Hand Movement Trajectory Reconstruction from EEG for Brain-Computer Interface Systems

Decoding hand movement parameters (for example movement trajectory, speed etc.) from scalp recordings such as Electroencephalography (EEG) is a challenging and less explored area of research in the field of Brain Computer Interface (BCI) systems. By identifying neural features underlying movement parameters, a detailed and well defined control command set can be provided to the BCI output device. A continuous control to the output device is better suited for practical BCI systems, and can be achieved by continuous reconstruction of movement trajectory than discrete brain activity classifications. In this study, we attempt to reconstruct/estimate various parameters of hand movement trajectory from multi channel EEG recordings. The data for analysis is collected by performing an experiment that involved centre-out right hand movement tasks in four different directions at two different speeds in random order. Multiple linear regression (MLR) strategy that fits the recorded movement parameters to a set of spatial, spectral and temporal localized neural data set is adopted. We propose a method to define the predictor set for MLR, using wavelet analysis, to decompose the signal into various subbands. The correlation between recorded and estimated parameters are calculated and an average correlation coefficient of (0.56 +/- 0.16) is obtained over estimating six movement parameters. The promising results achieved using the proposed algorithm, which are better than that of the existing algorithms, indicate the applicability of EEG for continuous motor control.