Automatic Detection and Correction of Blink Artifacts in Single Channel Neural EEG Signals
| Autor: | Vijayasankar Anumala, P. Rajesh Kumar, Subbaiah Pv, Hema Kumar Goru |
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| Rok vydání: | 2020 |
| Předmět: |
Discrete wavelet transform
0209 industrial biotechnology Signal processing Artifact (error) Channel (digital image) business.industry Computer science Stationary wavelet transform Wavelet transform Pattern recognition 02 engineering and technology 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Detection theory Artificial intelligence business Energy (signal processing) |
| Zdroj: | 2020 International Conference on Artificial Intelligence and Signal Processing (AISP). |
| DOI: | 10.1109/aisp48273.2020.9073123 |
| Popis: | Ocular Artifacts (OAs) are inevitable during Electroencephalogram (EEG) acquisition predominantly in human identified brain potentials, which makes the task of signal detection and correction is an essential and a critical function. This paper on one hand adopts an energy detection method to identify the artifacts and on the other hand performs wavelet thresholding in the identified zones to protect the neural information at non artifact regions. A multipronged combination of Wavelet Transform (WT) techniques, Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), and the threshold functions Universal Threshold (UT), Statistical Threshold (ST) and Hybrid Threshold (HT) are employed to identify the optimum combination for OAs removal. The performance of these methods at artifact regions is analytically assimilated with various standard metrics. Results of this study demonstrated that the SWT+HT method has yielded the desired outcome as compared to other methods of artifacts correction. |
| Databáze: | OpenAIRE |
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