computation time per iteration by 57.78% in case of SSCG as compared to SCG technique of training. Supremacy of NFCSSCGLH among the considered classifier is validated through Friedman test. Classification result is used to control switching of light emitting diode, turning thoughts into action.
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