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An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography
Hai Hu1, Shengxin Guo2, Ran Liu2
1State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, China.
Peerj
|July 5, 2017
Summary
A new adaptive Singular Spectrum Analysis (SSA) method effectively removes artifacts and extracts brain rhythms from electroencephalography (EEG) signals. This technique improves accuracy in distinguishing brain states, crucial for wearable EEG devices.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Neuroscience
Background:
- Artifact removal and rhythm extraction are critical for portable electroencephalography (EEG) devices.
- Existing methods may struggle with varying artifact levels and rhythm complexities.
Purpose of the Study:
- To propose and validate a novel adaptive Singular Spectrum Analysis (SSA) method for EEG artifact removal and rhythm extraction.
- To enhance the adaptability of SSA to diverse EEG signal characteristics.
Main Methods:
- Developed an adaptive SSA method incorporating a novel grouping rule based on EEG signal amplitude.
- Reconstructed components were adaptively identified as artifacts and removed.
- Remaining components were grouped by Fourier transform peak frequencies to extract rhythms.
- Validated using simulated (Markov Process Amplitude model) and experimental (eyes-open/closed states) EEG data.
Main Results:
- The adaptive SSA method demonstrated superior performance in artifact removal and rhythm extraction compared to wavelet decomposition (WDec) and other SSA techniques.
- Extracted alpha rhythm features using adaptive SSA achieved 95.8% accuracy in distinguishing eyes-open from eyes-closed states.
- This accuracy significantly outperformed WDec (79.2%) and infinite impulse response (IIR) filtering (83.3%).
Conclusions:
- The proposed adaptive SSA method offers a robust and effective solution for processing EEG signals in wearable devices.
- Its adaptive nature and improved accuracy make it a valuable tool for brain-computer interfaces and neurological monitoring.
- The method shows significant potential for advancing portable EEG applications.
