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Novel eye-blink artefact detection algorithm from raw EEG signals using FCN-based semantic segmentation method
IET Signal Processing ( IF 1.7 ) Pub Date : 2020-10-02 , DOI: 10.1049/iet-spr.2019.0602
Mustafa Tosun 1 , Ömer Kasım 1
Affiliation  

Electroencephalography (EEG) signal artefacts cause some problems in processing and analysis of EEG signals. There are several options in the literature to identify artefacts in signal areas. However, high accuracy level in artefact detection was not achieved with these methods. New methods are needed to increase the level of accuracy. Semantic segmentation algorithms, which are widely used in image processing, were used for the first time in this study to detect eye-blink artefacts in EEG signals. In the proposed method, EEG recordings obtained from four channels were divided into 10-s segments. These segments were converted into images in 256 × 256 resolution and were labelled as eye-blink, not-blink and background. The algorithm was trained with these labelled images. The trained algorithm was tested with images containing 1630 eye-blink and not-blink signal segments obtained from single-channel and multi-channel EEG signals. Classification accuracy of the algorithm was 94.4%. The proposed method successfully detected eye-blink artefacts in simultaneous multi-channel signal images.

中文翻译:

基于FCN的语义分割方法从原始EEG信号中检测眨眼伪像的新算法

脑电图(EEG)信号伪像在处理和分析EEG信号时引起一些问题。文献中有几种选择可以识别信号区域中的伪像。但是,这些方法无法实现伪影检测的高精度水平。需要新的方法来提高准确性水平。语义分割算法被广泛用于图像处理中,在本研究中首次用于检测EEG信号中的眨眼伪像。在所提出的方法中,将从四个通道获得的脑电图记录分为10秒段。这些片段被转换为256×256分辨率的图像,并标记为眨眼,不眨眼和背景。使用这些标记的图像对算法进行了训练。使用从单通道和多通道EEG信号获得的包含1630眼眨眼和非眨眼信号段的图像测试经过训练的算法。该算法的分类准确率为94.4%。所提出的方法成功地同时检测了多通道信号图像中的眨眼伪像。
更新日期:2020-10-06
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