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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 (VHV+HV and LV+VLV) and five-category (VHV, HV, neutral, LV, and VLV) classification. For each subject, an appropriate number of features were selected for the highest accuracy. The mean accuracy was computed among all the subjects. Error bars show the standard deviation of the mean accuracies across all subjects.
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Related In: Results  -  Collection


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fig10: Classification accuracies of raw data and artifact-removed data for binary-category (VHV+HV and LV+VLV) and five-category (VHV, HV, neutral, LV, and VLV) classification. For each subject, an appropriate number of features were selected for the highest accuracy. The mean accuracy was computed among all the subjects. Error bars show the standard deviation of the mean accuracies across all subjects.

Mentions: In Validation 2, we performed a binary-category (VHV+HV and LV+VLV) classification and five-category (VHV, HV, neutral, LV, and VLV) classification to verify our method. In contrast to Validation 1, features were selected from all the 16 channels, and the maximum number of features was 2880. We also compared the highest accuracies of the classification performances between raw data and artifact-removed data among all subjects (Figure 10). 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. Thus, the artifact removal method was also effective for EEG data of higher-order cognitive processes. However, we found that the classification performance did not improve or even worsened for some subjects. This finding may be caused by the removed artifact components, which contained some information correlated with emotion, or an involuntary muscle contraction that occurred while the pictures were displayed for emotion induction.


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 (VHV+HV and LV+VLV) and five-category (VHV, HV, neutral, LV, and VLV) classification. For each subject, an appropriate number of features were selected for the highest accuracy. The mean accuracy was computed among 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

fig10: Classification accuracies of raw data and artifact-removed data for binary-category (VHV+HV and LV+VLV) and five-category (VHV, HV, neutral, LV, and VLV) classification. For each subject, an appropriate number of features were selected for the highest accuracy. The mean accuracy was computed among all the subjects. Error bars show the standard deviation of the mean accuracies across all subjects.
Mentions: In Validation 2, we performed a binary-category (VHV+HV and LV+VLV) classification and five-category (VHV, HV, neutral, LV, and VLV) classification to verify our method. In contrast to Validation 1, features were selected from all the 16 channels, and the maximum number of features was 2880. We also compared the highest accuracies of the classification performances between raw data and artifact-removed data among all subjects (Figure 10). 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. Thus, the artifact removal method was also effective for EEG data of higher-order cognitive processes. However, we found that the classification performance did not improve or even worsened for some subjects. This finding may be caused by the removed artifact components, which contained some information correlated with emotion, or an involuntary muscle contraction that occurred while the pictures were displayed for emotion induction.

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.