Abstract
Artifacts removal and rhythms extraction from electroencephalography (EEG) signals are important for portable and wearable EEG recording devices. Incorporating a novel grouping rule, we proposed an adaptive singular spectrum analysis (SSA) method for artifacts removal and rhythms extraction. Based on the EEG signal amplitude, the grouping rule determines adaptively the first one or two SSA reconstructed components as artifacts and removes them. The remaining reconstructed components are then grouped based on their peak frequencies in the Fourier transform to extract the desired rhythms. The grouping rule thus enables SSA to be adaptive to EEG signals containing different levels of artifacts and rhythms. The simulated EEG data based on the Markov Process Amplitude (MPA) EEG model and the experimental EEG data in the eyes-open and eyes-closed states were used to verify the adaptive SSA method. Results showed a better performance in artifacts removal and rhythms extraction, compared with the wavelet decomposition (WDec) and another two recently reported SSA methods. Features of the extracted alpha rhythms using adaptive SSA were calculated to distinguish between the eyes-open and eyes-closed states. Results showed a higher accuracy (95.8%) than those of the WDec method (79.2%) and the infinite impulse response (IIR) filtering method (83.3%).
Highlights
Electroencephalography (EEG) is a measurable voltage resulting from electrical activity of the brain neurons (Niedermeyer & Da Silva, 2005)
When the EEG was contaminated with large artifacts, as shown in Fig. 4A, the amplitude of the EEG signal was higher than the threshold (V0 = 200 μV)
We proposed an adaptive singular spectrum analysis (SSA) method with a novel grouping rule to remove artifacts and extract alpha rhythms from EEG signals in eyes-open and eyes-closed states
Summary
Electroencephalography (EEG) is a measurable voltage resulting from electrical activity of the brain neurons (Niedermeyer & Da Silva, 2005). Spontaneous EEG consists of several rhythms of different frequencies: delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz) and gamma (>30 Hz), each containing information about different brain activity. Rhythms extraction from the EEG signals is important for portable and wearable EEG recording devices, which have attracted much attention in recent studies The EEG alpha rhythm was used as a measure of resting-state arousal and activation (Barry et al, 2007). The EEG signal is always contaminated by artifacts, including electrooculogram (EOG), electromyography (EMG), baseline drift and stochastic noise, which interfere the rhythms extraction (Azarbad et al, 2014; Daly et al, 2013; Teixeira et al, 2006)
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