Author
Seung Wan Kang
Bio: Seung Wan Kang is an academic researcher. The author has contributed to research in topics: Artifact (error) & Artificial neural network. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.
Papers
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TL;DR: The results demonstrate that the deep learning is capable of strong potential for removing EEG artifacts resulting high quality of EEG signal analysis without manual inspection by EEG experts and shows that the proposed method can effectively, automatically remove muscle and ocular artifacts in EEG signals.
7 citations
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TL;DR: This study concludes that brain MRIs can be used to distinguish the patients with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI) and Cognitive Normal (CN) from each other; while most of the studies were only able to distinguish AD from CN.
45 citations
19 Jul 2020
TL;DR: This work proposes a pattern recognition neural network based single-channel automatic artifact detection tool capable of detecting the artifacts with an 93.2% of overall accuracy and requires an average computing time of 2.57 seconds to analyse LFPs of one minute duration, making it a strong candidate for online deployment without the need for employing high performance computing equipment.
Abstract: The neural recordings known as Local Field Potentials (LFPs) provide important information on how neural circuits operate and relate. Due to the involvement of complex electronic apparatuses in the recording setups, these signals are often significantly contaminated by artifacts generated by a number of internal and external sources. To make the best use of these signals, it is imperative to detect and remove the artifacts from these signals. Hence, this work proposes a pattern recognition neural network based single-channel automatic artifact detection tool. The tool is capable of detecting the artifacts with an 93.2% of overall accuracy and requires an average computing time of 2.57 seconds to analyse LFPs of one minute duration, making it a strong candidate for online deployment without the need for employing high performance computing equipment.
31 citations
Posted Content•
TL;DR: The first results on applying various machine learning algorithms to the recently released world's largest open-source artifact recognition dataset are shared to serve as a benchmark for researchers who might work with this dataset in future.
Abstract: Electroencephalograms (EEG) are often contaminated by artifacts which make interpreting them more challenging for clinicians. Hence, automated artifact recognition systems have the potential to aid the clinical workflow. In this abstract, we share the first results on applying various machine learning algorithms to the recently released world's largest open-source artifact recognition dataset. We envision that these results will serve as a benchmark for researchers who might work with this dataset in future.
7 citations
TL;DR: A plausible method for detecting and distinguishing the directions from EEG signals is presented and it is revealed that DBN surprisingly leads to the highest level of accuracy in comparison to the other proposed methods.
3 citations
TL;DR: This manuscript provided the most comprehensive overview of research where were used neural networks for EEG signal processing.
Abstract: In the last decade, unprecedented progress in the development of neural networks influenced dozens of different industries, among which are signal processing for the electroencephalography process (EEG). Electroencephalography, even though it appeared in the first half of the 20th century, to this day didn’t change the physical principles of operation. But the signal processing technique due to the use of neural networks progressed significantly in this area. Evidence for this can serve that for the past 5 years more than 1000 publications on the topic of using machine learning have been published in popular libraries. Many different models of neural networks complicate the process of understanding the real situation in this area. In this manuscript, we provided the most comprehensive overview of research where were used neural networks for EEG signal processing.
1 citations