Investigation of the phase feature of low-frequency electroencephalography signals for decoding hand movement parameters

The utility to decode hand movement parameters is significant to the control of artificial limb in the BCI fields. Most previous studies have adopted amplitude features of the low-frequency EEG signals to decode hand movement parameters. In this study, we have investigated the instantaneous phase of the low-frequency EEG signals attained by Hilbert transform for such a task for the first time, and compared its decoding accuracy with that of the amplitude features. An experiment was carried out that 5 subjects executed a center-out reaching task in two sessions. Then the Multiple Linear Regression (MLR) model is used to decode hand movement parameters based on the amplitude feature and the phase feature, respectively. The performance of the proposed approach is evaluated by calculating the correlation coefficients between the recorded parameters and the reconstructed parameters. The experiments results show that compared to the decoder with the amplitude feature, the correlation coefficients obtained by the decoder with the phase feature have increased 27.8% (X-position), 24.1% (Y-position), 27.9% (X-velocity), 20.9% (Y-velocity).