Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array. J Neural Eng 8(2):025027

Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI 02912, USA.
Journal of Neural Engineering (Impact Factor: 3.3). 03/2011; 8(2):025027. DOI: 10.1088/1741-2560/8/2/025027
Source: PubMed


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.

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Available from: Michael J Black, Mar 02, 2014
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    • "The prospective clinical applications of BMIs on a larger scale raises questions about which group of patients is eligible for therapeutic BMI implantations and whether these patients find intracranial electrode implantation acceptable for a BMI. Previous studies highlighted that generally, a broad group of patients with severe motor disabilities may benefit from a BMI, including patients affected by stroke, spinal cord injury (SCI), and neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) (Collinger et al 2013, Hochberg et al 2006, Kennedy and Bakay 1998, Simeral et al 2011). "
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    ABSTRACT: Brain-machine interfaces (BMI) are an emerging therapeutic option that can allow paralyzed patients to gain control over assistive technology devices (ATDs). BMI approaches can be broadly classified into invasive (based on intracranially implanted electrodes) and noninvasive (based on skin electrodes or extracorporeal sensors). Invasive BMIs have a favorable signal-to-noise ratio, and thus allow for the extraction of more information than noninvasive BMIs, but they are also associated with the risks related to neurosurgical device implantation. Current noninvasive BMI approaches are typically concerned, among other issues, with long setup times and/or intensive training. Recent studies have investigated the attitudes of paralyzed patients eligible for BMIs, particularly patients affected by amyotrophic lateral sclerosis (ALS). These studies indicate that paralyzed patients are indeed interested in BMIs. Little is known, however, about the degree of knowledge among paralyzed patients concerning BMI approaches or about how patients retrieve information on ATDs. Furthermore, it is not yet clear if paralyzed patients would accept intracranial implantation of BMI electrodes with the premise of decoding improvements, and what the attitudes of a broader range of patients with diseases such as stroke or spinal cord injury are towards this new kind of treatment. Using a questionnaire, we surveyed 131 paralyzed patients for their opinions on invasive BMIs and their attitude toward invasive BMI treatment options. The majority of the patients knew about and had a positive attitude toward invasive BMI approaches. The group of ALS patients was especially open to the concept of BMIs. The acceptance of invasive BMI technology depended on the improvements expected from the technology. Furthermore, the survey revealed that for paralyzed patients, the Internet is an important source of information on ATDs. Websites tailored to prospective BMI users should be further developed to provide reliable information to patients, and also to help to link prospective BMI users with researchers involved in the development of BMI technology.
    Journal of Neural Engineering 07/2015; 12(4):043001. DOI:10.1088/1741-2560/12/4/043001 · 3.30 Impact Factor
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    • "However, the difficulty in mapping recorded neural activity to teleoperation motion commands compounds the difficulties present in conventional teleoperation , hindering its applicability in contexts requiring high precision and dexterity. Reduced integrity of neural signals caused by the degradation of invasive BCIs over time [39] results in increased erraticity and noise in the interpreted motion commands. This effect also reduces the dimensionality Fig. 1: Brain-computer interface controlled telemanipulation. "
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    ABSTRACT: Robot teleoperation systems introduce a unique set of challenges including latency, intermittency, and asymmetry in control inputs. User control with Brain-Computer Interfaces (BCIs) exacerbates these problems through especially noisy and even erratic low-dimensional motion commands due to the difficulty in decoding neural activity. We introduce a general framework to address these challenges through a combination of Machine Vision, User Intent Inference, and Human-Robot Autonomy Control Arbitration. Adjustable levels of assistance allow the system to balance the operator's capabilities and feelings of comfort and control while compensating for a task's difficulty. We present experimental results demonstrating significant performance improvement using the shared-control assistance framework on adapted rehabilitation benchmarks with two subjects implanted with intracortical brain-computer interfaces controlling a high degree-of-freedom robotic manipulator as a prosthetic. Our results further indicate shared assistance mitigates perceived user difficulty and even enables successful performance on previously infeasible tasks. We showcase the extensibility of our architecture with applications to quality-of-life tasks such as opening a door with a BCI, pouring liquids from a container with a dual-joystick game controller, and manipulation in dense clutter with a 6-DoF motion controller.
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    • "A thorough evaluation of explanted electrodes using EIS and surface microscopy techniques, and comparison to pre-implantation and in vivo measurements would help to elucidate the influence of material changes on electrochemical properties of implanted electrodes. Researchers have routinely measured impedance at 1 kHz to characterize the electrode performance for electrophysiological recordings (Ludwig et al 2006, Ward et al 2009, Simeral et al 2011, Prasad et al 2012). The frequency of 1 kHz matches the frequency of the neural action potential, which is about a millisecond long (Humphrey and Schmidt 1990). "
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    ABSTRACT: Objective: A challenge for implementing high bandwidth cortical brain-machine interface devices in patients is the limited functional lifespan of implanted recording electrodes. Development of implant technology currently requires extensive non-clinical testing to demonstrate device performance. However, testing the durability of the implants in vivo is time-consuming and expensive. Validated in vitro methodologies may reduce the need for extensive testing in animal models. Approach: Here we describe an in vitro platform for rapid evaluation of implant stability. We designed a reactive accelerated aging (RAA) protocol that employs elevated temperature and reactive oxygen species (ROS) to create a harsh aging environment. Commercially available microelectrode arrays (MEAs) were placed in a solution of hydrogen peroxide at 87 °C for a period of 7 days. We monitored changes to the implants with scanning electron microscopy and broad spectrum electrochemical impedance spectroscopy (1 Hz-1 MHz) and correlated the physical changes with impedance data to identify markers associated with implant failure. Main results: RAA produced a diverse range of effects on the structural integrity and electrochemical properties of electrodes. Temperature and ROS appeared to have different effects on structural elements, with increased temperature causing insulation loss from the electrode microwires, and ROS concentration correlating with tungsten metal dissolution. All array types experienced impedance declines, consistent with published literature showing chronic (>30 days) declines in array impedance in vivo. Impedance change was greatest at frequencies <10 Hz, and smallest at frequencies 1 kHz and above. Though electrode performance is traditionally characterized by impedance at 1 kHz, our results indicate that an impedance change at 1 kHz is not a reliable predictive marker of implant degradation or failure. Significance: ROS, which are known to be present in vivo, can create structural damage and change electrical properties of MEAs. Broad-spectrum electrical impedance spectroscopy demonstrates increased sensitivity to electrode damage compared with single-frequency measurements. RAA can be a useful tool to simulate worst-case in vivo damage resulting from chronic electrode implantation, simplifying the device development lifecycle.
    Journal of Neural Engineering 01/2015; 12(2):026003. DOI:10.1088/1741-2560/12/2/026003 · 3.30 Impact Factor
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