Zobrazeno 1 - 10
of 119
pro vyhledávání: '"Nima Mesgarani"'
Autor:
Vishal Choudhari, Cong Han, Stephan Bickel, Ashesh D. Mehta, Catherine Schevon, Guy M. McKhann, Nima Mesgarani
Publikováno v:
Advanced Science, Vol 11, Iss 41, Pp n/a-n/a (2024)
Abstract Focusing on a specific conversation amidst multiple interfering talkers is challenging, especially for those with hearing loss. Brain‐controlled assistive hearing devices aim to alleviate this problem by enhancing the attended speech based
Externí odkaz:
https://doaj.org/article/e5c1ff50e1754d53b2000b815cd2e740
Autor:
James O’Sullivan, Guy Bogaarts, Philipp Schoenenberger, Julian Tillmann, David Slater, Nima Mesgarani, Eckhart Eule, Timothy Kilchenmann, Lorraine Murtagh, Joerg Hipp, Michael Lindemann, Florian Lipsmeier, Wei-Yi Cheng, David Nobbs, Christopher Chatham
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-10 (2023)
Abstract Challenges in social communication is one of the core symptom domains in autism spectrum disorder (ASD). Novel therapies are under development to help individuals with these challenges, however the ability to show a benefit is dependent on a
Externí odkaz:
https://doaj.org/article/846d1de534494a73930d89ef67c2d91d
Publikováno v:
PLoS Biology, Vol 21, Iss 6, p e3002128 (2023)
Humans can easily tune in to one talker in a multitalker environment while still picking up bits of background speech; however, it remains unclear how we perceive speech that is masked and to what degree non-target speech is processed. Some models su
Externí odkaz:
https://doaj.org/article/1b449908da784d99b2542b8a88d3da26
Publikováno v:
NeuroImage, Vol 266, Iss , Pp 119819- (2023)
The human auditory system displays a robust capacity to adapt to sudden changes in background noise, allowing for continuous speech comprehension despite changes in background environments. However, despite comprehensive studies characterizing this a
Externí odkaz:
https://doaj.org/article/8764205298334bfca7ed287d06eea38d
Publikováno v:
Frontiers in Neuroscience, Vol 16 (2022)
Externí odkaz:
https://doaj.org/article/bf1c18e184a140f1b85e7bc0c9818375
Autor:
Bahar Khalighinejad, Prachi Patel, Jose L. Herrero, Stephan Bickel, Ashesh D. Mehta, Nima Mesgarani
Publikováno v:
NeuroImage, Vol 235, Iss , Pp 118003- (2021)
Heschl's gyrus (HG) is a brain area that includes the primary auditory cortex in humans. Due to the limitations in obtaining direct neural measurements from this region during naturalistic speech listening, the functional organization and the role of
Externí odkaz:
https://doaj.org/article/b59614924e75472ab7d58acc73cd886f
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-11 (2019)
How does the auditory system allow for accurate speech perception against changes in background noise? Here, using neural activity in the auditory cortex as people listen to speech, the authors provide evidence that background noise is selectively su
Externí odkaz:
https://doaj.org/article/2c994cb2f45f460f8b0430ee404076dd
Autor:
Giovanni M. Di Liberto, Jingping Nie, Jeremy Yeaton, Bahar Khalighinejad, Shihab A. Shamma, Nima Mesgarani
Publikováno v:
NeuroImage, Vol 227, Iss , Pp 117586- (2021)
Acquiring a new language requires individuals to simultaneously and gradually learn linguistic attributes on multiple levels. Here, we investigated how this learning process changes the neural encoding of natural speech by assessing the encoding of t
Externí odkaz:
https://doaj.org/article/097076f5e2a744e2bbb4e5a461f0f50f
Autor:
Enea Ceolini, Jens Hjortkjær, Daniel D.E. Wong, James O’Sullivan, Vinay S. Raghavan, Jose Herrero, Ashesh D. Mehta, Shih-Chii Liu, Nima Mesgarani
Publikováno v:
NeuroImage, Vol 223, Iss , Pp 117282- (2020)
Hearing-impaired people often struggle to follow the speech stream of an individual talker in noisy environments. Recent studies show that the brain tracks attended speech and that the attended talker can be decoded from neural data on a single-trial
Externí odkaz:
https://doaj.org/article/6d22285e1b364afd8312f6db12b1c33f
Autor:
Menoua Keshishian, Hassan Akbari, Bahar Khalighinejad, Jose L Herrero, Ashesh D Mehta, Nima Mesgarani
Publikováno v:
eLife, Vol 9 (2020)
Our understanding of nonlinear stimulus transformations by neural circuits is hindered by the lack of comprehensive yet interpretable computational modeling frameworks. Here, we propose a data-driven approach based on deep neural networks to directly
Externí odkaz:
https://doaj.org/article/15b3005efd2d4846b9408f69cdd93693