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Deep learning as a tool for neural data analysis: speech classification and cross-frequency coupling in human sensorimotor cortex [article]

Jesse A. Livezey, Kristofer E. Bouchard, Edward F. Chang
2018 arXiv   pre-print
Together, these results demonstrate the utility of deep networks as a data analysis tool for neuroscience.  ...  Here, we apply deep networks to predict produced speech syllables from cortical surface electric potentials recorded from human sensorimotor cortex.  ...  Acknowledgments J.A.L. was funded by LBNL-internal LDRD "Neuromorphic Kalman Filters" led by Paolo Calafiura and LBNL-internal LDRD "Deep Learning for Science" led by Prabhat.  ... 
arXiv:1803.09807v1 fatcat:75lmoqh74bhbhcyyp5avhrhoye

Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex

Jesse A. Livezey, Kristofer E. Bouchard, Edward F. Chang, Kai Miller
2019 PLoS Computational Biology  
as a data analysis tool for neuroscience.  ...  Together, these results demonstrate the utility of deep networks as a data analysis tool for basic and applied neuroscience.  ...  Acknowledgments We would like to thank Gopala Anumanchipalli for helpful discussion and feedback. Author Contributions  ... 
doi:10.1371/journal.pcbi.1007091 pmid:31525179 pmcid:PMC6762206 fatcat:mcmagb43tvahte5x5gdej7juza

Hyperactive sensorimotor cortex during voice perception in spasmodic dysphonia

Yuji Kanazawa, Yo Kishimoto, Ichiro Tateya, Toru Ishii, Tetsuji Sanuki, Shinya Hiroshiba, Toshihiko Aso, Koichi Omori, Kimihiro Nakamura
2020 Scientific Reports  
Thus, the sensorimotor cortex and thalamus play a central role in SD pathophysiology and sensorimotor signals can be a new biomarker for SD diagnosis.  ...  Since vocalization in falsetto is intact in SD, we predicted that neural activation during speech perception would differ between the two groups only for modal voice and not for falsetto voice.  ...  Acknowledgements This research was supported by Japan Agency for Medical Research and Development, AMED under Grant Number 16ek0109006h0003 and a research grant from The Shimizu Foundation for Immunology  ... 
doi:10.1038/s41598-020-73450-0 pmid:33057071 fatcat:fcefncfaynghtkuowcpwsspn3u

How does artificial intelligence contribute to iEEG research? [article]

Julia Berezutskaya, Anne-Lise Saive, Karim Jerbi, Marcel van Gerven
2022 arXiv   pre-print
We explain key machine learning concepts, specifics of processing and modeling iEEG data and details of state-of-the-art iEEG-based neurotechnology and brain-computer interfaces.  ...  IEEG data is unique as it provides extremely high-quality signals recorded directly from brain tissue.  ...  Acknowledgements This work was supported by the Netherlands Organisation for Scientific Research (NWO) and is part of the Language in Interaction consortium (NWO Gravitation Grant No. 024.001.006) and  ... 
arXiv:2207.13190v1 fatcat:kgc7gfhnpnhmpo2woh3nwk2hka

Direct Speech Reconstruction from Sensorimotor Brain Activity with Optimized Deep Learning Models [article]

Julia Berezutskaya, Zachary V. Freudenburg, Mariska J. Vansteensel, Erik J. Aarnoutse, Nick F. Ramsey, Marcel A. J. van Gerven
2022 bioRxiv   pre-print
A BCI control strategy that is gaining attention employs speech decoding from neural data.  ...  In this paper, we optimized and validated a decoding approach based on speech reconstruction directly from high-density electrocorticography recordings from sensorimotor cortex during a speech production  ...  We thank Frans Leijten, Cyrille Ferrier, Geert-Jan Huiskamp, Sandra van der Salm and Tineke Gebbink for help with collecting ECoG data; Peter Gosselaar and Peter van Rijen for implanting the electrodes  ... 
doi:10.1101/2022.08.02.502503 fatcat:oz327qpv4zeefkrs26pleveaau

Conserved structures of neural activity in sensorimotor cortex of freely moving rats allow cross-subject decoding [article]

Svenja Melbaum, David Eriksson, Thomas Brox, Ilka Diester
2021 bioRxiv   pre-print
Via alignments of low-dimensional neural manifolds, we demonstrate cross-subject and cross-session generalization in a decoding task arguing for a conserved neuronal code.One-sentence summarySimilarity  ...  of neural population structures across the sensorimotor cortex enables generalization across animals in the decoding of unconstrained behavior.  ...  Acknowledgments We thank Krishna Shenoy, Artur Schneider, Philipp Schröppel and Christian Zimmermann for comments on the manuscript.  ... 
doi:10.1101/2021.03.04.433869 fatcat:7tsmlnz3g5b2xddf2z4zzhvkua

Beyond the brain-computer interface: Decoding brain activity as a tool to understand neuronal mechanisms subtending cognition and behavior

Célia Loriette, Julian L. Amengual, Suliann Ben Hamed
2022 Frontiers in Neuroscience  
In this context, the inclusion of machine learning techniques to decode different aspects of human cognition and behavior and its use to develop brain–computer interfaces for applications in neuroprosthetics  ...  as attention, motivation and decision making.  ...  Specifically, they used several within electrode cross-frequency coupling (CFC) measures such as amplitude-amplitude coupling (AAC), phase-amplitude coupling (PAC), and phase-phase coupling (PPC) within  ... 
doi:10.3389/fnins.2022.811736 pmid:36161174 pmcid:PMC9492914 fatcat:t5zsiu4hdzb2bahjjguu24cz5q

Decoding Movement From Electrocorticographic Activity: A Review

Ksenia Volkova, Mikhail A. Lebedev, Alexander Kaplan, Alexei Ossadtchi
2019 Frontiers in Neuroinformatics  
ECoG is routinely used in clinical practice for preoperative cortical mapping in epileptic patients.  ...  Here we review the evolution of this field and its recent tendencies, and discuss the potential areas for future development.  ...  Deep learning is rapidly gaining popularity as a BCI decoding method.  ... 
doi:10.3389/fninf.2019.00074 pmid:31849632 pmcid:PMC6901702 fatcat:gadwyhntarddpl3sckoiuipgvy

Modeling & Analysis

2003 NeuroImage  
Understanding the relationship between structure and function in the cerebral cortex is both a classification and a localization problem.  ...  to be associated with CBF increases in the supplementary motor area, the left sensorimotor cortex and thalamus, and the right cerebellum.  ...  Such relations are consistent with the variation of neural activity with changing levels of force as observed in non-human primates.  ... 
doi:10.1016/s1053-8119(05)70006-9 fatcat:zff2suxcofbxvetfrwfwcxi3zm

Intelligible speech synthesis from neural decoding of spoken sentences [article]

Gopala K Anumanchipalli, Josh Chartier, Edward F Chang
2018 bioRxiv   pre-print
Here, we designed a neural decoder that explicitly leverages the continuous kinematic and sound representations encoded in cortical activity to generate fluent and intelligible speech.  ...  For example, technology that translates cortical activity into speech would be transformative for people unable to communicate as a result of neurological impairment.  ...  Deep learning as a tool 589 for neural data analysis: speech classification and cross-frequency coupling in 590 human sensorimotor cortex. arXiv preprint arXiv:1803.Berndt, D.  ... 
doi:10.1101/481267 fatcat:hed472oeyvgxjl5a6rhsy3klwu

Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis [article]

Sergey D Stavisky, Francis R Willett, Brian A Murphy, Paymon Rezaii, Donald T Avansino, William D Memberg, Jonathan P Miller, Robert F Kirsch, Leigh R Hochberg, A. Bolu Ajiboye, Krishna V Shenoy, Jaimie M Henderson
2018 bioRxiv   pre-print
Speaking is a sensorimotor behavior whose neural basis difficult to study at the resolution of single neurons due to the scarcity of human intracortical measurements and the lack of animal models.  ...  Neurons in this area, which have not previously been implicated in speech, modulated during speaking and during non-speaking movement of the tongue, lips, and jaw.  ...  Deep learning as a tool 480 for neural data analysis: speech classification and cross-frequency coupling in human sensorimotor cortex. ArXiv 1-23.  ... 
doi:10.1101/505487 fatcat:3ul5n4fc3rfu7ldjjhryvtqv5u

Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future

Jane E. Huggins, Christoph Guger, Mounia Ziat, Thorsten O. Zander, Denise Taylor, Michael Tangermann, Aureli Soria-Frisch, John Simeral, Reinhold Scherer, Rüdiger Rupp, Giulio Ruffini, Douglas K. R. Robinson (+18 others)
2017 Brain-Computer Interfaces  
aberdeen, mD usa; s machine Learning Group, technical university of Berlin, Berlin, Germany; t Defitech Chair in Brain-machine interface (CnBi), Center for neuroprosthetics, École polytechnique fédérale  ...  BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the  ...  Acknowledgements Overall acknowledgements The authors thank the National Institute on Deafness and other Communication Disorders (NIDCD), Eunice Kennedy Shriver National Institute of Child Health & Human  ... 
doi:10.1080/2326263x.2016.1275488 pmid:29152523 pmcid:PMC5693371 fatcat:ozzkm4mqofa3fcvwuyk5k3vy3i

Cross-Participant EEG-Based Assessment of Cognitive Workload Using Multi-Path Convolutional Recurrent Neural Networks

Ryan Hefron, Brett Borghetti, Christine Schubert Kabban, James Christensen, Justin Estepp
2018 Sensors  
Applying deep learning methods to electroencephalograph (EEG) data for cognitive state assessment has yielded improvements over previous modeling methods.  ...  Using experimental data from the Multi-Attribute Task Battery (MATB) environment, a variety of deep neural network models are evaluated in the trade-space of computational efficiency, model accuracy, variance  ...  Acknowledgments: We thank the Air Force Research Laboratory 711th Human Performance Wing (711HPW/RHCPA) for providing the data from their MATB experiment and other support for this research.  ... 
doi:10.3390/s18051339 pmid:29701668 pmcid:PMC5982227 fatcat:et3vgsstujgyzlfv2yxs4ndksq

Proceedings of the Fifth International Workshop on Advances in Electrocorticography

Anthony Ritaccio, Peter Brunner, Aysegul Gunduz, Dora Hermes, Lawrence J. Hirsch, Joshua Jacobs, Kyousuke Kamada, Sabine Kastner, Robert T. Knight, Ronald P. Lesser, Kai Miller, Terrence Sejnowski (+2 others)
2014 Epilepsy & Behavior  
Conflict of interest We confirm that there are no known conflicts of interest associated with this publication and that there has been no significant financial support for this work that could have influenced  ...  These findings spanning attention, memory, motor learning, and cognitive control highlight the fundamental role of cross-frequency coupling in human cognition.  ...  The role of cross-frequency coupling extends to the motor domain where the degree of coupling in the premotor and motor cortices precisely tracks motor learning across a range of tasks [54] .  ... 
doi:10.1016/j.yebeh.2014.09.015 pmid:25461213 pmcid:PMC4268064 fatcat:idz5n6kam5g57hpsmpmfcuylt4

Proceedings of the First International Workshop on Advances in Electrocorticography

Anthony Ritaccio, Peter Brunner, Mackenzie C. Cervenka, Nathan Crone, Christoph Guger, Eric Leuthardt, Robert Oostenveld, William Stacey, Gerwin Schalk
2010 Epilepsy & Behavior  
Conflict of interest We confirm that there are no known conflicts of interest associated with this publication and that there has been no significant financial support for this work that could have influenced  ...  These findings spanning attention, memory, motor learning, and cognitive control highlight the fundamental role of cross-frequency coupling in human cognition.  ...  The role of cross-frequency coupling extends to the motor domain where the degree of coupling in the premotor and motor cortices precisely tracks motor learning across a range of tasks [54] .  ... 
doi:10.1016/j.yebeh.2010.08.028 pmid:20889384 fatcat:lvywkhn63jhrzchfc73r76mlru
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