伪迹移除 | Artifact suppression using data adaptive time domain filtering

Artifact suppression from EEG signals using data adaptive time domain filtering

Abstract

This paper presents a data adaptive filtering approach to separate the EOG artifact from the recorded EEG signal.

Empirical mode decomposition(EMD) technique is used to implement the time domain filter.

Fractional Gaussian noise(fGn) is used here as the reference signal to detect the distinguish feature of EOG signal to be used to separate from EEG.

EMD is applied to the raw EEG and fGn separately to produce a finite number band limited signals named intrinsic mode functions(IMFs).

The energies of individual IMFs of fGn and that of raw EEG are compared to derive the energy based threshold for the suppression of EOG effects.

The separation results using EMD based approach is also compared with wavelet thresholding technique.

The experimental results show that the data adaptive filtering technique performs better than the wavelet based approach.

proposed method's advantages

• efficiently separates the EOG artifact without changing the amplitude and other necessary properties of the EEG signals
• full data adaptive nature

Note:

• Frequency analysis or filtering —–> deal with single channel signal
• Frequency regression analysis —–> suppress the eye-movement artifact
• Time-invariant band-pass filtering or Fourier transform(FT) —–> extract the target frequency component(specific frequency range)
• classical time-frequency analyzers(FFT based filtering or short-time Fourier transform(STFT)) —–> non-stationary signal
  Note:will bring the spectral distrotion
• PCA:extracts and sort out the principal components according to the influence on the overall data space.
  Note:It requires some priori knowledge to identify the PC as the artifact
• ICA:
  key word:decompose
  problem of using ICA:extracted components do not confirm the original scale and sequences.
• main limitation of the filtering method:introduce some spectral distortion
• EMD:
  key benefit of using EMD: automatic decomposition and fully data adaptive
  UEMD:
  BEMD:suppress EOG artifacts
  MEMD:multi-variate data analysis