ArticleClinical neurophysiology practice2023
High performance clean versus artifact dry electrode EEG data classification using Convolutional Neural Network transfer learning.
Article in Clinical neurophysiology practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Hybrid BCI for Meal-Assist Robot Using Dry-Type EEG and Pupillary Light Reflex.Biomimetics (Basel, Switzerland) · 2025Article
- Prehospital Detection of Large Vessel Occlusion Stroke With EEG.Neurology · 2023Article
- Editorial: Affective brain-computer interface in emotion artificial intelligence and medical engineering.Frontiers in computational neuroscience · 2023Article
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Authors and funding
5 authors.
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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 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 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 Conclusions: Despite a relatively small dry electrode EEG dataset, transfer learning enabled development of a high performing CNN-based algorithm for 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.
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