Search for: All records

The ability to connect the form and meaning of a concept, known as word retrieval, is fundamental to human communication. While various input modalities could lead to identical word retrieval, the exact neural dy- namics supporting this process relevant to daily auditory discourse remain poorly understood. Here, we re- corded neurosurgical electrocorticography (ECoG) data from 48 patients and dissociated two key language networks that highly overlap in time and space, critical for word retrieval. Using unsupervised temporal clus- tering techniques, we found a semantic processing network located in the middle and inferior frontal gyri. This network was distinct from an articulatory planning network in the inferior frontal and precentral gyri, which was invariant to input modalities. Functionally, we confirmed that the semantic processing network en- codes word surprisal during sentence perception. These findings elucidate neurophysiological mechanisms underlying the processing of semantic auditory inputs ranging from passive language comprehension to conversational speech. 

Across the animal kingdom, neural responses in the auditory cortex are suppressed during vocalization, and humans are no exception. A common hypothesis is that suppression increases sensitivity to auditory feedback, enabling the detection of vocalization errors. This hypothesis has been previously confirmed in non-human primates, however a direct link between auditory suppression and sensitivity in human speech monitoring remains elusive. To address this issue, we obtained intracranial electroencephalography (iEEG) recordings from 35 neurosurgical participants during speech production. We first characterized the detailed topography of auditory suppression, which varied across superior temporal gyrus (STG). Next, we performed a delayed auditory feedback (DAF) task to determine whether the suppressed sites were also sensitive to auditory feedback alterations. Indeed, overlapping sites showed enhanced responses to feedback, indicating sensitivity. Importantly, there was a strong correlation between the degree of auditory suppression and feedback sensitivity, suggesting suppression might be a key mechanism that underlies speech monitoring. Further, we found that when participants produced speech with simultaneous auditory feedback, posterior STG was selectively activated if participants were engaged in a DAF paradigm, suggesting that increased attentional load can modulate auditory feedback sensitivity. 

When we vocalize, our brain distinguishes self-generated sounds from external ones. A corollary discharge signal supports this function in animals; however, in humans, its exact origin and temporal dynamics remain unknown. We report electrocorticographic recordings in neurosurgical patients and a connectivity analysis framework based on Granger causality that reveals major neural communications. We find a reproducible source for corollary discharge across multiple speech production paradigms localized to the ventral speech motor cortex before speech articulation. The uncovered discharge predicts the degree of auditory cortex suppression during speech, its well-documented consequence. These results reveal the human corollary discharge source and timing with far-reaching implication for speech motor-control as well as auditory hallucinations in human psychosis. 

Decoding human speech from neural signals is essential for brain–computer interface (BCI) technologies that aim to restore speech in populations with neurological deficits. However, it remains a highly challenging task, compounded by the scarce availability of neural signals with corresponding speech, data complexity and high dimensionality. Here we present a novel deep learning-based neural speech decoding framework that includes an ECoG decoder that translates electrocorticographic (ECoG) signals from the cortex into interpretable speech parameters and a novel differentiable speech synthesizer that maps speech parameters to spectrograms. We have developed a companion speech-to-speech auto-encoder consisting of a speech encoder and the same speech synthesizer to generate reference speech parameters to facilitate the ECoG decoder training. This framework generates natural-sounding speech and is highly reproducible across a cohort of 48 participants. Our experimental results show that our models can decode speech with high correlation, even when limited to only causal operations, which is necessary for adoption by real-time neural prostheses. Finally, we successfully decode speech in participants with either left or right hemisphere coverage, which could lead to speech prostheses in patients with deficits resulting from left hemisphere damage. 

Speech production is a complex human function requiring continuous feedforward commands together with reafferent feedback processing. These processes are carried out by distinct frontal and temporal cortical networks, but the degree and timing of their recruitment and dynamics remain poorly understood. We present a deep learning architecture that translates neural signals recorded directly from the cortex to an interpretable representational space that can reconstruct speech. We leverage learned decoding networks to disentangle feedforward vs. feedback processing. Unlike prevailing models, we find a mixed cortical architecture in which frontal and temporal networks each process both feedforward and feedback information in tandem. We elucidate the timing of feedforward and feedback–related processing by quantifying the derived receptive fields. Our approach provides evidence for a surprisingly mixed cortical architecture of speech circuitry together with decoding advances that have important implications for neural prosthetics. 

When we vocalize, our brain distinguishes self-generated sounds from external ones. A corollary discharge signal supports this function in animals, however, in humans its exact origin and temporal dynamics remain unknown. We report Electrocorticographic (ECoG) recordings in neurosurgical patients and a novel connectivity approach based on Granger-causality that reveals major neural communications. We find a reproducible source for corollary discharge across multiple speech production paradigms localized to ventral speech motor cortex before speech articulation. The uncovered discharge predicts the degree of auditory cortex suppression during speech, its well-documented consequence. These results reveal the human corollary discharge source and timing with far-reaching implication for speech motor-control as well as auditory hallucinations in human psychosis. 

Decoding auditory stimulus from neural activity can enable neuroprosthetics and direct communication with the brain. Some recent studies have shown successful speech decoding from intracranial recording using deep learning models. However, scarcity of training data leads to low quality speech reconstruction which prevents a complete brain-computer-interface (BCI) application. In this work, we propose a transfer learning approach with a pre-trained GAN to disentangle representation and generation layers for decoding. We first pre-train a generator to produce spectrograms from a representation space using a large corpus of natural speech data. With a small amount of paired data containing the stimulus speech and corresponding ECoG signals, we then transfer it to a bigger network with an encoder attached before, which maps the neural signal to the representation space. To further improve the network generalization ability, we introduce a Gaussian prior distribution regularizer on the latent representation during the transfer phase. With at most 150 training samples for each tested subject, we achieve a state-of-the-art decoding performance. By visualizing the attention mask embedded in the encoder, we observe brain dynamics that are consistent with findings from previous studies investigating dynamics in the superior temporal gyrus (STG), pre-central gyrus (motor) and inferior frontal gyrus (IFG). Our findings demonstrate a high reconstruction accuracy using deep learning networks together with the potential to elucidate interactions across different brain regions during a cognitive task.