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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.


One trial of the emotion recognition experiment. The EEG epoch represents the data used for analysis and emotion classification.
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Related In: Results  -  Collection


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fig4: One trial of the emotion recognition experiment. The EEG epoch represents the data used for analysis and emotion classification.

Mentions: The emotion induction experiment (150 trials) is illustrated in Figure 4. Each trial started with a 4 s resting period, followed by a 2 s ready period, during which subjects were instructed to stare at the center fixation cross and try not to think of anything on purpose. Subsequently, a picture was presented to the subjects for 4 s, during which participants were instructed to try to engage themselves into the emotion represented by the picture. At the end of each trial, participants were given 4 s to evaluate the perceived emotion and categorize it as one of the five categories by pressing number keys 1 to 5. The acquisition equipment and parameter settings were similar to those used in Validation 1.


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)

One trial of the emotion recognition experiment. The EEG epoch represents the data used for analysis and emotion classification.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: One trial of the emotion recognition experiment. The EEG epoch represents the data used for analysis and emotion classification.
Mentions: The emotion induction experiment (150 trials) is illustrated in Figure 4. Each trial started with a 4 s resting period, followed by a 2 s ready period, during which subjects were instructed to stare at the center fixation cross and try not to think of anything on purpose. Subsequently, a picture was presented to the subjects for 4 s, during which participants were instructed to try to engage themselves into the emotion represented by the picture. At the end of each trial, participants were given 4 s to evaluate the perceived emotion and categorize it as one of the five categories by pressing number keys 1 to 5. The acquisition equipment and parameter settings were similar to those used in Validation 1.

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.