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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.


Block diagram of WICA for automatic EEG artifact removal. Raw data to be removed are appended to the artifact samples first. The next stage is wavelet decomposition via channel by channel, in which data are projected into n-dimensional space where ICA is performed. Subsequently, n-m neural-related WICs are used for n-channel wavelet coefficient reconstruction, whereas m artifactual WICs are automatically recognized by correlation analysis. Finally, the n-channel EEG signal without artifacts is reconstructed by inverse DWT from n-channel wavelet coefficient.
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Related In: Results  -  Collection


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fig2: Block diagram of WICA for automatic EEG artifact removal. Raw data to be removed are appended to the artifact samples first. The next stage is wavelet decomposition via channel by channel, in which data are projected into n-dimensional space where ICA is performed. Subsequently, n-m neural-related WICs are used for n-channel wavelet coefficient reconstruction, whereas m artifactual WICs are automatically recognized by correlation analysis. Finally, the n-channel EEG signal without artifacts is reconstructed by inverse DWT from n-channel wavelet coefficient.

Mentions: The algorithm for EOG and EMG artifact removal in EEG (as shown in Figure 2) is presented as follows.


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)

Block diagram of WICA for automatic EEG artifact removal. Raw data to be removed are appended to the artifact samples first. The next stage is wavelet decomposition via channel by channel, in which data are projected into n-dimensional space where ICA is performed. Subsequently, n-m neural-related WICs are used for n-channel wavelet coefficient reconstruction, whereas m artifactual WICs are automatically recognized by correlation analysis. Finally, the n-channel EEG signal without artifacts is reconstructed by inverse DWT from n-channel wavelet coefficient.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Block diagram of WICA for automatic EEG artifact removal. Raw data to be removed are appended to the artifact samples first. The next stage is wavelet decomposition via channel by channel, in which data are projected into n-dimensional space where ICA is performed. Subsequently, n-m neural-related WICs are used for n-channel wavelet coefficient reconstruction, whereas m artifactual WICs are automatically recognized by correlation analysis. Finally, the n-channel EEG signal without artifacts is reconstructed by inverse DWT from n-channel wavelet coefficient.
Mentions: The algorithm for EOG and EMG artifact removal in EEG (as shown in Figure 2) is presented as follows.

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.