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

Autor:	van Stigt MN; Amsterdam UMC location University of Amsterdam, Department of Clinical Neurophysiology, Meibergdreef 9, Amsterdam, the Netherlands.; Amsterdam UMC location University of Amsterdam, Department of Neurology, Meibergdreef 9, Amsterdam, the Netherlands., Groenendijk EA; Amsterdam UMC location University of Amsterdam, Department of Clinical Neurophysiology, Meibergdreef 9, Amsterdam, the Netherlands.; Amsterdam UMC location University of Amsterdam, Department of Neurology, Meibergdreef 9, Amsterdam, the Netherlands., Marquering HA; Amsterdam UMC location University of Amsterdam, Department of Biomedical Engineering and Physics, Meibergdreef 9, Amsterdam, the Netherlands.; Amsterdam UMC location University of Amsterdam, Department of Radiology and Nuclear Medicine, Meibergdreef 9, Amsterdam, the Netherlands., Coutinho JM; Amsterdam UMC location University of Amsterdam, Department of Neurology, Meibergdreef 9, Amsterdam, the Netherlands., Potters WV; Amsterdam UMC location University of Amsterdam, Department of Clinical Neurophysiology, Meibergdreef 9, Amsterdam, the Netherlands.; Amsterdam UMC location University of Amsterdam, Department of Neurology, Meibergdreef 9, Amsterdam, the Netherlands.
Jazyk:	angličtina
Zdroj:	Clinical neurophysiology practice [Clin Neurophysiol Pract] 2023 Apr 25; Vol. 8, pp. 88-91. Date of Electronic Publication: 2023 Apr 25 (Print Publication: 2023).
DOI:	10.1016/j.cnp.2023.04.002
Abstrakt:	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 validation. The three fine-tuned CNNs were combined in one final clean versus artifact classification algorithm, in which the majority vote was used for classification. We calculated accuracy, F1-score, precision, and recall of the pre-trained CNN and fine-tuned algorithm when applied to unseen test data. Results: The algorithm was trained on 0.40 million and tested on 0.17 million overlapping EEG segments. The pre-trained CNN had a test accuracy of 65.6%. The fine-tuned clean versus artifact classification algorithm had an improved test accuracy of 90.7%, F1-score of 90.2%, precision of 89.1% and recall of 91.2%. Conclusions: Despite a relatively small dry electrode EEG dataset, transfer learning enabled development of a high performing CNN-based algorithm for clean versus artifact classification. Significance: Development of CNNs for classification of dry electrode EEG data is challenging as dry electrode EEG datasets are sparse. Here, we show that transfer learning can be used to overcome this problem. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. HM is co-founder and minor shareholder of Nicolab and TrianecT. JC received related research support from the Dutch Heart Foundation and Medtronic and unrelated research support from Bayer and Boehringer (all fees were paid to his employer), and is co-founder and minor shareholder of TrianecT. WP is co-founder and minor shareholder of TrianecT. The other authors have no conflicts to report. (© 2023 The Authors.)
Databáze:	MEDLINE
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