Low Complexity Automatic Stationary Wavelet Transform for Elimination of Eye Blinks from EEG

Figure 1. Examples of signals and histograms for: clean EEG (a), blink artifact (b) and contaminated EEG (c). S is the skewness value.

Figure 2. The block diagram of the proposed method.

Figure 3. Examples of simulated data from CHB-MIT database: x(n)—pure EEG, r(n)—eye blink artifact, z1(n)—contaminated EEG with a=0.75, z2(n)—a=1.0, z3(n)—a=1.5, and z4(n)—a=2.0.

Figure 4. Mean ± std of NRMSEs per different T values.

Figure 5. Examples of approximation coefficients and corresponding skewness values for a contaminated EEG signal from CHB-MIT database in SWT domain.

Figure 6. Box plots of NRMSE and correlation coefficient between pure and filtered EEG for simulated data by all methods: (a,b) are for CHB-MIT, (c,d) are for EEG-MAT databases.

Figure 7. Examples of eye blink cancellation in simulated EEG signals from: EEG-MAT (a), and CHB-MIT (b) databases: z(n)—contaminated EEG, r(n)—real eye blink artifact, x(n)—pure EEG, x1(n)—filtered EEG by the proposed method, x2(n)—filtered EEG by the AWICA and x3(n)—filtered EEG by the EAWICA.

Figure 8. Examples of the PSDs for the pure and the filtered EEG signals by all methods for simulated data using CHB-MIT (a) and EEG-MAT (b) databases.

Figure 9. PSNR curves as the function of NRMSE for filtered EEG signals: CHB-MIT (a) and EEG-MAT (b) databases. ASWT outperformed the other algorithms because in each subplot, the points associated with the largest PSNR and the smallest NRMSE were achieved by ASWT.

Figure 10. Examples of eye blink cancellation in real EEG signals from: BCI Competition 2008—Graz Data Sets 2a (a), and BCI 2011 left/right motor imagery (b). z(n)—EEG contaminated with eye blink, x1(n)—filtered EEG by the proposed method, x2(n)—filtered EEG by AWICA and x3(n)—filtered EEG by EAWICA.