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1.
Sci Robot ; 4(31)2019 06 26.
Article in English | MEDLINE | ID: mdl-31656937

ABSTRACT

Brain-computer interfaces (BCIs) utilizing signals acquired with intracortical implants have achieved successful high-dimensional robotic device control useful for completing daily tasks. However, the substantial amount of medical and surgical expertise required to correctly implant and operate these systems significantly limits their use beyond a few clinical cases. A noninvasive counterpart requiring less intervention that can provide high-quality control would profoundly impact the integration of BCIs into the clinical and home setting. Here, we present and validate a noninvasive framework utilizing electroencephalography (EEG) to achieve the neural control of a robotic device for continuous random target tracking. This framework addresses and improves upon both the "brain" and "computer" components by respectively increasing user engagement through a continuous pursuit task and associated training paradigm, and the spatial resolution of noninvasive neural data through EEG source imaging. In all, our unique framework enhanced BCI learning by nearly 60% for traditional center-out tasks and by over 500% in the more realistic continuous pursuit task. We further demonstrated an additional enhancement in BCI control of almost 10% by using online noninvasive neuroimaging. Finally, this framework was deployed in a physical task, demonstrating a near seamless transition from the control of an unconstrained virtual cursor to the real-time control of a robotic arm. Such combined advances in the quality of neural decoding and the practical utility of noninvasive robotic arm control will have major implications on the eventual development and implementation of neurorobotics by means of noninvasive BCI.

2.
J Neural Eng ; 15(1): 016009, 2018 02.
Article in English | MEDLINE | ID: mdl-28914232

ABSTRACT

OBJECTIVE: Combining repetitive transcranial magnetic stimulation (rTMS) with brain-computer interface (BCI) training can address motor impairment after stroke by down-regulating exaggerated inhibition from the contralesional hemisphere and encouraging ipsilesional activation. The objective was to evaluate the efficacy of combined rTMS + BCI, compared to sham rTMS + BCI, on motor recovery after stroke in subjects with lasting motor paresis. APPROACH: Three stroke subjects approximately one year post-stroke participated in three weeks of combined rTMS (real or sham) and BCI, followed by three weeks of BCI alone. Behavioral and electrophysiological differences were evaluated at baseline, after three weeks, and after six weeks of treatment. MAIN RESULTS: Motor improvements were observed in both real rTMS + BCI and sham groups, but only the former showed significant alterations in inter-hemispheric inhibition in the desired direction and increased relative ipsilesional cortical activation from fMRI. In addition, significant improvements in BCI performance over time and adequate control of the virtual reality BCI paradigm were observed only in the former group. SIGNIFICANCE: When combined, the results highlight the feasibility and efficacy of combined rTMS + BCI for motor recovery, demonstrated by increased ipsilesional motor activity and improvements in behavioral function for the real rTMS + BCI condition in particular. Our findings also demonstrate the utility of BCI training alone, as shown by behavioral improvements for the sham rTMS + BCI condition. This study is the first to evaluate combined rTMS and BCI training for motor rehabilitation and provides a foundation for continued work to evaluate the potential of both rTMS and virtual reality BCI training for motor recovery after stroke.


Subject(s)
Brain-Computer Interfaces , Motor Cortex/physiology , Stroke Rehabilitation/methods , Stroke/therapy , Transcranial Magnetic Stimulation/methods , Virtual Reality Exposure Therapy/methods , Adult , Aged , Combined Modality Therapy , Female , Humans , Magnetic Resonance Imaging/methods , Male , Middle Aged , Recovery of Function/physiology , Stroke/diagnosis , Stroke/physiopathology
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