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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.


Related in: MedlinePlus

Correlation scores (/r/) plots between five PSD features of each component and artifact labels. Plots (a), (b), and (c) show the correlation scores with eye blinking, eye rolling, and teeth clenching, respectively. Regions of interest are marked with yellow boxes.
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fig6: Correlation scores (/r/) plots between five PSD features of each component and artifact labels. Plots (a), (b), and (c) show the correlation scores with eye blinking, eye rolling, and teeth clenching, respectively. Regions of interest are marked with yellow boxes.

Mentions: We plotted the corresponding time-domain components of the WICs to visually inspect and identify the artifacts and compared the results of automatic identification by correlation analysis. Figure 5 shows the corresponding time-domain components of the WICs. Components 3 and 5 represent EOG artifacts, whereas components 1, 2, 4, and 9 are intuitively considered artifacts containing strong EMG. Figure 6 shows the correlation scores between five PSD features of each component and artifact labels. By ranking all the correlation coefficients, we found that the EOG artifact components exhibited the highest correlation scores (1–10 Hz) with eye blinking label and eye rolling label. Similarly, a number of strong EMG artifact components were recognized by ranking correlation scores with teeth clenching label. We selected the four highest mean scores as the EMG artifacts to be removed. By comparison, we found the artifact components that the highest correlation scores represented were the same as those we visually inspected. Therefore, all artifact components could be automatically recognized by correlation analysis.


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)

Correlation scores (/r/) plots between five PSD features of each component and artifact labels. Plots (a), (b), and (c) show the correlation scores with eye blinking, eye rolling, and teeth clenching, respectively. Regions of interest are marked with yellow boxes.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig6: Correlation scores (/r/) plots between five PSD features of each component and artifact labels. Plots (a), (b), and (c) show the correlation scores with eye blinking, eye rolling, and teeth clenching, respectively. Regions of interest are marked with yellow boxes.
Mentions: We plotted the corresponding time-domain components of the WICs to visually inspect and identify the artifacts and compared the results of automatic identification by correlation analysis. Figure 5 shows the corresponding time-domain components of the WICs. Components 3 and 5 represent EOG artifacts, whereas components 1, 2, 4, and 9 are intuitively considered artifacts containing strong EMG. Figure 6 shows the correlation scores between five PSD features of each component and artifact labels. By ranking all the correlation coefficients, we found that the EOG artifact components exhibited the highest correlation scores (1–10 Hz) with eye blinking label and eye rolling label. Similarly, a number of strong EMG artifact components were recognized by ranking correlation scores with teeth clenching label. We selected the four highest mean scores as the EMG artifacts to be removed. By comparison, we found the artifact components that the highest correlation scores represented were the same as those we visually inspected. Therefore, all artifact components could be automatically recognized by correlation analysis.

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.


Related in: MedlinePlus