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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 motor imagery experiment. The EEG epoch represents the data used for analysis and classification.
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Related In: Results  -  Collection


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fig3: One trial of the motor imagery experiment. The EEG epoch represents the data used for analysis and classification.

Mentions: Fourteen healthy BCI novices performed first motor imagery with the left hand, right hand, and neither in a calibration measurement without feedback. Every 10 s, one of three different visual cues (arrows pointing left, right, or both) indicated to the subject which type of motor imagery to perform (Figure 3). Twenty trials of each motor condition were recorded in random order. The sessions were recorded using a 16-channel g.USBamp system (Table 1). The recordings were conducted at a sampling frequency of 512 Hz using an activated high-pass filter at 0.1 Hz, low-pass filter at 60 Hz, and notch filter at 50 Hz to suppress power line noise. A Priori artifact information was acquired in advance during artifact acquisition sessions and then incorporated in our automatic artifact removal approach.


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 motor imagery experiment. The EEG epoch represents the data used for analysis and classification.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: One trial of the motor imagery experiment. The EEG epoch represents the data used for analysis and classification.
Mentions: Fourteen healthy BCI novices performed first motor imagery with the left hand, right hand, and neither in a calibration measurement without feedback. Every 10 s, one of three different visual cues (arrows pointing left, right, or both) indicated to the subject which type of motor imagery to perform (Figure 3). Twenty trials of each motor condition were recorded in random order. The sessions were recorded using a 16-channel g.USBamp system (Table 1). The recordings were conducted at a sampling frequency of 512 Hz using an activated high-pass filter at 0.1 Hz, low-pass filter at 60 Hz, and notch filter at 50 Hz to suppress power line noise. A Priori artifact information was acquired in advance during artifact acquisition sessions and then incorporated in our automatic artifact removal approach.

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.