Scientists Decode Inner Speech by Mapping the Brain’s Hidden Articulator Movements

When you read a sentence silently, your tongue, lips, jaw and larynx do not move, yet something inside your brain appears to rehearse the movements anyway. A new study published in Nature Neuroscience has now captured that hidden rehearsal in extraordinary detail, revealing a neural architecture in which imagined speech and spoken speech share a common planning network while remaining fundamentally distinct at the level of execution. The findings, based on high-density electrocorticography in nine awake neurosurgical patients, offer the most precise picture to date of how the brain simulates articulation without ever producing a sound, and they demonstrate for the first time that these internal simulations can be decoded into text, audio and movement trajectories with accuracy rivaling that of overt speech.

The research team, led by investigators at Huashan Hospital of Fudan University and ShanghaiTech University, recorded from 256-channel electrode grids temporarily placed over frontoparietal cortex in patients undergoing awake brain tumor surgery. As part of their clinical language mapping, participants articulated syllables aloud and then silently imagined the same syllables, with strict controls to guarantee that no muscle activity occurred during imagery. Preoperative training, continuous video and audio surveillance, and electromyographic monitoring of orofacial muscles ensured that the imagined-speech trials were genuinely covert. The researchers then converted the recorded audio into 13-dimensional articulatory kinematic trajectories, capturing the two-dimensional movements of six articulators, including the upper and lower lips, jaw, and three tongue regions, plus laryngeal activity derived from fundamental frequency.

Using time-delayed ridge regression models, the team asked which frequency bands of neural activity tracked these articulatory trajectories. The answer revealed a striking spectral dissociation between the two speech modes. During overt articulation, encoding peaked in the high gamma band, roughly 70 to 150 hertz, the classic signature of local neuronal firing tightly coupled to motor output. During imagined speech, however, the dominant carrier was the beta1 band, spanning 12 to 24 hertz, with high gamma engagement dropping to a small fraction of its overt-speech strength. Only 7.1 percent of electrodes showed high gamma responsiveness during imagery, compared with 38.2 percent during articulation, while the beta1 band remained broadly engaged across both modalities.

This frequency shift is more than a technical curiosity. Beta-band activity is thought to arise partly from network-level dynamics associated with top-down control, which fits neatly with the idea that imagined speech is an internally generated simulation rather than a weakened echo of movement. The broader spatial distribution of beta1 responses across frontoparietal cortex, in contrast to the spatially constrained high gamma signals, suggests that imagery recruits a wider, more distributed circuit regime. The researchers propose that beta1 activity may provide a unified physiological window onto both overt and covert speech processes, something that previous brain-computer interface efforts, which have focused heavily on high gamma, have largely overlooked.

Within the beta1 band, the team identified three functionally distinct populations of electrodes. About 37.8 percent of responsive electrodes responded to both speech modes, and among these dual-responsive electrodes, unsupervised clustering revealed a subset with strongly correlated encoding across articulation and imagery. These shared-encoding electrodes maintained consistent articulator contributions across modalities, with a cosine similarity of 0.32 that was highly significant under permutation testing. The team interprets them as supramodal populations participating in a speech planning network that transcends the distinction between internal and external production. The remaining modality-specific electrodes, by contrast, showed no significant cross-modal similarity in their encoding patterns, indicating that separate neural subpopulations within the same cortical territory switch their functional engagement depending on whether speech is executed or merely simulated.

Mapping these populations onto a common brain template exposed a clear spatial logic. Modality-specific electrodes concentrated bilaterally around the central sulcus in ventral primary sensorimotor cortex, while supramodal electrodes formed three distributed clusters: one in middle premotor cortex, one in the subcentral gyrus, and one at the junction of the postcentral gyrus and supramarginal gyrus. Cross-correlation analysis of simultaneously recorded electrode pairs showed that the supramodal clusters consistently led the modality-specific sensorimotor populations in time, in both speech modes. This temporal precedence, likely communicated along the arcuate fasciculus and superior longitudinal fasciculus-III pathways, supports a top-down organization in which abstract articulatory plans are generated in frontoparietal hubs and then forwarded to execution circuits.

The somatotopic organization of the two populations differed in an unexpected way. During overt speech, the modality-specific electrodes displayed a well-defined ventral-to-dorsal gradient in sensorimotor cortex, with dual laryngeal representations at the extremes, one corresponding to the laryngeal motor area known from nonhuman primates and one apparently human-specific, and tongue, lip and jaw representations arranged sequentially between them. During imagined speech, the organization was weaker and spatially diffuse, with imagery-specific electrodes interleaved among articulation-specific ones rather than forming their own macroscopic map. This mosaic-like interdigitation echoes the recently proposed somato-cognitive action network, in which effector-specific zones alternate with integrative regions, and it may explain why functional MRI studies have struggled to detect imagery-related activity in primary sensorimotor cortex, where the dominant overt motor signal masks subtler internal activations.

The translational payoff came from a triple-stream deep learning framework that mapped beta1 activity from encoding electrodes to three parallel outputs: a syllable classifier, a speech synthesizer and an articulatory movement synthesizer. The classifier achieved a median accuracy of 80.4 percent for imagined speech, well above the 16.7 percent chance level and statistically indistinguishable from the 78.3 percent achieved for overt articulation. Synthesized speech from both modalities fell well below the 8-decibel Mel cepstral distortion threshold considered acceptable for voice recognition, and human listeners rated the imagined-speech synthesis as highly intelligible, with mean opinion scores around 3.9 on a five-point scale. Notably, decoding based only on the significant encoding electrodes outperformed decoding from all 256 channels, while downsampling to a clinically typical 32-channel grid degraded performance substantially, underscoring the value of high-density coverage.

Perhaps most remarkably, the articulatory movement synthesizer generalized to syllables it had never seen. Using a leave-one-syllable-out strategy, the model still predicted movement trajectories for excluded syllables significantly above chance in both modalities, and performance remained robust even when two or three syllables were withheld. Cross-modal transfer experiments on the shared-encoding electrodes showed that models trained on overt speech decoded imagined speech nearly as well as within-modality models, with a cross-modal generalization index of 0.934 approaching perfect transfer. This degree of generalization indicates that the shared electrodes encode a stable, supramodal articulatory code, providing a principled implantation target for future speech brain-computer interfaces that could serve patients across a spectrum of impairments, from locked-in syndrome and amyotrophic lateral sclerosis to Broca’s aphasia.

The authors caution that several limitations remain. The decoding framework was tested on a limited syllable repertoire rather than continuous speech, ground-truth audio may have been contaminated by intraoperative noise, and electrocorticography lacks the single-neuron resolution needed to separate closely adjacent but functionally distinct populations. Validation in chronically implanted patients will be essential. Nevertheless, the study delivers a conceptual advance that extends beyond engineering: imagined speech is not merely muted speech but an active cognitive process with its own spectral signature, its own somatotopic reorganization, and its own dedicated neural populations, all orchestrated by a supramodal planning network that treats speaking and thinking in words as two expressions of a single, unified motor-cognitive map.

Subject of Research: Neural encoding and decoding of articulatory kinematics during imagined and overt speech

Article Title: A neural architecture for imagined and overt speech motor dynamics

Article References: Zhao, Z., Wang, Z., Liu, Y., Qian, Y., Yin, Y., Gao, X., Yuan, B., Tong, S. X., Tian, X., Chen, G., Li, Y., Lu, J., & Wu, J. (2026). A neural architecture for imagined and overt speech motor dynamics. Nature Neuroscience. https://doi.org/10.1038/s41593-026-02456-0

Image Credits: AI Generated

DOI: 10.1038/s41593-026-02456-0

Keywords: speech imagery, electrocorticography, articulatory kinematics, beta oscillations, motor cortex, brain-computer interface, neural decoding, speech synthesis, premotor cortex, supramarginal gyrus, somatotopy, Nature Neuroscience