Automated preprocessing of 64 channel electroenchephalograms recorded by biosemi instruments
Preprocessing is a mandatory step in electroencephalogram (EEG) signal analysis. Overcoming challenges posed by high noise levels and substantial amplitude artifacts, such as blink-induced electrooculogram (EOG) and muscle-related electromyogram (EMG) interference, is imperative. The signal-to-noise...
Elmentve itt :
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| Dokumentumtípus: | Cikk |
| Megjelent: |
2023
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| Sorozat: | METHODSX
11 |
| Tárgyszavak: | |
| doi: | 10.1016/j.mex.2023.102378 |
| mtmt: | 34177906 |
| Online Access: | http://publicatio.bibl.u-szeged.hu/28658 |
| Tartalmi kivonat: | Preprocessing is a mandatory step in electroencephalogram (EEG) signal analysis. Overcoming challenges posed by high noise levels and substantial amplitude artifacts, such as blink-induced electrooculogram (EOG) and muscle-related electromyogram (EMG) interference, is imperative. The signal-to-noise ratio significantly influences the reliability and statistical significance of subsequent analyses. Existing referencing approaches employed in multi-card systems, like using a single electrode or averaging across multiple electrodes, fall short in this respect. In this article, we introduce an innovative referencing method tailored to multi-card instruments, enhancing signal fidelity and analysis outcomes. Our proposed signal processing loop not only mitigates blink-related artifacts but also accurately identifies muscle activity. This work contributes to advancing EEG analysis by providing a robust solution for artifact removal and enhancing data integrity. |
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| Terjedelem/Fizikai jellemzők: | 7 |
| ISSN: | 2215-0161 |