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Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information.

Zhang C, Tong L, Zeng Y, Jiang J, Bu H, Yan B, Li J - Biomed Res Int (2015)

Bottom Line: The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components.The artifact components were then automatically identified using a priori artifact information, which was acquired in advance.Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals.

View Article: PubMed Central - PubMed

Affiliation: China National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450002, China.

ABSTRACT
Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition.

No MeSH data available.


Raw EEG signals with strong EOG and EMG artifacts.
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Related In: Results  -  Collection


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fig7: Raw EEG signals with strong EOG and EMG artifacts.

Mentions: Figure 7 shows a short 16-channel subset of the raw EEG recordings. Strong artifacts caused by eye motion and muscle activity are visible across all channels. The strong artifacts obscure the neural information and are likely to render the corresponding trials useless for the following neural information extraction.


Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information.

Zhang C, Tong L, Zeng Y, Jiang J, Bu H, Yan B, Li J - Biomed Res Int (2015)

Raw EEG signals with strong EOG and EMG artifacts.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4562337&req=5

fig7: Raw EEG signals with strong EOG and EMG artifacts.
Mentions: Figure 7 shows a short 16-channel subset of the raw EEG recordings. Strong artifacts caused by eye motion and muscle activity are visible across all channels. The strong artifacts obscure the neural information and are likely to render the corresponding trials useless for the following neural information extraction.

Bottom Line: The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components.The artifact components were then automatically identified using a priori artifact information, which was acquired in advance.Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals.

View Article: PubMed Central - PubMed

Affiliation: China National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450002, China.

ABSTRACT
Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition.

No MeSH data available.