ArticlePLoS computational biology2019
Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 66 citations in OpenAlex.
- Decoding Intracranial EEG With Machine Learning: A Systematic Review.Frontiers in human neuroscience · 2022Pooled it
- Distinct neural processes link speech planning and execution.Nature human behaviour · 2026Article
- Neuronal Decoding of Decisions in Multidimensional Feature Space Using a Gated Recurrent Variational Autoencoder.bioRxiv : the preprint server for biology · 2025Article
- Master classes of the tenth international brain-computer interface meeting: showcasing the research of BCI trainees.Journal of neural engineering · 2025Article
- Decoding Single and Paired Phonemes Using 7T Functional MRI.Brain topography · 2024Article
- Iterative alignment discovery of speech-associated neural activity.Journal of neural engineering · 2024Article
- Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model.PLoS computational biology · 2024Article
- Boosting brain-computer interfaces with functional electrical stimulation: potential applications in people with locked-in syndrome.Journal of neuroengineering and rehabilitation · 2023Review
- High-resolution neural recordings improve the accuracy of speech decoding.Nature communications · 2023Article
- Direct speech reconstruction from sensorimotor brain activity with optimized deep learning models.Journal of neural engineering · 2023Article
- Decoding and synthesizing tonal language speech from brain activity.Science advances · 2023Article
- Deep learning applied to EEG source-data reveals both ventral and dorsal visual stream involvement in holistic processing of social stimuli.Scientific reports · 2023Article
- Cortical control of posture in fine motor skills: evidence from inter-utterance rest position.Frontiers in human neuroscience · 2023Article
- Harnessing the Power of Artificial Intelligence in Otolaryngology and the Communication Sciences.Journal of the Association for Research in Otolaryngology : JARO · 2022Review
- Representing the dynamics of high-dimensional data with non-redundant wavelets.Patterns (New York, N.Y.) · 2022Article
- Dementia ConnEEGtome: Towards multicentric harmonization of EEG connectivity in neurodegeneration.International journal of psychophysiology : official journal of the International Organization of Psychophysiology · 2022Review
- Imagined speech can be decoded from low- and cross-frequency intracranial EEG features.Nature communications · 2022Article
- Decoding spoken English from intracortical electrode arrays in dorsal precentral gyrus.Journal of neural engineering · 2020Article
- Decoding Imagined and Spoken Phrases From Non-invasive Neural (MEG) Signals.Frontiers in neuroscience · 2020Article
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Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of neural activity from multiple single-trial measurements. This is often accomplished by learning a simple, linear transformations between neural features and features of the sensory stimuli or motor task. While successful in some early sensory processing areas, linear mappings are unlikely to be ideal tools for elucidating nonlinear, hierarchical representations of higher-order brain areas during complex tasks, such as the production of speech by humans. Here, we apply deep networks to predict produced speech syllables from a dataset of high gamma cortical surface electric potentials recorded from human sensorimotor cortex. We find that deep networks had higher decoding prediction accuracy compared to baseline models. Having established that deep networks extract more task relevant information from neural data sets relative to linear models (i.e., higher predictive accuracy), we next sought to demonstrate their utility as a data analysis tool for neuroscience. We first show that deep network's confusions revealed hierarchical latent structure in the neural data, which recapitulated the underlying articulatory nature of speech motor control. We next broadened the frequency features beyond high-gamma and identified a novel high-gamma-to-beta coupling during speech production. Finally, we used deep networks to compare task-relevant information in different neural frequency bands, and found that the high-gamma band contains the vast majority of information relevant for the speech prediction task, with little-to-no additional contribution from lower-frequency amplitudes. Together, these results demonstrate the utility of deep networks as a data analysis tool for basic and applied neuroscience.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.