The temporal dynamics of collective neural activity was studied in Broca's area using local field potentials 2,5. However, the basic encoding of speech features in the firing patterns of neuronal ...

Two significant challenges persist in decoding speech from neural signals. First, the limited duration of training data contrasts with the extensive data required for deep learning models.

In a recent study published in Nature Communications, researchers performed high-resolution, micro-electrocorticographic (µECoG) neural recordings for speech decoding to improve speech prostheses.

This work is published in Nature Communications, in the paper, “High-Resolution Neural Recordings Improve the Accuracy of Speech Decoding.” “There are many patients who suffer from ...

but much less on the decoding of imagined speech. This is because, in the latter case, the associated neural signals are weak and variable compared to explicit speech. They are therefore difficult ...

21,22 To determine whether speech can be directly decoded to produce language from the neural activity of a person who is unable to speak, we tested real-time decoding of words and sentences from ...

Decoding the spectral and temporal ... is to characterize the development f neural encoding of the spectral and temporal fine structure of speech sounds over time. To do so, they are currently ...

Brain–computer interfaces can enable communication for people with paralysis by transforming cortical activity associated with attempted speech ... of decoding his cortical neural activity ...

speech audio and facial-avatar animation. We trained and evaluated deep-learning models using neural data collected as the participant attempted to silently speak sentences. For text, we demonstrate ...

Decoding the spectral and temporal ... is to characterize the development f neural encoding of the spectral and temporal fine structure of speech sounds over time. To do so, they are currently ...