Gripping-force identification using EEG and phase-demodulation approach

In this paper we investigate the fuzzy identification of brain-code during simple gripping-force control tasks. Since the synchronized oscillatory activity and the phase dynamics between the brain areas are two important mechanisms in the brain's function and information transfer, we decided to examine whether it is possible to extract the encoded information from the EEG signals using the phase-demodulation approach. The EEG was measured during the performance of different visuomotor tasks and the information we were trying to decode was the gripping force as applied by the subjects. The study revealed that it is possible, by using simple beta-rhythm filtering, phase demodulation, principal component analysis and a fuzzy model, to estimate the gripping-force response by using EEG signals as the inputs for the proposed model. The presented study has shown that even though EEG signals represent a superposition of all the active neurons, it is still possible to decode some information about the current activity of the brain centers. Furthermore, the cross-validation showed that the information about the gripping force is encoded in a very similar way for all the examined subjects. Thus, the phase shifts of the EEG signals seem to have a key role during activity and information transfer in the brain, while the phase-demodulation method proved to be a crucial step in the signal processing.