Although brain-computer interfaces (BCIs) can be used in several different ways to restore communication, communicative BCI has not approached the rate or success of natural human speech. Electrocorticography (ECoG) has precise spatiotemporal resolution that enables recording of brain activity that is distributed over a wide area of cortex, such as during speech production. In this study, we investigated words that span the entire set of phonemes in the General American accent using ECoG with 4 subjects. We classified phonemes with up to 36% accuracy when classifying all phonemes and up to 63% accuracy for a single phoneme. Further, misclassified phonemes follow articulation organization described in phonology literature, aiding classification of whole words. Precise temporal alignment to phoneme onset was crucial for classification success. We identified specific spatiotemporal features that aid classification, which could guide future applications. Word identification was equivalent to information transfer rates as high as 3.0 bits/s (33.6 words/min), supporting pursuit of speech articulation for BCI control.

Objective Brain machine interfaces (BMIs) have the potential to restore movement to people with paralysis. However, a clinically-viable BMI must enable consistently accurate control over time spans ranging from years to decades, which has not yet been demonstrated. Most BMIs that use single-unit spikes as inputs will experience degraded performance over time without frequent decoder re-training. Two other signals, local field potentials (LFPs) and multi-unit spikes (MSPs), may offer greater reliability over long periods and better performance stability than single-unit spikes. Here, we demonstrate that LFPs can be used in a biomimetic BMI to control a computer cursor. Approach We implanted two rhesus macaques with intracortical microelectrodes in primary motor cortex. We recorded LFP and MSP signals from the monkeys while they performed a continuous reaching task, moving a cursor to randomly-placed targets on a computer screen. We then used the LFP and MSP signals to construct biomimetic decoders for control of the cursor. Main results Both monkeys achieved high-performance, continuous control that remained stable or improved over nearly 12 months using an LFP decoder that was not retrained or adapted. In parallel, the monkeys used MSPs to control a BMI without retraining or adaptation and had similar or better performance, and that predominantly remained stable over more than six months. In contrast to their stable online control, both LFP and MSP signals showed substantial variability when used offline to predict hand movements. Significance Our results suggest that the monkeys were able to stabilize the relationship between neural activity and cursor movement during online BMI control, despite variability in the relationship between neural activity and hand movements.

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The human motor system is capable of remarkably precise control of movements-consider the skill of professional baseball pitchers or surgeons. This precise control relies upon stable representations of movements in the brain. Here, we investigated the stability of cortical activity at multiple spatial and temporal scales by recording local field potentials (LFPs) and action potentials (multiunit spikes, MSPs) while two monkeys controlled a cursor either with their hand or directly from the brain using a brain-machine interface. LFPs and some MSPs were remarkably stable over time periods ranging from 3 d to over 3 years; overall, LFPs were significantly more stable than spikes. We then assessed whether the stability of all neural activity, or just a subset of activity, was necessary to achieve stable behavior. We showed that projections of neural activity into the subspace relevant to the task (the "task-relevant space") were significantly more stable than were projections into the task-irrelevant (or "task-null") space. This provides cortical evidence in support of the minimum intervention principle, which proposes that optimal feedback control (OFC) allows the brain to tightly control only activity in the task-relevant space while allowing activity in the taskirrelevantspacetovarysubstantiallyfromtrialtotrial.Wefoundthatthebrainappearscapableofmaintainingstablemovementrepresentations for extremely long periods of time, particularly so for neural activity in the task-relevant space, which agrees with OFC predictions.

Background A significant factor in impaired movement caused by stroke is the inability to activate muscles independently. While the pathophysiology behind this abnormal co-activation is not clear, reducing the co-activation could improve overall arm function. A myoelectric computer interface (MCI), which maps EMG signals to cursor movement, could be used as a treatment to help retrain muscle activation patterns. Objective To investigate the use of MCI training to reduce abnormal muscle co-activation in chronic stroke survivors. Methods Five healthy subjects and five stroke survivors with hemiparesis participated in multiple sessions of MCI training. The level of arm impairment in stroke survivors was assessed using the upper extremity portion of Fugl-Meyer Motor Assessment (FMA-UE). Subjects performed isometric activations of up to five muscles. Activation of each muscle was mapped to different directions of cursor movement. The MCI specifically targeted one pair of muscles in each subject for reduction of co-activation. Results Both healthy subjects and stroke survivors learned to reduce abnormal co-activation of the targeted muscles with MCI training. Three out of five stroke survivors exhibited objective reduction in arm impairment as well (improvement in FMA-UE of 3 points in each of these subjects). Conclusions These results suggest that the MCI was an effective tool in directly retraining muscle activation patterns following stroke.