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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.


Classification accuracies of raw data and artifact-removed data for binary-category (left and right) and three-category (left, right, and neither) classification. For each subject, an appropriate number of features were selected for the highest accuracy. The mean accuracy was computed across all the subjects. Error bars show the standard deviation of the mean accuracies across all subjects.
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fig9: Classification accuracies of raw data and artifact-removed data for binary-category (left and right) and three-category (left, right, and neither) classification. For each subject, an appropriate number of features were selected for the highest accuracy. The mean accuracy was computed across all the subjects. Error bars show the standard deviation of the mean accuracies across all subjects.

Mentions: All the classification tests in this study were carried out using fivefold cross validation with RBF-kernel SVM. We calculated the offline classification accuracies with different numbers of features selected by different correlation score thresholds. For all subjects, we compared the highest accuracies of the classification between raw data and artifact-removed data. Both the results of binary-category (left and right) classification and three-category (left, right, and neither) classification were utilized to test our method. The mean highest accuracy of binary-category classification across fourteen subjects is shown in Figure 9. For both binary-category and three-category classification, the average prediction accuracy of artifact-removed data was significantly higher than that of raw data on t-statistics at a significance level of 0.05. This may be because our proposed method removed the artifact components that influenced the classification, resulting in extracted features that were highly interrelated with the motor imagery task.


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)

Classification accuracies of raw data and artifact-removed data for binary-category (left and right) and three-category (left, right, and neither) classification. For each subject, an appropriate number of features were selected for the highest accuracy. The mean accuracy was computed across all the subjects. Error bars show the standard deviation of the mean accuracies across all subjects.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig9: Classification accuracies of raw data and artifact-removed data for binary-category (left and right) and three-category (left, right, and neither) classification. For each subject, an appropriate number of features were selected for the highest accuracy. The mean accuracy was computed across all the subjects. Error bars show the standard deviation of the mean accuracies across all subjects.
Mentions: All the classification tests in this study were carried out using fivefold cross validation with RBF-kernel SVM. We calculated the offline classification accuracies with different numbers of features selected by different correlation score thresholds. For all subjects, we compared the highest accuracies of the classification between raw data and artifact-removed data. Both the results of binary-category (left and right) classification and three-category (left, right, and neither) classification were utilized to test our method. The mean highest accuracy of binary-category classification across fourteen subjects is shown in Figure 9. For both binary-category and three-category classification, the average prediction accuracy of artifact-removed data was significantly higher than that of raw data on t-statistics at a significance level of 0.05. This may be because our proposed method removed the artifact components that influenced the classification, resulting in extracted features that were highly interrelated with the motor imagery task.

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.