کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6267631 1614601 2016 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An improved artifacts removal method for high dimensional EEG
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب (عمومی)
پیش نمایش صفحه اول مقاله
An improved artifacts removal method for high dimensional EEG
چکیده انگلیسی


- An automated EEG artifact removal method is developed.
- A novel independent component analysis strategy is proposed and applied.
- Dimension reduction is achieved with permutation and resampling.
- Improved eye blinks removal performance is demonstrated.

BackgroundMultiple noncephalic electrical sources superpose with brain signals in the recorded EEG. Blind source separation (BSS) methods such as independent component analysis (ICA) have been shown to separate noncephalic artifacts as unique components. However, robust and objective identification of artifact components remains a challenge in practice. In addition, with high dimensional data, ICA requires a large number of observations for stable solutions. Moreover, using signals from long recordings to provide the large observation set might violate the stationarity assumption of ICA due to signal changes over time.New methodInstead of decomposing all channels simultaneously, subsets of channels are randomly selected and decomposed with ICA. With reduced dimensionality of the subsets, much less amount of data is required to derive stable components. To characterize each independent component, an artifact relevance index (ARI) is calculated by template matching each component with a model of the artifact. Automatic artifact identification is then implemented based on the statistical distribution of ARI of the numerous components generated.ResultsThe proposed permutation resampling for identification matching (PRIM) method effectively removed eye blink artifacts from both simulated and real EEG.Comparison with existing methodThe average topomap correlation coefficient between the cleaned EEG and the ground truth is 0.89 ± 0.01 for PRIM, compared with 0.64 ± 0.05 for conventional ICA based method. The average relative root-mean-square error is 0.40 ± 0.01 for PRIM, compared with 0.66 ± 0.10 for conventional method.ConclusionsThe proposed method overcame limitations of conventional ICA based method and succeeded in removing eye blink artifacts automatically.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Neuroscience Methods - Volume 268, 1 August 2016, Pages 31-42
نویسندگان
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