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BCI Decoder Performance Comparison of an LSTM Recurrent Neural Network and a Kalman Filter in Retrospective Simulation

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BCI Decoder Performance Comparison of an LSTM Recurrent Neural Network and a Kalman Filter in Retrospective Simulation BCI decoder performance comparison of an LSTM recurrent neural network and a Kalman filter in retrospective simulation Tommy Hosman, Marco Vilela, Daniel Milstein, Jessica N. Kelemen, David M. Brandman, Leigh R. Hochberg, John D. Simeral, IEEE Senior Member Abstract— Intracortical brain computer interfaces (iBCIs) imagined or attempted movements of their paralyzed hand using linear Kalman decoders have enabled individuals with [1]–[3]. Toward clinical viability, human iBCI research has paralysis to control a computer cursor for continuous point- also demonstrated rapid decoder calibration [4], sustained and-click typing on a virtual keyboard, browsing the internet, robust neural decoding [5], [6] and increasingly fast and and using familiar tablet apps. However, further advances are accurate cursor control [5]–[7] enabling online chat, internet needed to deliver iBCI-enabled cursor control approaching browsing, and the use of apps on a consumer tablet [8], [9]. able-bodied performance. Motivated by recent evidence that Nonetheless, human iBCIs have not yet provided cursor nonlinear recurrent neural networks (RNNs) can provide performance equivalent to able-bodied hand movements. higher performance iBCI cursor control in nonhuman primates Candidates for iBCIs, particularly individuals with tetraplegia (NHPs), we evaluated decoding of intended cursor velocity or locked-in syndrome, have a strong interest in using BCIs from human motor cortical signals using a long-short term with high communication rates [10], [11], motivating further memory (LSTM) RNN trained across multiple days of multi- research to improve BCI-enabled cursor performance. In electrode recordings. Running simulations with previously clinical trials, iBCIs have generally applied linear Kalman recorded intracortical signals from three BrainGate iBCI trial participants, we demonstrate an RNN that can substantially decoders to translate multielectrode intracortical signals into increase bits-per-second metric in a high-speed cursor-based commands for assistive devices [5], [6], [9]. Recent results target selection task as well as a challenging small-target high- using nonlinear decoders in preliminary work in humans and accuracy task when compared to a Kalman decoder. These in non-human primates (NHPs) show promise for even results indicate that RNN decoding applied to human higher performance [12]–[16]. Specifically, in [13] a intracortical signals could achieve substantial performance nonlinear recurrent neural network (RNN) variant advances in continuous 2-D cursor control and motivate a real- outperformed the Kalman in electrode dropping experiments time RNN implementation for online evaluation by individuals and achieved an increase in performance relative to the with tetraplegia. Kalman decoder in head-to-head comparisons. Motivated by the cursor performance gains with nonlinear approaches, we I. INTRODUCTION developed RNN methods for decoding human motor cortical Intracortical brain computer interfaces (iBCIs) have signals into 2-dimensional cursor kinematics and evaluated enabled individuals living with severe motor disability to performance relative to a steady-state Kalman decoder in achieve point and click control of a computer cursor using simulated target selection tasks. II. METHODS * Caution: Investigational device. Limited by federal law to This study evaluated the relative performance of a investigational use. Research supported by Office of Research and Development, Rehab. R&D Service, Dept. of Veterans Affairs (N9288C, Kalman filter and a long-short term memory (LSTM) RNN in A2295R, B6453R, P1155R), NINDS (U01NS098968), NIH (S10 ODO1636 achieving fast and accurate cursor movements when for cluster computation), ECOR of Massachusetts Gen. Hosp. (MGH), decoding human intracortical neural signals recorded from MGH-Deane Institute. The content is solely the responsibility of the authors implanted microelectrode arrays. The Institutional Review and does not necessarily represent the official views of the NIH, the Boards of Partners HealthCare/Massachusetts General Department of Veterans Affairs or the US Government. T. Hosman ([email protected]) and M. Vilela Hospital, Brown University, and Providence VA Medical ([email protected]) are with the School of Engineering and Carney Center granted permission for this study. Institute for Brain Science, Brown University, Providence, RI 02912. D. Milstein ([email protected]) was with the Department of A. Participants Computer Science and Carney Institute for Brain Science, Brown This study was performed through simulations University. incorporating intracortical neural signals previously recorded J. Kelemen is with the Department of Neurology at Massachusetts from three participants with tetraplegia (T7, T9, T10) General Hospital (MGH), Boston, MA. D. Brandman was with the School of Engineering, Brown University. He enrolled in a pilot clinical trial of the BrainGate* iBCI is now with the Department of Surgery (Neurosurgery), Dalhousie (ClinicalTrials.gov identifier: NCT00912041). Participants University, Halifax, Canada. had two 96-channel microelectrode arrays (Blackrock L.R. Hochberg ([email protected]) and J.D. Simeral Microsystems, Salt Lake City, UT) implanted [3] in the ([email protected]) are with the VA Rehabilitation R&D Center for dominant hand area of the precentral gyrus except T10 whose Neurorestoration and Neurotechnology, Dept. of VA Med. Ctr., Providence, RI, and the School of Engineering and Carney Institute for Brain Science at second array was placed in the caudal middle frontal gyrus. Brown University. L.R.H. is also with the Dept. of Neurology at MGH, Boston, MA and the Dept. of Neurology, Harvard Medical School, Boston. This preprint is the “accepted” version by IEEE NER. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Fig. 1. (A) The process of simulating a Grid task with intracortical recordings. Given a cursor-to-target vector (black arrow), the simulator samples neural data with similarly labelled angle and distance-from-target. The decoder uses this data to make a predicted cursor update (red arrow). (B) Online and simulated cursor trajectories from a Radial-8 block. (C) A typical distribution of 30 sessions prior to a test sesion (rightmost line) demonstrating a typical amount of training data and and its distribution over days. (D) Data segmentation between training, validation and testing. Decoders were built using the training data, spanning D-days back from the test session. The trained decoders used validation blocks to optimize the gain and smoothing parameters for each decoder. Simulated comparisons were run from neural data sampled from the test blocks. B. Human Intracortical Neural Recording into a distance and direction to the (simulator-generated) Decoders in this study were trained and evaluated using target (square on the grid). To determine the next-step cursor intracortical microelectrode neural data originally recorded kinematics, the simulator drew neural features from a from participants in BrainGate research sessions in which distribution of the participant’s recorded 20 ms features neural features (multi-unit threshold-crossing spike rates and whose originally-recorded labels had a similar cursor-to- power in the spike-band LFP, 250 Hz – 5000 Hz) [4] were target target vector (regardless of the absolute target computed every 20 ms and decoded by a Kalman filter into position). The decoder-under-test used this neural data instantaneous closed-loop cursor kinematics. In those original sample to compute a new velocity vector which was applied data sessions, participants used the Kalman iBCI to complete to move the cursor. The trial advanced in 20 ms time steps several closed-loop point-and-select cursor tasks: out-and- until the decoded cursor movements successfully acquired back Radial-4 or -8, Grid, and Random Target (mFitts) tasks the target. Alternatively, an error trial resulted if the cursor [2], [4], [7], [17], [18]. Offline evaluation of alternative dwelled on the wrong target or a timeout expired without a decoding algorithms was enabled by labeling (and saving) target acquisition; dwell and timeout durations differed with each 20 ms multielectrode neural feature with its task as described below. The simulator then spawned the next corresponding online instantaneous 2-D cursor-to-target target location and the task continued until 2 simulated vector. The offline analyses looked at task blocks which minutes had elapsed. This procedure enabled offline lasted 3-5 minutes (a “block”) consisting of at least 16 target decoding of participant-specific neural signals rather than acquisitions (“trials”). Neural features were z-scored per artificially-created model-based data (e.g., cosine tuning). block and cursor-to-target vectors for each session were The sampling process enabled the generation of novel data- normalized by the 99th percentile of the session’s labels. This driven cursor trajectories responsive to instantaneous cursor scaling kept the majority of the 2D cursor-to-target vectors in and target positions. the range of [±1, ±1], and prevented outliers from D. Decoder Assessment Overall
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