Cargando…
Post-hoc Labeling of Arbitrary M/EEG Recordings for Data-Efficient Evaluation of Neural Decoding Methods
Many cognitive, sensory and motor processes have correlates in oscillatory neural source activity, which is embedded as a subspace in the recorded brain signals. Decoding such processes from noisy magnetoencephalogram/electroencephalogram (M/EEG) signals usually requires data-driven analysis methods...
Autores principales: | Castaño-Candamil, Sebastián, Meinel, Andreas, Tangermann, Michael |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6688515/ https://www.ncbi.nlm.nih.gov/pubmed/31427941 http://dx.doi.org/10.3389/fninf.2019.00055 |
Ejemplares similares
-
Pre-Trial EEG-Based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task
por: Meinel, Andreas, et al.
Publicado: (2016) -
Identifying controllable cortical neural markers with machine learning for adaptive deep brain stimulation in Parkinson’s disease
por: Castaño-Candamil, Sebastián, et al.
Publicado: (2020) -
Deep learning with convolutional neural networks for EEG decoding and visualization
por: Schirrmeister, Robin Tibor, et al.
Publicado: (2017) -
An extended clinical EEG dataset with 15,300 automatically labelled recordings for pathology decoding
por: Kiessner, Ann-Kathrin, et al.
Publicado: (2023) -
Neural decoding of music from the EEG
por: Daly, Ian
Publicado: (2023)