Adversarial Domain Adaptation for Stable Brain-Machine Interfaces

1 INTRODUCTION . Individuals with tetraplegia due to spinal cord injury identify restoration of hand function as their highest priority ( Anderson , 2004 ) . Over 50 % of respondents with a C1-C4 injury would be willing to undergo brain surgery to restore grasp ( Blabe et al. , 2015 ) . 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 ( Barrese et al. , 2013 ) , causes considerable variation in the actions produced by the BMI . This turnover may occur within a single day ( Downey et al. , 2018 ) , and is estimated to be on the order of 40 % over two weeks ( Dickey et al. , 2009 ) . 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 ( Ajiboye et al. , 2017 ) . 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 ( Gallego et al. , 2017 ) . 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 ) ( Yu et al. , 2009 ; Shenoy et al. , 2013 ; Sadtler et al. , 2014 ; Gallego et al. , 2017 ) ; more recently , autoencoders ( AEs ) have been used for the nonlinear dimensionality reduction of neural signals ( Pandarinath et al. , 2018 ) . 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 ( Goodfellow et al. , 2014 ) . 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 . 2 RELATED WORK . Current approaches to solving the stability problem for BMIs based on spiking activity recorded using chronically implanted multi-electrode arrays include gradually updating interface parameters using an exponentially weighted sliding average ( Orsborn et al. , 2012 ; Dangi et al. , 2013 ) , adjusting interface parameters by tracking recording nonstationarities ( Zhang & Chase , 2013 ; Bishop et al. , 2014 ) or by retrospectively inferring the user intention among a set of fixed targets ( Jarosiewicz et al. , 2015 ) , cancelling out neural fluctuations by projecting the recorded activity onto a very lowdimensional space ( Nuyujukian et al. , 2014 ) , training the interface with large data volumes collected over a period of several months to achieve robustness against future changes in neural recordings ( Sussillo et al. , 2016 ) , and a semi-unsupervised approach based on aligning the PDF of newly predicted movements to a previously established PDF of typical movements ( Dyer et al. , 2017 ) . Other approaches , more similar to ours , are based on the assumption that the relationship between latent dynamics and movement intent will remain stable despite changes in the recorded neural signals . Recent studies reveal the potential of latent dynamics for BMI stability . Kao et al . ( 2017 ) use past information about population dynamics to partially stabilize a BMI even under severe loss of recorded neurons , by aligning the remaining neurons to previously learned dynamics . Pandarinath et al . ( 2018 ) extract a single latent space from concatenating neural recordings over five months , and show that a predictor of movement kinematics based on these latent signals is reliable across all the recorded sessions . 3 EXPERIMENTAL SETUP . A male rhesus monkey ( Macaca mulatta ) sat in a primate chair with the forearm restrained and its hand secured into a padded , custom fit box . A torque cell with six degrees of freedom was mounted onto the box . The monkey was trained to generate isometric torques that controlled a computer cursor displayed on a screen placed at eye-level , and performed a 2D center-out task in which the cursor moved from a central target to one of eight peripheral targets equally spaced along a circle centered on the central target ( Figure 1A ) ( Oby et al. , 2012 ) . To record neural activity , we implanted a 96-channel microelectrode array ( Blackrock Microsystems , Salt Lake City , Utah ) into the hand area of primary motor cortex ( M1 ) . Prior to implanting the array , we intraoperatively identified the hand area of M1 through sulcal landmarks , and by stimulating the surface of the cortex to elicit twitches of the wrist and hand muscles . We also implanted electrodes in 14 muscles of the forearm and hand , allowing us to record the electromyograms ( EMGs ) that quantify the level of activity in each of the muscles . Data was collected in five experimental sessions spanning 16 days . All methods were approved by Northwestern Universitys IACUC and carried out in accordance with the Guide for the Care and Use of Laboratory Animals . 4 METHODS . 4.1 COMPUTATIONAL INTERFACE . Our goal is to reliably predict the actual patterns of muscle activity during task execution , based on the recorded neural signals . Similar real-time predictions of kinematics are the basis of BMIs used to provide control of a computer cursor or a robotic limb to a paralyzed person ( Taylor et al. , 2002 ; Hochberg et al. , 2006 ; Collinger et al. , 2013 ) . Predictions of muscle activity have been used to control the intensity of electrical stimulation of muscles that are temporarily paralyzed by a pharmacological peripheral nerve block ( Ethier et al. , 2012 ) , a procedure that effectively bypasses the spinal cord to restore voluntary control of the paralyzed muscles . Similar methods have been attempted recently in humans ( Bouton et al. , 2016 ; Ajiboye et al. , 2017 ) . The BMI is a computational interface that transforms neural signals into command signals for movement control , in this case the EMG patterns . Here we propose an interface that consists of two components , a neural autoencoder ( AE ) and an EMG predictor ( Figure 1B ) . The AE is a fully connected multilayer network consisting of an input layer , five layers of hidden units , and an output layer . The reconstruction aims at minimizing the mean square error ( MSE ) between input and output signals . Units in the latent and output layers implement linear readouts of the activity of the preceding layer . Units in the remaining hidden layers implement a linear readout followed by an exponential nonlinearity . To provide inputs to the AE , we start with neural data st consisting of the spike trains recorded from n electrodes at time t. We bin neural spikes at 50 ms intervals and apply a Gaussian filter with a standard deviation of 125 ms to the binned spike counts to obtain n-dimensional smoothed firing rates xt . The output layer provides n-dimensional estimates x̂t of the inputs xt . The latent layer activity zt consist of l latent variables , with l < n. The EMG data yt is the envelope of the muscle activity recorded from m muscles , with m < n. The l-dimensional latent activity zt is mapped onto the m-dimensional EMGs through a long-short term memory ( LSTM ) ( Hochreiter & Schmidhuber , 1997 ) layer with m units , followed by a linear layer ŷt =W TLSTM ( zt ) . To train the model , we minimize a loss function L that simultaneously accounts for two losses : Lx : Rn → R+ is the MSE of the reconstruction of the smooth firing rates , and Ly : Rm → R+ is the MSE of the EMG predictions : L = λLx + Ly = 1 T T∑ t=1 ( λ||x̂t − xt||2 + ||ŷt − yt||2 ) ( 1 ) Here T is the number of time samples , and t labels the time-ordered data points that constitute the training set . The factor λ that multiplies the AE loss adjusts for the different units and different value ranges of firing rates and EMGs ; it equalizes the contributions of the two terms in the loss function so that the learning algorithm does not prioritize one over the other . The value of λ is updated for each new training iteration ; it is computed as the ratio λ = L y Lx of the respective losses at the end of the preceding iteration . Once the neural AE and the EMG predictor networks have been trained on the data acquired on the first recording session , indicated as day-0 , their weights remain fixed . 4.2 DOMAIN ADAPTATION . To stabilize a fixed BMI , we need to align the latent space of later days to that of the first day , when the fixed interface was initially built . This step is necessary to provide statistically stationary inputs to the EMG predictor . We first use two different approaches to align latent variables across days : Canonical Correlation Analysis ( CCA ) between latent trajectories and Kullback-Leibler ( KL ) divergence minimization between latent distributions . We then use an ADAN to align the distributions of the residuals of the reconstructed neural data . This procedure results in the alignment of the distributions of the neural data and of their latent representation across days .

Summary: This contribution describes a novel approach for implanted brain-machine interface in order to address calibration problem and covariate shift. A latent representation is extracted from SEEG signals and is the input of a LTSM trained to predict muscle activity. To mitigate the variation of neural activities across days, the authors compare a CCA approach, a Kullback-Leibler divergence minimization and a novel adversarial approach called ADAN.