High performance communication by people with paralysis using an intracortical brain-computer interface

Abstract

Brain-computer interfaces (BCIs) have the potential to restore communication for people with tetraplegia and anarthria by translating neural activity into control signals for assistive communication devices. While previous pre-clinical and clinical studies have demonstrated promising proofs-of-concept (Serruya et al., 2002; Simeral et al., 2011; Bacher et al., 2015; Nuyujukian et al., 2015; Aflalo et al., 2015; Gilja et al., 2015; Jarosiewicz et al., 2015; Wolpaw et al., 1998; Hwang et al., 2012; Spüler et al., 2012; Leuthardt et al., 2004; Taylor et al., 2002; Schalk et al., 2008; Moran, 2010; Brunner et al., 2011; Wang et al., 2013; Townsend and Platsko, 2016; Vansteensel et al., 2016; Nuyujukian et al., 2016; Carmena et al., 2003; Musallam et al., 2004; Santhanam et al., 2006; Hochberg et al., 2006; Ganguly et al., 2011; O’Doherty et al., 2011; Gilja et al., 2012), the performance of human clinical BCI systems is not yet high enough to support widespread adoption by people with physical limitations of speech. Here we report a high-performance intracortical BCI (iBCI) for communication, which was tested by three clinical trial participants with paralysis. The system leveraged advances in decoder design developed in prior pre-clinical and clinical studies (Gilja et al., 2015; Kao et al., 2016; Gilja et al., 2012). For all three participants, performance exceeded previous iBCIs (Bacher et al., 2015; Jarosiewicz et al., 2015) as measured by typing rate (by a factor of 1.4–4.2) and information throughput (by a factor of 2.2–4.0). This high level of performance demonstrates the potential utility of iBCIs as powerful assistive communication devices for people with limited motor function.Clinical Trial No: NCT00912041DOI: http://dx.doi.org/10.7554/eLife.18554.001

Augmenting intracortical brain-machine interface with neurally driven error detectors

“…Thus, a helpful corrective intervention is to automatically “detect-and-undo” the previous target selection by deleting the previous character when an error signal is detected. To estimate the effect error auto-deletion would have on a BMI, we considered the bit-rate metric [41,52,53,84,85], which quantifies BMI communication performance. Bit-rate is defined as the rate of correct key selections (weighted by how many bits of information each selection conveys) minus a penalty for incorrect key selections based on the conservative assumption that each incorrect selection must be compensated for with a correct selection (e.g., selecting ‘delete’): $$\mathrm{italicbps}={\mathit{\text{log}}}_2\left(N-1\right)\frac{s-f}{T}$$where T is the total length of the trials, N is the number of potential targets, and s and f are the numbers of success and fail trials, respectively.…”

“…These “threshold crossings” have become the standard for BMI applications [46,50,52,55,77]. However, for supplementary analyses we sorted spikes to identify single unit activity using Blackrock Offline Spike Sorter (BOSS, Blackrock Microsystems, Inc.).…”

“…Thus, in real-world use, optimal interfaces (and optimal target sizes) will not always be present, and errors occur more often than when working with an optimally-designed lab system. To date, most efforts at increasing the performance of intracortical BMIs have focused on improving movement intention decoding [31–39,41–43,52,55]. Here we provide a proof-of-principle of a complementary approach: a parallel error detector that executes corrective interventions, thereby prevent or undoing mistakes.…”

“…However, a similar approach has not been implemented in real-time continuous control BMIs recording neural activity from motor areas (premotor and motor cortex), nor from intracortical recordings of spiking activity. It is important to determine if and how error decoding can increase the performance of such intracortical BMIs, which are to date the highest-performing BMI systems [46,49,51,52]. …”

Inferring single-trial neural population dynamics using sequential auto-encoders

“…The data were originally collected for neural prosthetic decoder calibration, as part of research testing algorithms for closed-loop neural cursor control ( [12], [25], [26]). In the Center-out-and-back task, data were collected either in motor-based control (with T7, who retained limited residual movements), or an attempted movement paradigm (with T5, who did not retain sufficient movement to reliably measure or physically control a cursor).…”