Long-term intracortical microelectrode array performance in a human: a 5 year retrospective analysis.

. Brain-computer interfaces (BCIs) that record neural activity using intracortical microelectrode arrays (MEAs) have shown promise for mitigating disability associated with neurological injuries and disorders. While the chronic performance and failure modes of MEAs have been well studied and systematically described in non-human primates, there is far less reported about long-term MEA performance in humans.

Classifying Intracortical Brain-Machine Interface Signal Disruptions Based on System Performance and Applicable Compensatory Strategies: A Review.

Brain-machine interfaces (BMIs) record and translate neural activity into a control signal for assistive or other devices. Intracortical microelectrode arrays (MEAs) enable high degree-of-freedom BMI control for complex tasks by providing fine-resolution neural recording. However, chronically implanted MEAs are subject to a dynamic environment where transient or systematic disruptions can interfere with neural recording and degrade BMI performance.

Restoring the Sense of Touch Using a Sensorimotor Demultiplexing Neural Interface.

Paralyzed muscles can be reanimated following spinal cord injury (SCI) using a brain-computer interface (BCI) to enhance motor function alone. Importantly, the sense of touch is a key component of motor function. Here, we demonstrate that a human participant with a clinically complete SCI can use a BCI to simultaneously reanimate both motor function and the sense of touch, leveraging residual touch signaling from his own hand.

Toward a Consensus for Musculoskeletal Ultrasonography Education in Physical Medicine and Rehabilitation: A National Poll of Residency Directors.

Objectives: The aims of the study were to evaluate integration of musculoskeletal ultrasonography education in physical medicine and rehabilitation training programs in 2014-2015, when the American Academy of Physical Medicine & Rehabilitation and Accreditation Council for Graduate Medical Education Residency Review Committee both recognized it as a fundamental component of physiatric practice, to identify common musculoskeletal ultrasonography components of physical medicine and rehabilitation residency curricula, and to identify common barriers to integration.

Design: Survey of 78 Accreditation Council for Graduate Medical Education-accredited physical medicine and rehabilitation residency programs was conducted.

Results: The 2015 survey response rate was more than 50%, and respondents were representative of programs across the United States.

A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent.

Laboratory demonstrations of brain-computer interface (BCI) systems show promise for reducing disability associated with paralysis by directly linking neural activity to the control of assistive devices. Surveys of potential users have revealed several key BCI performance criteria for clinical translation of such a system. Of these criteria, high accuracy, short response latencies, and multi-functionality are three key characteristics directly impacted by the neural decoding component of the BCI system, the algorithm that translates neural activity into control signals.

Brain Computer Interfaces in Rehabilitation Medicine.

One innovation currently influencing physical medicine and rehabilitation is brain-computer interface (BCI) technology. BCI systems used for motor control record neural activity associated with thoughts, perceptions, and motor intent; decode brain signals into commands for output devices; and perform the user's intended action through an output device. BCI systems used for sensory augmentation transduce environmental stimuli into neural signals interpretable by the central nervous system.

Meeting brain-computer interface user performance expectations using a deep neural network decoding framework.

Brain-computer interface (BCI) neurotechnology has the potential to reduce disability associated with paralysis by translating neural activity into control of assistive devices. Surveys of potential end-users have identified key BCI system features, including high accuracy, minimal daily setup, rapid response times, and multifunctionality. These performance characteristics are primarily influenced by the BCI's neural decoding algorithm, which is trained to associate neural activation patterns with intended user actions.

A High Definition Noninvasive Neuromuscular Electrical Stimulation System for Cortical Control of Combinatorial Rotary Hand Movements in a Human With Tetraplegia.

Objective: Paralysis resulting from spinal cord injury (SCI) can have a devastating effect on multiple arm and hand motor functions. Rotary hand movements, such as supination and pronation, are commonly impaired by upper extremity paralysis, and are essential for many activities of daily living. In this proof-of-concept study, we utilize a neural bypass system (NBS) to decode motor intention from motor cortex to control combinatorial rotary hand movements elicited through stimulation of the arm muscles, effectively bypassing the SCI of the study participant.

Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications.

Background: Understanding the long-term behavior of intracortically-recorded signals is essential for improving the performance of Brain Computer Interfaces. However, few studies have systematically investigated chronic neural recordings from an implanted microelectrode array in the human brain.

Methods: In this study, we show the applicability of wavelet decomposition method to extract and demonstrate the utility of long-term stable features in neural signals obtained from a microelectrode array implanted in the motor cortex of a human with tetraplegia.

Neuroprosthetic-enabled control of graded arm muscle contraction in a paralyzed human.

Neuroprosthetics that combine a brain computer interface (BCI) with functional electrical stimulation (FES) can restore voluntary control of a patients' own paralyzed limbs. To date, human studies have demonstrated an "all-or-none" type of control for a fixed number of pre-determined states, like hand-open and hand-closed. To be practical for everyday use, a BCI-FES system should enable smooth control of limb movements through a continuum of states and generate situationally appropriate, graded muscle contractions.

Reducing concussion symptoms among teenage youth: Evaluation of a mobile health app.

Objective: To evaluate whether a mobile health application that employs elements of social game design could compliment medical care for unresolved concussion symptoms.

Design: Phase I and Phase II (open-label, non-randomized, ecological momentary assessment methodology).

Setting: Outpatient concussion clinic.

Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface.

Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second.

Restoring cortical control of functional movement in a human with quadriplegia.

Millions of people worldwide suffer from diseases that lead to paralysis through disruption of signal pathways between the brain and the muscles. Neuroprosthetic devices are designed to restore lost function and could be used to form an electronic 'neural bypass' to circumvent disconnected pathways in the nervous system. It has previously been shown that intracortically recorded signals can be decoded to extract information related to motion, allowing non-human primates and paralysed humans to control computers and robotic arms through imagined movements.

Subjective perceptual distortions and visual dysfunction in children with autism.

Case reports and sensory inventories suggest that autism involves sensory processing anomalies. Behavioral tests indicate impaired motion and normal form perception in autism. The present study used first-person accounts to investigate perceptual anomalies and related subjective to psychophysical measures.

Steady state visual evoked potential abnormalities in schizophrenia.

Objective: The steady state visual evoked potential (SSVEP) can be used to test the frequency response function of neural circuits. Previous studies have shown reduced SSVEPs to alpha and lower frequencies of stimulation in schizophrenia. We investigated SSVEPs in schizophrenia at frequencies spanning the theta (4Hz) to gamma (40Hz) range.

Personality traits in schizophrenia and related personality disorders.

We investigated whether schizophrenia spectrum disorders share common personality characteristics or traits. Participants with a diagnosis of schizophrenia or schizoaffective disorder (SZ) or with a schizophrenia spectrum personality disorder (schizophrenia spectrum PD: schizoid, paranoid, and schizotypal personality disorder) were compared with non-psychiatric control subjects on the five-factor model of personality and the psychosis-proneness scales. On the five-factor personality scales, SZ subjects showed higher levels of neuroticism, and lower levels of openness, agreeableness, extraversion, and conscientiousness than control subjects.