An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography
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 o...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2017-06-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/3474.pdf |
| _version_ | 1827612328512716800 |
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| author | Hai Hu Shengxin Guo Ran Liu Peng Wang |
| author_facet | Hai Hu Shengxin Guo Ran Liu Peng Wang |
| author_sort | Hai Hu |
| collection | DOAJ |
| description | 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%). |
| first_indexed | 2024-03-09T08:19:49Z |
| format | Article |
| id | doaj.art-9bbbbe36e1904dbfa6d7e632abb9f724 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T08:19:49Z |
| publishDate | 2017-06-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-9bbbbe36e1904dbfa6d7e632abb9f7242023-12-02T21:50:35ZengPeerJ Inc.PeerJ2167-83592017-06-015e347410.7717/peerj.3474An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalographyHai Hu0Shengxin Guo1Ran Liu2Peng Wang3State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, ChinaDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, ChinaDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, ChinaState Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, ChinaArtifacts 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%).https://peerj.com/articles/3474.pdfAdaptive singular spectrum analysisRhythms extractionArtifacts removalEEG |
| spellingShingle | Hai Hu Shengxin Guo Ran Liu Peng Wang An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography PeerJ Adaptive singular spectrum analysis Rhythms extraction Artifacts removal EEG |
| title | An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography |
| title_full | An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography |
| title_fullStr | An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography |
| title_full_unstemmed | An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography |
| title_short | An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography |
| title_sort | adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography |
| topic | Adaptive singular spectrum analysis Rhythms extraction Artifacts removal EEG |
| url | https://peerj.com/articles/3474.pdf |
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