Cargando…
Machine learning for MEG during speech tasks
We consider whether a deep neural network trained with raw MEG data can be used to predict the age of children performing a verb-generation task, a monosyllable speech-elicitation task, and a multi-syllabic speech-elicitation task. Furthermore, we argue that the network makes predictions on the grou...
Autores principales: | Kostas, Demetres, Pang, Elizabeth W., Rudzicz, Frank |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6367450/ https://www.ncbi.nlm.nih.gov/pubmed/30733596 http://dx.doi.org/10.1038/s41598-019-38612-9 |
Ejemplares similares
-
BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data
por: Kostas, Demetres, et al.
Publicado: (2021) -
MEG Sensor Selection for Neural Speech Decoding
por: DASH, DEBADATTA, et al.
Publicado: (2020) -
Speech Kinematics and Coordination Measured With an MEG-Compatible Speech Tracking System
por: Anastasopoulou, Ioanna, et al.
Publicado: (2022) -
Correcting MEG Artifacts Caused by Overt Speech
por: Abbasi, Omid, et al.
Publicado: (2021) -
Gaze-Direction-Based MEG Averaging During Audiovisual Speech Perception
por: Hirvenkari, Lotta, et al.
Publicado: (2010)