Advantages of closed-loop calibration in intracortical brain–computer interfaces for people with tetraplegia

Abstract

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 vs. 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.

Silicon-Integrated High-Density Electrocortical Interfaces

“…An implantable recording and stimulation system can contain a digital signal processor capable of deciding when to stimulate [77]. Other applications of cortical stimulation include closed-loop brain computer interfaces (BCI) which aim to generate functional maps of the brain [78], restore somatosensory feedback [76], restore motor control to tetraplegics [79], aid stroke survivors [80], [81], restore vision [82], reduce pain [73], or even change emotional state [83].…”

Closed-Loop Decoder Adaptation Shapes Neural Plasticity for Skillful Neuroprosthetic Control

“…Much as neural adaptation has proven beneficial, recent work shows the potential promise of adaptive decoders to improve performance. Closed-loop decoder adaptation (CLDA)-modification of decoder parameters based on closed-loop performance (Dangi et al, 2013)-can reliably improve performance (Taylor et al, 2002;Li et al, 2011;Gilja et al, 2012;Orsborn et al, 2012;Jarosiewicz et al, 2013). CLDA may be particularly useful for compensating for nonstationary neural recordings (Li et al, 2011) and has been shown to produce high-performance BMI control for many months independent of stationary neural recordings (Gilja et al, 2012).…”

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

“…This value is equal to the expected increase in the threshold crossing rate above baseline when the participant imagined moving in the preferred direction of that channel. The third metric was the normalized modulation depth (Hochberg et al, 2012; Jarosiewicz et al, 2013), defined as ∥ H i ∥ /std(ε i ) which is equal to the reciprocal of the coefficient of variation. We note that the normalized modulation depth (NMD) metric is related to the signal-to-noise ratio (SNR) as $$SNR=\frac{{\sigma }_{signal}^2}{{\sigma }_{noise}^2}=\frac{\Vert H_i{\Vert }^2∕2}{var\left({\epsilon }_i\right)}=\frac{NM{D}^2}{2},$$ assuming uniform sampling of cursor directions.…”

“…The first parameter is the matrix of preferred directions of each channel, H , as defined above. The second is the error covariance matrix in reconstructing the threshold crossing rates: $$Q=(z-b-Hd){(z-b-Hd)}^T.$$ The third matrix is a “state model” which defines the linear relation between the intended movement directions at consecutive time points, d(t) = Ad ( t −1), and the fourth set is the error in this state model: $$W=\frac{1}{N}{\Sigma }_{t=1}^N(d\left(t\right)-Ad(t-1)){(d\left(t\right)-Ad(t-1))}^T.$$ These last two values were set to constant values ( A = 0.965 I and W = 0.03 I where I is the identity matrix) as in previous studies (Hochberg et al, 2012; Jarosiewicz et al, 2013) to provide a balance between responsiveness and smoothness of decoded cursor movement. For all time steps of each trial in each session, we decoded the intended movement using a Kalman filter that was calibrated using all other trials in that session.…”