Combining Statistical Analysis and Machine Learning for EEG Scalp Topograms Classification
Incorporating brain-computer interfaces (BCIs) into daily life requires reducing the reliance of decoding algorithms on the calibration or enabling calibration with the minimal burden on the user. A potential solution could be a pre-trained decoder demonstrating a reasonable accuracy on the naive op...
Guardado en:
Autores principales: | Alexander Kuc, Sergey Korchagin, Vladimir A. Maksimenko, Natalia Shusharina, Alexander E. Hramov |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/5743091b38de46a7bf8fadbfca948492 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Effect of repetition on the behavioral and neuronal responses to ambiguous Necker cube images
por: Vladimir Maksimenko, et al.
Publicado: (2021) -
Visual and kinesthetic modes affect motor imagery classification in untrained subjects
por: Parth Chholak, et al.
Publicado: (2019) -
Assortative mixing in spatially-extended networks
por: Vladimir V. Makarov, et al.
Publicado: (2018) -
Absence Seizure Control by a Brain Computer Interface
por: Vladimir A. Maksimenko, et al.
Publicado: (2017) -
Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features
por: Anis Malekzadeh, et al.
Publicado: (2021)