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

Abstract

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.

Conflict of interest statement

Competing Financial Interests: Dr. Hochberg has a financial interest in Synchron Med, Inc., a company developing a minimally invasive implantable brain device that could help paralyzed patients achieve direct brain control of assisted technologies. Dr. Hochberg’s interests were reviewed and are managed by Massachusetts General Hospital, Partners HealthCare, and Brown University in accordance with their conflict of interest policies.

Figures

Fig. 1

Schematic representation of a typical iBCI calibration protocol in humans vs. the rapid calibration sequence. Each black arrow represents a step where a technician currently intervenes. Hexagonal and rounded steps refer to offline and online steps, respectively. The BCI user does not actively participate in offline steps. Red, yellow and green steps refer to the setup, calibration, and use of the BCI system, respectively. Top. Typical use of an intracortical BCI system has several steps. First, the user is connected to the computer and the software is initialized. The user then performs open-loop imagery; decoders are seeded using this initial data; and then closed-loop calibration proceeds, and may be repeated several times depending on the protocol being used. Bottom. No explicit open-loop imagery step is required, and the decoder calibration steps occur without the need for technician oversight or intervention.

Fig. 2

Rapid calibration during the Radial-8 task. Participant T10 performed a three-minute calibration sequence with either the GP-DKF (A) or the Kalman (B) decoders. Targets were acquired when the cursor overlapped the target for 300ms, with a 15-second timeout. (C). Multiple calibration sequences from participants T5 (red), T8 (green) and T10 (blue) were done using the GP-DKF decoder, and with the Kalman decoder in T10 (grey). The thin dark lines are the average amount of time to acquire a target across all blocks (shaded area is +/− 1 standard deviation). Averages are computed by binning data in 15-second increments, with a 5 second offset from calibration start. (D). Example cursor trajectories during calibration using the GP-DKF decoder (participant T5). The brightness goes from light to dark as time elapses during the 60 second interval of closed-loop neural cursor control. Data were used from trial days: 30 and 33 (T5); 662, 665, (T8); and 112, 203, 215, 236 (T10).

Fig. 3

Bootstrapped angular error as a function of each participant’s neural features used to simulate decoding. (A) For each experimental session, a decoder was trained using a random subsample, without replacement, and then used to mean angular error for another subsample of the same size. Decoder predictions were bootstrapped 100 times. Intuitively, the decoding performance would approach 90 degrees as the amount of data approaches zero, since a poor-quality decoder (e.g. one with limited training data) would decode a random angular error between 0 (perfect) and 180 (opposite) degrees to the target. The average of a large number of random angles drawn between 0 and 180 degrees would average to be 90 degrees. (B) The angular error curves were fit using a decaying exponential, and the amount of data required to achieve 95% saturation of the peak angular error performance was computed. The bootstrapped mean angular error saturated in less than 3 minutes for all three users. (mean +/− standard deviation; participant T5: 41.6s +/− 4.6s; T8: 83.3s +/− 9.9s; T10: 69.3s +/− 7.5s).

Fig. 4

Bit rate comparisons between decoder methods. After calibrating the GP-DKF decoder for 3 (T5 - red, T10 - blue) or 4 (T8 - green) minutes, the decoder parameters were locked, and the participants selected targets using the Grid Task. Bit-rates were computed for the GP-DKF decoder and compared to the performance using a Kalman filter using ~10 minutes of calibration data with an explicit open-loop imagery step[16]. Bit rates were not statistically different when comparing decoders within participants (Wilcoxon-rank test). Data were used from trial days: 30 and 33 (T5); 660, 662, 665 (T8); and 84, 112 (T10).

Fig. 5

Time to target acquisition as a function of time on T5’s first attempt at closed-loop neural control (Trial day 30, block 3).