High performance clean versus artifact dry electrode EEG data classification using Convolutional Neural Network transfer learning

High performance clean versus artifact dry electrode EEG data classification using Convolutional Neural Network transfer learning

Objective: Convolutional Neural Networks (CNNs) are promising for artifact detection in electroencephalography (EEG) data, but require large amounts of data. Despite increasing use of dry electrodes for EEG data acquisition, dry electrode EEG datasets are sparse. We aim to develop an algorithm for c...

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Bibliographic Details
Main Authors: M.N. van Stigt, E.A. Groenendijk, H.A. Marquering, J.M. Coutinho, W.V. Potters
Format: Article
Language:English
Published: Elsevier 2023-01-01
Series:Clinical Neurophysiology Practice
Subjects:
Electroencephalography
Dry electrodes
Artifact
Convolutional neural network
Transfer learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2467981X23000094

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http://www.sciencedirect.com/science/article/pii/S2467981X23000094

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