Article

Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array.

Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI 02912, USA.
Journal of Neural Engineering (Impact Factor: 3.28). 03/2011; 8(2):025027. DOI: 10.1088/1741-2560/8/2/025027
Source: PubMed

ABSTRACT The ongoing pilot clinical trial of the BrainGate neural interface system aims in part to assess the feasibility of using neural activity obtained from a small-scale, chronically implanted, intracortical microelectrode array to provide control signals for a neural prosthesis system. Critical questions include how long implanted microelectrodes will record useful neural signals, how reliably those signals can be acquired and decoded, and how effectively they can be used to control various assistive technologies such as computers and robotic assistive devices, or to enable functional electrical stimulation of paralyzed muscles. Here we examined these questions by assessing neural cursor control and BrainGate system characteristics on five consecutive days 1000 days after implant of a 4 × 4 mm array of 100 microelectrodes in the motor cortex of a human with longstanding tetraplegia subsequent to a brainstem stroke. On each of five prospectively-selected days we performed time-amplitude sorting of neuronal spiking activity, trained a population-based Kalman velocity decoding filter combined with a linear discriminant click state classifier, and then assessed closed-loop point-and-click cursor control. The participant performed both an eight-target center-out task and a random target Fitts metric task which was adapted from a human-computer interaction ISO standard used to quantify performance of computer input devices. The neural interface system was further characterized by daily measurement of electrode impedances, unit waveforms and local field potentials. Across the five days, spiking signals were obtained from 41 of 96 electrodes and were successfully decoded to provide neural cursor point-and-click control with a mean task performance of 91.3% ± 0.1% (mean ± s.d.) correct target acquisition. Results across five consecutive days demonstrate that a neural interface system based on an intracortical microelectrode array can provide repeatable, accurate point-and-click control of a computer interface to an individual with tetraplegia 1000 days after implantation of this sensor.

0 Bookmarks
 · 
86 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Decoding neural signals into control outputs has been a key to the development of brain-computer interfaces (BCIs). While many studies have identified neural correlates of kinematics or applied advanced machine learning algorithms to improve decoding performance, relatively less attention has been paid to optimal design of decoding models. For generating continuous movements from neural activity, design of decoding models should address how to incorporate movement dynamics into models and how to select a model given specific BCI objectives. Considering nonlinear and independent speed characteristics, we propose a hybrid Kalman filter to decode the hand direction and speed independently. We also investigate changes in performance of different decoding models (the linear and Kalman filters) when they predict reaching movements only or predict both reach and rest. Our offline study on human magnetoencephalography (MEG) during point-to-point arm movements shows that the performance of the linear filter or the Kalman filter is affected by including resting states for training and predicting movements. However, the hybrid Kalman filter consistently outperforms others regardless of movement states. The results demonstrate that better design of decoding models is achieved by incorporating movement dynamics into modeling or selecting a model according to decoding objectives.
    BioMed Research International 01/2014; 2014:176857. · 2.71 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Currently, there are lack effective therapeutic methods to restore neurological function for chronic complete spinal cord injury (SCI) by conventional treatment. Neurorestorative strategies with positive preclinical results have been translated to the clinic and some patients have gotten benefits and their quality of life has improved. These strategies include cell therapy, neurostimulation or neuromodulation, neuroprosthesis, neurotization or nerve bridging, and neurorehabilitation. The aim of this consensus by thirty one experts from 20 countries is to show the objective evidence of clinical neurorestoration for chronic complete SCI by the above mentioned neurorestorative strategies. Complete chronic SCI patients may no longer be told "nothing can be done". The translation to the clinic of more effective preclinical neurorestorative strategies should be encouraged as fast as possible in order to benefit patients with incurable CNS diseases. This manuscript is published as part of the International Association of Neurorestoratology (IANR) special issue of Cell Transplantation.
    Cell transplantation. 10/2014;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Lower motor neurons in the spinal cord lose supraspinal inputs after complete spinal cord injury, leading to a loss of volitional control below the injury site. Extensive locomotor training with spinal cord stimulation can restore locomotion function after spinal cord injury in humans and animals. However, this locomotion is non-voluntary, meaning that subjects cannot control stimulation via their natural "intent". A recent study demonstrated an advanced system that triggers a stimulator using forelimb stepping electromyographic patterns to restore quadrupedal walking in rats with spinal cord transection. However, this indirect source of "intent" may mean that other non-stepping forelimb activities may false-trigger the spinal stimulator and thus produce unwanted hindlimb movements.
    Journal of NeuroEngineering and Rehabilitation 07/2014; 11(1):107. · 2.57 Impact Factor

Full-text (2 Sources)

Download
10 Downloads
Available from
Jun 3, 2014