Review: Human Intracortical Recording and Neural Decoding for Brain-Computer Interfaces.
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ABSTRACT:
SUBMITTER: Brandman DM
PROVIDER: S-EPMC5815832 | biostudies-literature | 2017 Oct
REPOSITORIES: biostudies-literature
Publications
Review: Human Intracortical Recording and Neural Decoding for Brain-Computer Interfaces.
Brandman David M DM Cash Sydney S SS Hochberg Leigh R LR
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society 20170302 10
Brain-computer interfaces (BCIs) use neural information recorded from the brain for the voluntary control of external devices. The development of BCI systems has largely focused on improving functional independence for individuals with severe motor impairments, including providing tools for communication and mobility. In this review, we describe recent advances in intracortical BCI technology and provide potential directions for further research. ...[more]
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Project description:Intracortical brain-computer interface (BCI) decoders are typically retrained daily to maintain stable performance. Self-recalibrating decoders aim to remove the burden this may present in the clinic by training themselves autonomously during normal use but have only been developed for continuous control. Here we address the problem for discrete decoding (classifiers).We recorded threshold crossings from 96-electrode arrays implanted in the motor cortex of two rhesus macaques performing center-out reaches in 7 directions over 41 and 36 separate days spanning 48 and 58 days in total for offline analysis.We show that for the purposes of developing a self-recalibrating classifier, tuning parameters can be considered as fixed within days and that parameters on the same electrode move up and down together between days. Further, drift is constrained across time, which is reflected in the performance of a standard classifier which does not progressively worsen if it is not retrained daily, though overall performance is reduced by more than 10% compared to a daily retrained classifier. Two novel self-recalibrating classifiers produce a ~15% increase in classification accuracy over that achieved by the non-retrained classifier to nearly recover the performance of the daily retrained classifier.We believe that the development of classifiers that require no daily retraining will accelerate the clinical translation of BCI systems. Future work should test these results in a closed-loop setting.
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Project description:The efficacy of wireless intracortical brain-computer interfaces (iBCIs) is limited in part by the number of recording channels, which is constrained by the power budget of the implantable system. Designing wireless iBCIs that provide the high-quality recordings of today's wired neural interfaces may lead to inadvertent over-design at the expense of power consumption and scalability. Here, we report analyses of neural signals collected from experimental iBCI measurements in rhesus macaques and from a clinical-trial participant with implanted 96-channel Utah multielectrode arrays to understand the trade-offs between signal quality and decoder performance. Moreover, we propose an efficient hardware design for clinically viable iBCIs, and suggest that the circuit design parameters of current recording iBCIs can be relaxed considerably without loss of performance. The proposed design may allow for an order-of-magnitude power savings and lead to clinically viable iBCIs with a higher channel count.
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Project description:OBJECTIVE:When using an intracortical BCI (iBCI), users modulate their neural population activity to move an effector towards a target, stop accurately, and correct for movement errors. We call the rules that govern this modulation a 'feedback control policy'. A better understanding of these policies may inform the design of higher-performing neural decoders. APPROACH:We studied how three participants in the BrainGate2 pilot clinical trial used an iBCI to control a cursor in a 2D target acquisition task. Participants used a velocity decoder with exponential smoothing dynamics. Through offline analyses, we characterized the users' feedback control policies by modeling their neural activity as a function of cursor state and target position. We also tested whether users could adapt their policy to different decoder dynamics by varying the gain (speed scaling) and temporal smoothing parameters of the iBCI. MAIN RESULTS:We demonstrate that control policy assumptions made in previous studies do not fully describe the policies of our participants. To account for these discrepancies, we propose a new model that captures (1) how the user's neural population activity gradually declines as the cursor approaches the target from afar, then decreases more sharply as the cursor comes into contact with the target, (2) how the user makes constant feedback corrections even when the cursor is on top of the target, and (3) how the user actively accounts for the cursor's current velocity to avoid overshooting the target. Further, we show that users can adapt their control policy to decoder dynamics by attenuating neural modulation when the cursor gain is high and by damping the cursor velocity more strongly when the smoothing dynamics are high. SIGNIFICANCE:Our control policy model may help to build better decoders, understand how neural activity varies during active iBCI control, and produce better simulations of closed-loop iBCI movements.
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Project description:OBJECTIVE:Brain-computer interfaces (BCIs) aim to provide a means for people with severe motor disabilities to control their environment directly with neural activity. In intracortical BCIs for people with tetraplegia, the decoder that maps neural activity to desired movements has typically been calibrated using 'open-loop' (OL) imagination of control while a cursor automatically moves to targets on a computer screen. However, because neural activity can vary across contexts, a decoder calibrated using OL data may not be optimal for 'closed-loop' (CL) neural control. Here, we tested whether CL calibration creates a better decoder than OL calibration even when all other factors that might influence performance are held constant, including the amount of data used for calibration and the amount of elapsed time between calibration and testing. APPROACH:Two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial performed a center-out-back task using an intracortical BCI, switching between decoders that had been calibrated on OL versus CL data. MAIN RESULTS:Even when all other variables were held constant, CL calibration improved neural control as well as the accuracy and strength of the tuning model. Updating the CL decoder using additional and more recent data resulted in further improvements. SIGNIFICANCE:Differences in neural activity between OL and CL contexts contribute to the superiority of CL decoders, even prior to their additional 'adaptive' advantage. In the near future, CL decoder calibration may enable robust neural control without needing to pause ongoing, practical use of BCIs, an important step toward clinical utility.
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