Hand Knob Area of Premotor Cortex Represents the Whole Body in a Compositional Way.

Abstract 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. 

Brain control of bimanual movement enabled by recurrent neural networks

Decades after the motor homunculus was first proposed, it is still unknown how different body parts are intermixed and interrelated in human motor cortex at single-neuron resolution. Using microelectrode arrays, we studied how face, head, arm and leg movements on both sides of the body are represented in hand knob area of precentral gyrus in people with tetraplegia. Contrary to the traditional somatotopy, we found strong representation of all movements. Probing further, we found that ipsilateral and contralateral movements, and homologous arm and leg movements (e.g. wrist and ankle), had a correlated representation. Additionally, there were neural dimensions where the limb was represented independently of the movement. Together, these patterns formed a "modular" code that might facilitate skill transfer across limbs. We also investigated dual-effector movement, finding that more strongly represented effectors suppressed the activity of weaker effectors. Finally, we leveraged these results to improve discrete brain-computer interfaces by spreading targets across all limbs. NCT00912041

https://biorxiv.org/content/biorxiv/early/2019/06/08/659839.full-text.pdf

Hand Knob Area of Motor Cortex in People with Tetraplegia Represents the Whole Body in a Modular Way

Neural decoding using gyral and intrasulcal electrocorticograms

The invention discloses an automatic training device of non-human primate touch sense research and a training method of the device. The automatic training device comprises a PC, a camera, a microcontroller, an animal seat and a multiple target selection grasping device, wherein the PC is provided with a display; the camera is connected with the PC; the microcontroller is connected with the PC by a serial port; the animal seat is used for fixing an experimental animal; the multiple target selection grasping device is located in front of the animal seat and divided into three layers, including a preparing button on the first layer, a grasping target object on the second layer and a judgement selection target object on the third layer; a prompt lamp is arranged on each layer, pressure sensors are fixed on the preparing button, the grasping target object and the judgement selection target object respectively, the grasping target object and the judgement selection target object are fixed on electric linear push rods which can move front and back, and the prompt lamps, the pressure sensors and the electric linear push rods are connected with the microcontroller respectively. According to the automatic training device, the training speed is greatly improved, and consumption of manpower resource can be saved.

Automatic training device of non-human primate touch sense research and training method of device

Brain machine interfaces (BMIs) have emerged as a technology to restore lost functionality in motor impaired patients. Most BMI systems employed neural signals from contralateral hemisphere. But many studies have also demonstrated the possibility to control hand movement using signals from ipsilateral one. However, the relationship of neural signals in sensorimotor cortex between contralateral and ipsilateral hand movement control is still unclear. In this study, the electrocorticographic signals (ECoG) of sensorimotor cortex were analyzed in two epilepsy participants when they performed a visual guided rock-scissors-paper task by using contralateral and ipsilateral hand respectively. Although typical beta suppression followed increased gamma were observed during the movements of each individual hands, the stronger responses were found in two participants when their contralateral hands were used during the task. We further extracted the power spectrum of high gamma frequency band (70–135Hz) of ECoG signals as neural features to decode the hand movements. The results showed that the classification accuracy of contralateral decoding and ipsilateral decoding were 81% and 78% for participator one (P1) and 84% and 77% for participator two (P2). The accuracy of ipsilateral decoding was only slightly lower than that of contralateral one. The hand movement information contained in ipsilateral sensorimotor cortex suggested that the ipsilateral hemisphere might be also involved in neural modulation as well as contralateral hemisphere did when performing unimanual movement, which would expand the clinical application of BMIs.

Electrocorticographic signals comparison in sensorimotor cortex between contralateral and ipsilateral hand movements

The invention provides an automatic grabbing training and recording device for non-human primates for solving the technical problem how to enable trained primates to rapidly grasp various specified grabbing actions. The device comprises a rotating disc body, a microcontroller, a water feed control system, a brain electrical signal recorder and a PC. The device is easy to operate and high in training efficiency. The training mode is adjustable as needed, and the training paradigm of different grabbing masks can be achieved; the gestures are accurately detected, the specified experimental paradigm is conveniently designed, data precision of follow-up analysis is improved, and later analysis and arrangement are convenient. According to the automatic grabbing training and recording device, specified gesture grabbing training of non-human primates can be achieved, the grabbing training of the non-human primates can be rapidly and efficiently achieved through the painless automatic training process, manpower resource consumption can be greatly reduced, and the automatic grabbing training and recording device is applicable to research and development of rehabilitation therapy instruments in the field of brain-computer interfaces.

Automatic grabbing training and recording device for non-human primates

The utility model provides an automatic gripping training of inhuman primate and recorder. The device includes the carousel main part, microcontroller, feedwater control system, EEG signal record appearance and PC, the carousel main part links to each other with microcontroller and PC, microcontroller arranges in inside the carousel main part, with the carousel main part, feedwater control system, EEG signal record appearance and PC are connected, feedwater control system arranges the experimental animals top in, link to each other with microcontroller, EEG signal record appearance is through fiber connection to PC, link to each other with microprocessor simultaneously, the carousel main part is connected respectively to the PC, microcontroller and EEG signal record appearance. The device easy operation trains efficiently, and training mode is adjustable, can realize the specific gesture gripping training of inhuman primate, and through there not being painful automatic training process, the gripping of high efficiency's the inhuman primate of realization is trained to reduce manpower resource consumption.

Automatic gripping training of inhuman primate and recorder

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Electrocorticographic signals comparison in sensorimotor cortex between contralateral and ipsilateral hand movements

Automatic grabbing training and recording device for non-human primates

Automatic gripping training of inhuman primate and recorder