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Brain-computer interfaces have so far focused largely on enabling the control of a single effector, for example a single computer cursor or robotic arm. Restoring multi-effector motion could unlock greater functionality for people with paralysis (e.g., bimanual movement). However, it may prove challenging to decode the simultaneous motion of multiple effectors, as we recently found that a compositional neural code links movements across all limbs and that neural tuning changes nonlinearly during dual-effector motion. Here, we demonstrate the feasibility of high-quality bimanual control of two cursors via neural network (NN) decoders. Through simulations, we show that NNs leverage a neural 'laterality' dimension to distinguish between left and right-hand movements as neural tuning to both hands become increasingly correlated. In training recurrent neural networks (RNNs) for two-cursor control, we developed a method that alters the temporal structure of the training data by dilating/compressing it in time and re-ordering it, which we show helps RNNs successfully generalize to the online setting. With this method, we demonstrate that a person with paralysis can control two computer cursors simultaneously. Our results suggest that neural network decoders may be advantageous for multi-effector decoding, provided they are designed to transfer to the online setting. [ABSTRACT FROM AUTHOR]

A brain-controlled vehicle (BCV) is a vehicle that uses the Brain–Computer interface (BCI) to analyze the driver's electroencephalogram (EEG) to obtain human control commands, which can help disabled people extend movement range and improve their self-independence. However, the performance of BCV is poor and the accuracy of BCI recognition varies considerably with different subjects, which severely restrict its promotion and application. To develop a better solution, with the concept of shared control, an intention preferenced fusion brain control strategy based on fuzzy logic is proposed. Firstly, based on Fuzzy Discrete Event System Supervision (FDES) theory, instructions of the driver are supervised and evaluated. At the same time, based on fuzzy logic an automatic controller which gives an automatic decision according to the cur-rent state of the vehicle is designed. Then according to the automatic decision and the correctness of the driver's instructions, a fuzzy reasoning is made, making the final decision more in line with human intentions. In order to verify the effectiveness of the proposed method, based on our previously-designed SSMVEP platform, we conduct virtual experiments, semi-object tests and full physical tests separately for verification. Compared with others shared control schemes, it not only achieves continuous control, but the final control-making is more in line with human intentions, which can encourage users to become more involved, enhancing their self-esteem and brain functions. • An intention preferenced fusion brain control strategy is proposed. • FDES theory is used to supervise the brain instructions. • An automatic controller is designed based on fuzzy logic. • Fuzzy reasoning is used to make the final decision more in line with human intentions. • Virtual experiments, semi-object tests and full physical tests are conducted for verification. [ABSTRACT FROM AUTHOR]