Brain-Machine Interfaces (BMIs) have recently emerged as a clinically viable option
 to restore voluntary movements after paralysis. These devices are based on the
 ability to extract information about movement intent from neural signals recorded
 using multi-electrode arrays chronically implanted in the motor cortices of the
 brain. However, the inherent loss and turnover of recorded neurons requires repeated
 recalibrations of the interface, which can potentially alter the day-to-day
 user experience. The resulting need for continued user adaptation interferes with
 the natural, subconscious use of the BMI. Here, we introduce a new computational
 approach that decodes movement intent from a low-dimensional latent representation
 of the neural data. We implement various domain adaptation methods
 to stabilize the interface over significantly long times. This includes Canonical
 Correlation Analysis used to align the latent variables across days; this method
 requires prior point-to-point correspondence of the time series across domains.
 Alternatively, we match the empirical probability distributions of the latent variables
 across days through the minimization of their Kullback-Leibler divergence.
 These two methods provide a significant and comparable improvement in the performance
 of the interface. However, implementation of an Adversarial Domain
 Adaptation Network trained to match the empirical probability distribution of the
 residuals of the reconstructed neural signals outperforms the two methods based
 on latent variables, while requiring remarkably few data points to solve the domain
 adaptation problem. Individuals with tetraplegia due to spinal cord injury identify restoration of hand function as their highest priority BID1 . Over 50% of respondents with a C1-C4 injury would be willing to undergo brain surgery to restore grasp . Brain-Machine Interfaces (BMIs) aim to restore motor function by extracting movement intent from neural signals. Despite its great promise, current BMI technology has significant limitations. A BMI that maps neural activity in primary motor cortex (M1) onto motor intent commands should ideally provide a stable day-to-day user experience. However, the gradual alterations of the activity recorded by chronically implanted multi-electrode arrays, due to neuron turnover or electrode movement and failure BID4 , causes considerable variation in the actions produced by the BMI. This turnover may occur within a single day BID12 , and is estimated to be on the order of 40% over two weeks BID11 ). In the face of changing neural signals, performance can be maintained by daily retraining the interface, but this is not a viable solution as it requires the user to keep on adapting to a new interface BID0 .There is a high degree of correlation across the M1 neural signals. This redundancy implies that the dimensionality of the underlying motor command is much lower than the number of M1 neurons, and even lower than the number of recorded M1 neurons BID15 . The use of dimen-sionality reduction methods is thus a common practice in BMI design, as it provides a more compact and denoised representation of neural activity, and a low-dimensional predictor of movement intent. Most of the earlier work used linear dimensionality reduction methods such as Principal Components Analysis (PCA) and Factor Analysis (FA) BID35 BID30 BID29 BID15 ; more recently, autoencoders (AEs) have been used for the nonlinear dimensionality reduction of neural signals BID27 . Here we develop a deep learning architecture to extract a low-dimensional representation of recorded M1 activity constrained to include features related to movement intent. This is achieved by the simultaneous training of a deep, nonlinear autoencoder network based on neural signals from M1, and a network that predicts movement intent from the inferred low-dimensional signals. We show that this architecture significantly improves predictions over the standard sequential approach of first extracting a low-dimensional, latent representation of neural signals, followed by training a movement predictor based on the latent signals.To stabilize the resulting BMI against continuous changes in the neural recordings, we introduce a novel approach based on the Generative Adversarial Network (GAN) architecture BID17 . This new approach, the Adversarial Domain Adaptation Network (ADAN), focuses on the probability distribution function (PDF) of the residuals of the reconstructed neural signals to align the residual's PDF at a later day to the PDF of the first day the BMI was calculated. The alignment of residual PDFs results in the alignment of the PDFs of the neural data and of their latent representation across multiple days. We show that this method results in a significantly more stable performance of the BMI over time than the stability achieved using several other domain adaptation methods. The use of an ADAN thus results in a BMI that remains stable and consistent to the user over long periods of time. A successful domain adaptation of the neural data eliminates the need for frequent recalibration of the BMI, which remains fixed. This strategy is expected to alleviate the cognitive burden on the user, who would no longer need to learn novel strategies to compensate for a changing interface. We address the problem of stabilizing a fixed Brain-Machine Interface against performance deterioration due to the loss and turnover of recorded neural signals. We introduce a new approach to extracting a low-dimensional latent representation of the neural signals while simultaneously inferring movement intent. We then implement various domain adaption methods to stabilize the latent representation over time, including Canonical Correlation Analysis and the minimization of a Kullback-Leibler divergence. These two methods provide comparable improvement in the performance of the interface. We find that an Adversarial Domain Adaptation Network trained to match the empirical probability distribution of the residuals of the reconstructed neural recordings restores the latent representation of neural trajectories and outperforms the two methods based on latent variables, while requiring remarkably little data to solve the domain adaptation problem. In addition, ADAN solves the domain adaptation problem in a manner that is not task specific, and thus is potentially applicable to unconstrained movements.Here we report on improvements in interface stability obtained offline, without a user in the loop. Online, closed-loop performance is not particularly well correlated with offline accuracy; in an online evaluation of performance, the user's ability to adapt at an unknown rate and to an unknown extent to an imperfect BMI obscures the performance improvements obtained with domain adaptation. Although the open-loop performance improvement demonstrated here is encouraging, additional experiments, both open and closed-loop, with additional animals and involving additional tasks, are required to fully validate our results and to establish that the improvements demonstrated here facilitate the sustained use of a brain-machine interface.
We implement an adversarial domain adaptation network to stabilize a fixed Brain-Machine Interface against gradual changes in the recorded neural signals. Describes a novel approach for implanted brain-machine interface in order to address calibration problem and covariate shift.  The authors define a BMI that uses an autoencoder and then address the problem of data drift in BMI.