Conscious visual perception is proposed to arise from the selective synchronization of functionally specialized but widely distributed cortical areas. It has been suggested that different frequency bands index distinct canonical computations. Here, we probed visual perception on a fine-grained temporal scale to study the oscillatory dynamics supporting prefrontal-dependent sensory processing. We tested whether a predictive context that was embedded in a rapid visual stream modulated the perception of a subsequent near-threshold target. The rapid stream was presented either rhythmically at 10 Hz, to entrain parietooccipital alpha oscillations, or arrhythmically. We identified a 2-to 4-Hz delta signature that modulated posterior alpha activity and behavior during predictive trials. Importantly, deltamediated top-down control diminished the behavioral effects of bottom-up alpha entrainment. Simultaneous source-reconstructed EEG and cross-frequency directionality analyses revealed that this delta activity originated from prefrontal areas and modulated posterior alpha power. Taken together, this study presents converging behavioral and electrophysiological evidence for frontal deltamediated top-down control of posterior alpha activity, selectively facilitating visual perception.top-down control | directional cross-frequency coupling | prefrontal cortex | alpha oscillations | phase-amplitude coupling V isual perception is flexible, selective, and rapidly integrates sensory evidence with endogenous high-level expectations and predictions (1, 2). It has been suggested that rhythmic brain activity constitutes a key mechanism to coordinate information flow in the human cerebral cortex by transiently forming task-relevant largescale networks (1). However, it is currently unclear how contextual information is dynamically integrated to support visual perception. Numerous studies have shown that visual perception critically depends on prestimulus alpha-band (8-12 Hz) activity (3-7). The gating-by-inhibition hypothesis postulates that alpha serves as a mechanism to route information to task-relevant cortical sites (8) but might also be under top-down control (6, 7). However, it is currently unclear which cortical regions and mechanisms mediate the directed top-down control of alpha oscillations (2). It has been suggested that slow-frequency activity in the delta range (<5 Hz) might reflect a mechanism for endogenous attentional selection and predictions (9, 10). In particular, endogenous low-frequency entrainment is thought to reflect a substrate of top-down processing (11)(12)(13)(14). Importantly, endogenous entrainment does not require rhythmicity in the input stream but reflects an intrinsic mechanism to enable predictions (15). Several studies have demonstrated that visual perception cycles as a function of the alpha phase but only a few reports have demonstrated that multiple rhythms modulate behavior on a fine-grained temporal scale (5,(16)(17)(18)(19).At present, it is uncertain how different temporal scales interact t...

Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speaking movements into text or sound. Early demonstrations, while promising, have not yet achieved accuracies high enough for communication of unconstrainted sentences from a large vocabulary. Here, we demonstrate the first speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant, who can no longer speak intelligibly due amyotrophic lateral sclerosis (ALS), achieved a 9.1% word error rate on a 50 word vocabulary (2.7 times fewer errors than the prior state of the art speech BCI2) and a 23.8% word error rate on a 125,000 word vocabulary (the first successful demonstration of large-vocabulary decoding). Our BCI decoded speech at 62 words per minute, which is 3.4 times faster than the prior record for any kind of BCI and begins to approach the speed of natural conversation (160 words per minute). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for using intracortical speech BCIs to restore rapid communication to people with paralysis who can no longer speak.

Speaking is a sensorimotor behavior whose neural basis is difficult to study with single neuron resolution due to the scarcity of human intracortical measurements. We used electrode arrays to record from the motor cortex ‘hand knob’ in two people with tetraplegia, an area not previously implicated in speech. Neurons modulated during speaking and during non-speaking movements of the tongue, lips, and jaw. This challenges whether the conventional model of a ‘motor homunculus’ division by major body regions extends to the single-neuron scale. Spoken words and syllables could be decoded from single trials, demonstrating the potential of intracortical recordings for brain-computer interfaces to restore speech. Two neural population dynamics features previously reported for arm movements were also present during speaking: a component that was mostly invariant across initiating different words, followed by rotatory dynamics during speaking. This suggests that common neural dynamical motifs may underlie movement of arm and speech articulators.

Objective : To evaluate the potential of intracortical electrode array signals for brain-computer interfaces (BCIs) to restore lost speech, we measured the performance of classifiers trained to discriminate a comprehensive basis set for speech: 39 English phonemes. We classified neural correlates of spoken-out-loud words in the "hand knob" area of precentral gyrus, which we view as a step towards the eventual goal of decoding attempted speech from ventral speech areas in patients who are unable to speak. Approach : Neural and audio data were recorded while two BrainGate2 pilot clinical trial participants, each with two chronically-implanted 96-electrode arrays, spoke 420 different words that broadly sampled English phonemes. Phoneme onsets were identified from audio recordings, and their identities were then classified from neural features consisting of each electrode's binned action potential counts or high-frequency local field potential power. We also examined two potential confounds specific to decoding overt speech: acoustic contamination of neural signals and systematic differences in labeling different phonemes' onset times..

Intracortical brain-computer interfaces (iBCIs) require frequent recalibration to maintain robust performance due to changes in neural activity that accumulate over time. Compensating for this nonstationarity would enable consistently high performance without the need for supervised recalibration periods, where users cannot engage in free use of their device. Here we introduce a hidden Markov model (HMM) to infer what targets users are moving toward during iBCI use. We then retrain the system using these inferred targets, enabling unsupervised adaptation to changing neural activity. Our approach outperforms the state of the art in large-scale, closed-loop simulations over two months and in closed-loop with a human iBCI user over one month. Leveraging an offline dataset spanning five years of iBCI recordings, we further show how recently proposed data distribution-matching approaches to recalibration fail over long time scales; only target-inference methods appear capable of enabling long-term unsupervised recalibration. Our results demonstrate how task structure can be used to bootstrap a noisy decoder into a highly-performant one, thereby overcoming one of the major barriers to clinically translating BCIs.

AbstractObjectiveTo evaluate the potential of intracortical electrode array signals for brain-computer interfaces (BCIs) to restore lost speech, we measured the performance of classifiers trained to discriminate a comprehensive basis set for speech: 39 English phonemes. We classified neural correlates of spoken-out-loud words in the “hand knob” area of precentral gyrus, which we view as a step towards the eventual goal of decoding attempted speech from ventral speech areas in patients who are unable to speak.ApproachNeural and audio data were recorded while two BrainGate2 pilot clinical trial participants, each with two chronically-implanted 96-electrode arrays, spoke 420 different words that broadly sampled English phonemes. Phoneme onsets were identified from audio recordings, and their identities were then classified from neural features consisting of each electrode’s binned action potential counts or high-frequency local field potential power. We also examined two potential confounds specific to decoding overt speech: acoustic contamination of neural signals and systematic differences in labeling different phonemes’ onset times.Main resultsA linear decoder achieved up to 29.3% classification accuracy (chance = 6%) across 39 phonemes, while a recurrent neural network classifier achieved 33.9% accuracy. Parameter sweeps indicated that performance did not saturate when adding more electrodes or more training data, and that accuracy improved when utilizing time-varying structure in the data. Microphonic contamination and phoneme onset differences modestly increased decoding accuracy, but could be mitigated by acoustic artifact subtraction and using a neural speech onset marker, respectively.SignificanceThe ability to decode a comprehensive set of phonemes using intracortical electrode array signals from a nontraditional speech area suggests that placing electrode arrays in ventral speech areas is a promising direction for speech BCIs.

No abstract