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Recently, the advent of the non-invasive brain-computer interface (BCI) for continuous decoding of upper limb motions opens a new horizon for motor-disabled people. However, the performance of discrete-decoding BCIs based on discriminating different brain states are still more robust. In this study, we aimed to cascade a discrete state decoder with a continuous decoder to enhance the prediction of hand trajectories. EEG data were recorded from nine healthy subjects performing a center-out task with four orthogonal targets on the horizontal plane. The pre-movement data of each trial has been used for training a binary discrete decoder which identifies the axis of the movement based on common spatial pattern (CSP) features. Two non-parametric continuous decoders based on Gaussian process regression (GPR) have been designed for continuous decoding of hand movements along each axis using the envelope features of EEG signals in six frequency bands. In addition to those four principal orthogonal targets, some targets at random directions on the horizontal plane were recorded to evaluate the generalizability of the proposed model. The discrete decoder attained the average binary classification of 97.1% for discriminating movement along the x-axis and y-axis. The proposed state-based method achieved the mean correlation coefficient of 0.54 between actual and predicted trajectories for principal targets over all subjects. The trajectories of random targets were also decoded with a mean correlation of 0.37. The generalizability of the proposed paradigm proved by the findings of this study could open new possibilities in developing novel types of neuroprostheses for rehabilitation purposes.

The continuous decoding of human movement intention based on electroencephalogram (EEG) signals is valuable for developing a more natural motor augmented or assistive system instead of its discrete classifications. The classic center-out paradigm has been widely used to study discrete and continuous hand movement parameter decoding. However, when applying it in studying continuous movement decoding, the classic paradigm needs to be improved to increase the decoding performance, especially generalization performance. In this paper, we first discuss the limitations of the classic center-out paradigm in exploring the hand movement’s continuous decoding. Then, an improved paradigm is proposed to enhance the continuous decoding performance. Besides, an adaptive decoder-ensemble framework is developed for continuous kinematic parameter decoding. Finally, with the improved center-out paradigm and the ensemble decoding framework, the average Pearson’s correlation coefficients between the predicted and recorded movement kinematic parameters improve significantly by about 75 percent for the directional parameters and about 10 percent for the non-directional parameters. Furthermore, its generalization performance improves significantly by about 20 percent for the directional parameters. This study indicates the advantage of the improved paradigm in predicting the hand movement’s kinematic information from low-frequency scalp EEG signals. It can advance the applications of the noninvasive motor brain-computer interface (BCI) in rehabilitation, daily assistance, and human augmentation areas.

The continuous decoding of human movement intention based on electroencephalogram (EEG) signals is valuable for developing a more natural motor augmented or assistive system instead of its discrete classifications. The classic center-out paradigm has been widely used to study discrete and continuous hand movement parameter decoding. However, when applying it in studying continuous movement decoding, the classic paradigm needs to be improved to increase the decoding performance, especially generalization performance. In this paper, we first discuss the limitations of the classic center-out paradigm in exploring the hand movement’s continuous decoding. Then, an improved paradigm is proposed to enhance the continuous decoding performance. Besides, an adaptive decoder-ensemble framework is developed for continuous kinematic parameter decoding. Finally, with the improved center-out paradigm and the ensemble decoding framework, the average Pearson’s correlation coefficients between the predicted and recorded movement kinematic parameters improve significantly by about 75 percent for the directional parameters and about 10 percent for the non-directional parameters. Furthermore, its generalization performance improves significantly by about 20 percent for the directional parameters. This study indicates the advantage of the improved paradigm in predicting the hand movement’s kinematic information from low-frequency scalp EEG signals. It can advance the applications of the noninvasive motor brain-computer interface (BCI) in rehabilitation, daily assistance, and human augmentation areas. [ABSTRACT FROM AUTHOR]