Brain-computer interface (BCI) neurotechnology has the potential to reduce disability associated with paralysis by translating neural activity into control of assistive devices. Surveys of potential end-users have identified key BCI system features, including high accuracy, minimal daily setup, rapid response times, and multifunctionality. These performance characteristics are primarily influenced by the BCI's neural decoding algorithm, which is trained to associate neural activation patterns with intended user actions. Here, we introduce a new deep neural network decoding framework for BCI systems enabling discrete movements that addresses these four key performance characteristics. Using intracortical data from a participant with tetraplegia, we provide offline results demonstrating that our decoder is highly accurate, sustains this performance beyond a year without explicit daily retraining by combining it with an unsupervised updating procedure, responds faster than competing methods, and can increase functionality with minimal retraining by using a technique known as transfer learning. We then show that our participant can use the decoder in real-time to reanimate his paralyzed forearm with functional electrical stimulation (FES), enabling accurate manipulation of three objects from the grasp and release test (GRT). These results demonstrate that deep neural network decoders can advance the clinical translation of BCI technology.

Why is it that behaviors that rely on control, so striking in their diversity and flexibility, are also subject to such striking limitations? Typically, people cannot engage in more than a few — and usually only a single — control-demanding task at a time. This limitation was a defining element in the earliest conceptualizations of controlled processing, it remains one of the most widely accepted axioms of cognitive psychology, and is even the basis for some laws (e.g., against the use of mobile devices while driving). Remarkably, however, the source of this limitation is still not understood. Here, we examine one potential source of this limitation, in terms of a tradeoff between the flexibility and efficiency of representation (“multiplexing”) and the simultaneous engagement of different processing pathways (“multitasking”). We show that even a modest amount of multiplexing rapidly introduces cross-talk among processing pathways, thereby constraining the number that can be productively engaged at once. We propose that, given the large number of advantages of efficient coding, the human brain has favored this over the capacity for multitasking of control-demanding processes.

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We investigate human error dynamics in sequential two-alternative choice tasks. When subjects repeatedly discriminate between two stimuli, their error rates and reaction times (RTs) systematically depend on prior sequences of stimuli. We analyze these sequential effects on RTs, separating error and correct responses, and identify a sequential RT tradeoff: a sequence of stimuli which yields a relatively fast RT on error trials will produce a relatively slow RT on correct trials and vice versa. We reanalyze previous data and acquire and analyze new data in a choice task with stimulus sequences generated by a first-order Markov process having unequal probabilities of repetitions and alternations. We then show that relationships among these stimulus sequences and the corresponding RTs for correct trials, error trials, and averaged over all trials are significantly influenced by the probability of alternations; these relationships have not been captured by previous models. Finally, we show that simple, sequential updates to the initial condition and thresholds of a pure drift diffusion model can account for the trends in RT for correct and error trials. Our results suggest that error-based parameter adjustments are critical to modeling sequential effects.

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Highlights d Following spinal cord injury, subperceptual touch signals affect the human motor cortex d A brain-computer interface uses subperceptual signals to restore the sense of touch d Sensorimotor function is further enhanced using demultiplexed sensorimotor signals d Touch-regulated grip force can automate movement cascades and grip reanimation

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Individuals with tetraplegia identify restoration of hand function as a critical, unmet need to regain their independence and improve quality of life. Brain-Computer Interface (BCI)-controlled Functional Electrical Stimulation (FES) technology addresses this need by reconnecting the brain with paralyzed limbs to restore function. In this study, we quantified performance of an intuitive, cortically-controlled, transcutaneous FES system on standardized object manipulation tasks from the Grasp and Release Test (GRT). We found that a tetraplegic individual could use the system to control up to seven functional hand movements, each with >95% individual accuracy. He was able to select one movement from the possible seven movements available to him and use it to appropriately manipulate all GRT objects in real-time using naturalistic grasps. With the use of the system, the participant not only improved his GRT performance over his baseline, demonstrating an increase in number of transfers for all objects except the Block, but also significantly improved transfer times for the heaviest objects (videocassette (VHS), Can). Analysis of underlying motor cortex neural representations associated with the hand grasp states revealed an overlap or non-separability in neural activation patterns for similarly shaped objects that affected BCI-FES performance. These results suggest that motor cortex neural representations for functional grips are likely more related to hand shape and force required to hold objects, rather than to the objects themselves. These results, demonstrating multiple, naturalistic functional hand movements with the BCI-FES, constitute a further step toward translating BCI-FES technologies from research devices to clinical neuroprosthetics.