EEG Decoding Model Of Upper Limb Motion And Its Application In Brain-Controlled Vehicles

EEG signals induced by upper limb movement include abundant motion-related information,such as left/right-hand information in motion planning or motor imagery and angular velocity information in the process of upper limb circular motion.Establishing the EEG decoding model of the upper limb movement can not only promote the study of the neural mechanism of human motion control,but also can be used to help the development of brain-controlled robots and brain-controlled vehicles,improve the movement range of persons with disabilities or limb movement restriction.Therefore,the study of EEG decoding of upper limb movement has important scientific significance and application value.This paper mainly studies the EEG signal decoding model of upper limb movement and its application in brain-controlled vehicles.Firstly,the EEG-based decoding model of the imagining moving left/right hands and decoding model of the angular velocities of the upper limbs during the circular motion are proposed.Then the BCI systems based on the decoding models are applied to the dynamic simulation vehicle system,respectively.Finally,the brain-controlled vehicle is evaluated and analyzed.The major achievements of this paper are as follow:1.The EEG signal decoding model of the angular velocity parameters of the upper limb circular motion based on Kalman filter is proposed.The experimental results show that the proposed model has better decoding performance than the traditional multiple linear regression decoding model and the state transition decoding model.2.Based on the decoding model of the angular velocity of the upper limbs,the brain-computer interface which can output continuous commands is established and applied to the driver's intention prediction.The effectiveness of the proposed model is tested by experiments.3.The EEG signal decoding model of upper limb movement direction is built,and the alpha wave-based brain-computer interface is integrated into the decoding model to realize the lateral and longitudinal control and the starting/stopping control of the simulated vehicle.These innovative results not only help the development of EEG signal decoding technology,but also have important guiding significance for establishing practical BCI systems which can output continuous command.