A Convolutional Neural Network for Artifacts Detection in EEG Data
Lecture Notes in Networks and Systems, ISSN: 2367-3389, Vol: 350, Page: 3-13
2022
- 3Citations
- 9Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Electroencephalography (EEG) is an effective tool for neurological disorders diagnosis such as seizures, chronic fatigue, sleep disorders, and behavioral abnormalities. Various artifacts types may impact EEG signals regardless the used, resulting in an erroneous diagnosis. Various data analysis tools have therefore been developed in the biomedical engineering literature to detect and/or remove these artifacts. In this sense, deep learning (DL) is one of the most promising methods. In this paper, we develop a novel method based on artifacts detection using a convolutional neural network (CNN) architecture. The available EEG data was collected using 32 channels from the Nihon Kohden Neurofax EEG-1200. The data are preprocessed and analyzed using our CNN to perform binary artifact detection. The suggested method highlights the best classification results with a maximal accuracy up to 99.20%.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85129313239&origin=inward; http://dx.doi.org/10.1007/978-981-16-7618-5_1; https://link.springer.com/10.1007/978-981-16-7618-5_1; https://dx.doi.org/10.1007/978-981-16-7618-5_1; https://link.springer.com/chapter/10.1007/978-981-16-7618-5_1
Springer Science and Business Media LLC
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