Brain machine interfaces (BMIs) have demonstrated lots of successful arm-related reach decoding in past decades, which provide a new hope for restoring the lost motor functions for the disabled. On the other hand, the more sophisticated hand grasp movement, which is more fundamental and crucial for daily life, was less referred. Current state of arts has specified some grasp related brain areas and offline decoding results; however, online decoding grasp movement and real-time neuroprosthetic control have not been systematically investigated. In this study, we obtained neural data from the dorsal premotor cortex (PMd) when monkey reaching and grasping one of four differently shaped objects following visual cues. The four grasp gesture types with an additional resting state were classified asynchronously using a fuzzy k-nearest neighbor model, and an artificial hand was controlled online using a shared control strategy. The results showed that most of the neurons in PMd are tuned by reach and grasp movement, us- ing which we get a high average offline decoding accuracy of 97.1%. In the online demonstration, the instantaneous status of monkey grasping could be extracted successfully to control the artificial hand, with an event-wise accuracy of 85.1%. Overall, our results inspect the neural firing along the time course of grasp and for the first time enables asynchronous neural control of a prosthetic hand, which underline a feasible hand neural prosthesis in BMIs.
This paper presents a hybrid brain-computer interface (BCI) control strategy,the goal of which is to expand control functions of a conventional motor imagery or a P300 potential based BCI in a virtual environment.The hybrid control strategy utilizes P300 potential to control virtual devices and motor imagery related sensorimotor rhythms to navigate in the virtual world.The two electroencephalography (EEG) patterns serve as source signals for different control functions in their corresponding system states,and state switch is achieved in a sequential manner.In the current system,imagination of left/right hand movement was translated into turning left/right in the virtual apartment continuously,while P300 potentials were mapped to discrete virtual device control commands using a five-oddball paradigm.The combination of motor imagery and P300 patterns in one BCI system for virtual environment control was tested and the results were compared with those of a single motor imagery or P300-based BCI.Subjects obtained similar performances in the hybrid and single control tasks,which indicates the hybrid control strategy works well in the virtual environment.
Brain-machine interfaces (BMIs) translate neural activities of the brain into specific instructions that can be carried out by external devices. BMIs have the potential to restore or augment motor functions of paralyzed patients suffering from spinal cord damage. The neural activities have been used to predict the 2D or 3D movement trajectory of monkey's arm or hand in many studies. However, there are few studies on decoding the wrist movement from neural activities in center-out paradigm. The present study developed an invasive BMI system with a monkey model using a 10×10-microelectrode array in the primary motor cortex. The monkey was trained to perform a two-dimensional forelimb wrist movement paradigm where neural activities and movement signals were simultaneous recorded. Results showed that neuronal firing rates highly correlated with forelimb wrist movement; > 70% (105/149) neurons exhibited specific firing changes during movement and > 36% (54/149) neurons were used to discriminate directional pairs. The neuronal firing rates were also used to predict the wrist moving directions and continuous trajectories of the forelimb wrist. The four directions could be classified with 96% accuracy using a support vector machine, and the correlation coefficients of trajectory prediction using a general regression neural network were above 0.8 for both horizontal and vertical directions. Results showed that this BMI system could predict monkey wrist movements in high accuracy through the use of neuronal firing information.
