Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG

Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG

Classification of electroencephalography (EEG) signals corresponding to imagined speech production is important for the development of a direct-speech brain–computer interface (DS-BCI). Deep learning (DL) has been utilized with great success across several domains. However, it remains an open questi...

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Bibliographic Details
Main Authors: Ciaran Cooney, Attila Korik, Raffaella Folli, Damien Coyle
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Sensors
Subjects:
electroencephalography (EEG)
brain–computer interface (BCI)
convolutional neural networks (CNN)
deep learning
machine learning
hyperparameter optimization
Online Access:https://www.mdpi.com/1424-8220/20/16/4629

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https://www.mdpi.com/1424-8220/20/16/4629

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