کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6951393 | 1451662 | 2015 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
EEG-fMRI: Dictionary learning for removal of ballistocardiogram artifact from EEG
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
In this paper, artifact removal from biomedical signals is addressed. We particularly focus on removing ballistocardiogram (BCG) artifact from EEG. BCG mainly appears in EEG signals during simultaneous EEG-fMRI recordings. Different from most existing artifact removal techniques, we propose a method based on dictionary learning framework. Due to strength of sparsifying dictionaries in applications such as image denoising, it is expected to succeed in BCG removal task as well. This is investigated in the proposed approach where a dictionary is learned from original EEG recording. The dictionary is designed to locally model BCG characteristics. After achieving the dictionary, BCG can be simply subtracted from the original signal and the clean EEG is obtained. Our experimental results on both synthetic and real data confirm the effectiveness of the proposed method. The results reveal the flexibility of learned dictionary for modeling the fluctuations in artifact, and removing it from original EEG signals.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biomedical Signal Processing and Control - Volume 18, April 2015, Pages 186-194
Journal: Biomedical Signal Processing and Control - Volume 18, April 2015, Pages 186-194
نویسندگان
Vahid Abolghasemi, Saideh Ferdowsi,