Reprint of "Non-causal spike filtering improves decoding of movement intention for intracortical BCIs"

Quantifying neuro-motor correlations during awake deep brain stimulation surgery using markerless tracking

The expanding application of deep brain stimulation (DBS) therapy both drives and is informed by our growing understanding of disease pathophysiology and innovations in neurosurgical care. Neurophysiological targeting, a mainstay for identifying optimal, motor responsive targets, has remained largely unchanged for decades. Utilizing deep learning-based computer vision and related computational methods, we developed an effective and simple intraoperative approach to objectively correlate neural signals with movements, automating and standardizing the otherwise manual and subjective process of identifying ideal DBS electrode placements. Kinematics are extracted from video recordings of intraoperative motor testing using a trained deep neural network and compared to multi-unit activity recorded from the subthalamic nucleus. Neuro-motor correlations were quantified using dynamic time warping with the strength of a given comparison measured by comparing against a null distribution composed of related neuro-motor correlations. This objective measure was then compared to clinical determinations as recorded in surgical case notes. In seven DBS cases for treatment of Parkinson's disease, 100 distinct motor testing epochs were extracted for which clear clinical determinations were made. Neuro-motor correlations derived by our automated system compared favorably with expert clinical decision making in post-hoc comparisons, although follow-up studies are necessary to determine if improved correlation detection leads to improved outcomes. By improving the classification of neuro-motor relationships, the automated system we have developed will enable clinicians to maximize the therapeutic impact of DBS while also providing avenues for improving continued care of treated patients.

The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models

The Kalman filter provides a simple and efficient algorithm to compute the posterior distribution for state-space models where both the latent state and measurement models are linear and gaussian. Extensions to the Kalman filter, including the extended and unscented Kalman filters, incorporate linearizations for models where the observation model p ( observation | state ) is nonlinear. We argue that in many cases, a model for p ( state | observation ) proves both easier to learn and more accurate for latent state estimation. Approximating p ( state | observation ) as gaussian leads to a new filtering algorithm, the discriminative Kalman filter (DKF), which can perform well even when p ( observation | state ) is highly nonlinear and/or nongaussian. The approximation, motivated by the Bernstein-von Mises theorem, improves as the dimensionality of the observations increases. The DKF has computational complexity similar to the Kalman filter, allowing it in some cases to perform much faster than particle filters with similar precision, while better accounting for nonlinear and nongaussian observation models than Kalman-based extensions. When the observation model must be learned from training data prior to filtering, off-the-shelf nonlinear and nonparametric regression techniques can provide a gaussian model for p ( observation | state ) that cleanly integrates with the DKF. As part of the BrainGate2 clinical trial, we successfully implemented gaussian process regression with the DKF framework in a brain-computer interface to provide real-time, closed-loop cursor control to a person with a complete spinal cord injury. In this letter, we explore the theory underlying the DKF, exhibit some illustrative examples, and outline potential extensions.

Rapid calibration of an intracortical brain-computer interface for people with tetraplegia

OBJECTIVE

Brain-computer interfaces (BCIs) can enable individuals with tetraplegia to communicate and control external devices. Though much progress has been made in improving the speed and robustness of neural control provided by intracortical BCIs, little research has been devoted to minimizing the amount of time spent on decoder calibration.

APPROACH

We investigated the amount of time users needed to calibrate decoders and achieve performance saturation using two markedly different decoding algorithms: the steady-state Kalman filter, and a novel technique using Gaussian process regression (GP-DKF).

MAIN RESULTS

Three people with tetraplegia gained rapid closed-loop neural cursor control and peak, plateaued decoder performance within 3 min of initializing calibration. We also show that a BCI-naïve user (T5) was able to rapidly attain closed-loop neural cursor control with the GP-DKF using self-selected movement imagery on his first-ever day of closed-loop BCI use, acquiring a target 37 s after initiating calibration.

SIGNIFICANCE

These results demonstrate the potential for an intracortical BCI to be used immediately after deployment by people with paralysis, without the need for user learning or extensive system calibration.

Robust Closed-Loop Control of a Cursor in a Person with Tetraplegia using Gaussian Process Regression

Intracortical brain computer interfaces can enable individuals with paralysis to control external devices through voluntarily modulated brain activity. Decoding quality has been previously shown to degrade with signal nonstationarities-specifically, the changes in the statistics of the data between training and testing data sets. This includes changes to the neural tuning profiles and baseline shifts in firing rates of recorded neurons, as well as nonphysiological noise. While progress has been made toward providing long-term user control via decoder recalibration, relatively little work has been dedicated to making the decoding algorithm more resilient to signal nonstationarities. Here, we describe how principled kernel selection with gaussian process regression can be used within a Bayesian filtering framework to mitigate the effects of commonly encountered nonstationarities. Given a supervised training set of (neural features, intention to move in a direction)-pairs, we use gaussian process regression to predict the intention given the neural data. We apply kernel embedding for each neural feature with the standard radial basis function. The multiple kernels are then summed together across each neural dimension, which allows the kernel to effectively ignore large differences that occur only in a single feature. The summed kernel is used for real-time predictions of the posterior mean and variance under a gaussian process framework. The predictions are then filtered using the discriminative Kalman filter to produce an estimate of the neural intention given the history of neural data. We refer to the multiple kernel approach combined with the discriminative Kalman filter as the MK-DKF. We found that the MK-DKF decoder was more resilient to nonstationarities frequently encountered in-real world settings yet provided similar performance to the currently used Kalman decoder. These results demonstrate a method by which neural decoding can be made more resistant to nonstationarities.

Review: Human Intracortical Recording and Neural Decoding for Brain-Computer Interfaces

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.

Motor Task Detection From Human STN Using Interhemispheric Connectivity

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders, such as Parkinson's disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient's behavior. Subthalamic nucleus (STN) local field potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. In this paper, we introduce a behavior detection method capable of asynchronously detecting the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from the STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity for detecting finger movement. Our experimental results, using the recordings from 11 patients with PD, demonstrate that this approach is applicable for behavior detection in the majority of subjects (average area under curve of 70±12%).