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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 artifact acquisition session. One trial consists of one 1 s blank period, one 1 s ready period, one 2 s action period, and one 2 s rest period. When a visual cue is presented in the action period, the subjects are required to do the corresponding action only. The EEG epoch represents the data used for the following automatic artifact removal.
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Related In: Results  -  Collection


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fig1: One trial of the artifact acquisition session. One trial consists of one 1 s blank period, one 1 s ready period, one 2 s action period, and one 2 s rest period. When a visual cue is presented in the action period, the subjects are required to do the corresponding action only. The EEG epoch represents the data used for the following automatic artifact removal.

Mentions: We first describe how to obtain a priori artifact information online, which is necessary for the following automatic artifactual component identification. During the actual EEG acquisition, artifacts are often generated by the movements of subjects, intentionally or unintentionally, such as eye blinking, eye rolling, teeth clenching, and swallowing. If the subject does only one action for a time period, the corresponding recoding data can be clearly marked by a corresponding artifact label, which can be utilized for the following automatic artifact classification. Thus, an artifact acquisition session was performed to extract a priori artifact information before the formal EEG data acquisition. Figure 1 shows a trial of the experimental design. At the beginning of one trial was a 1 s blank period, followed by a 1 s ready period, in which subjects were instructed to stare at the center fixation cross and try not to think of anything on purpose. A visual cue appeared for 2 s to indicate the corresponding action to the subjects. Given that typical actions can arouse artifacts, eye blinking, eye rolling, and teeth clenching were chosen as stimuli. No movement was set as the control stimulus. When the visual cue was presented in the screen, the subjects were required to do the corresponding action only. A 2 s rest period ended the trial. The artifact acquisition session contained 40 trials, with 10 trials for each type of stimulus. The total time was 240 s.


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 artifact acquisition session. One trial consists of one 1 s blank period, one 1 s ready period, one 2 s action period, and one 2 s rest period. When a visual cue is presented in the action period, the subjects are required to do the corresponding action only. The EEG epoch represents the data used for the following automatic artifact removal.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: One trial of the artifact acquisition session. One trial consists of one 1 s blank period, one 1 s ready period, one 2 s action period, and one 2 s rest period. When a visual cue is presented in the action period, the subjects are required to do the corresponding action only. The EEG epoch represents the data used for the following automatic artifact removal.
Mentions: We first describe how to obtain a priori artifact information online, which is necessary for the following automatic artifactual component identification. During the actual EEG acquisition, artifacts are often generated by the movements of subjects, intentionally or unintentionally, such as eye blinking, eye rolling, teeth clenching, and swallowing. If the subject does only one action for a time period, the corresponding recoding data can be clearly marked by a corresponding artifact label, which can be utilized for the following automatic artifact classification. Thus, an artifact acquisition session was performed to extract a priori artifact information before the formal EEG data acquisition. Figure 1 shows a trial of the experimental design. At the beginning of one trial was a 1 s blank period, followed by a 1 s ready period, in which subjects were instructed to stare at the center fixation cross and try not to think of anything on purpose. A visual cue appeared for 2 s to indicate the corresponding action to the subjects. Given that typical actions can arouse artifacts, eye blinking, eye rolling, and teeth clenching were chosen as stimuli. No movement was set as the control stimulus. When the visual cue was presented in the screen, the subjects were required to do the corresponding action only. A 2 s rest period ended the trial. The artifact acquisition session contained 40 trials, with 10 trials for each type of stimulus. The total time was 240 s.

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.