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

ABSTRACT:

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 clean versus artifact dry electrode EEG data classification using transfer learning.

Methods

Dry electrode EEG data were acquired in 13 subjects while physiological and technical artifacts were induced. Data were per 2-second segment labeled as clean or artifact and split in an 80% train and 20% test set. With the train set, we fine-tuned a pre-trained CNN for clean versus artifact wet electrode EEG data classification using 3-fold cross val

SUBMITTER: van Stigt MN 

PROVIDER: S-EPMC10196906 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature