ArticleCognitive neurodynamics2011
Selecting EEG components using time series analysis in brain death diagnosis.
Article in Cognitive neurodynamics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 9 citations in OpenAlex.
- Reliability of EEG in brain death diagnosis: an analysis of clinical and technical factors.Frontiers in medicine · 2026Article
- EEG-based analysis of human driving performance in turning left and right using Hopfield neural network.SpringerPlus · 2013Article
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Authors and funding
2 authors at 2 institutions in 2 countries.
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Abstract
In diagnosis of brain death for human organ transplant, EEG (electroencephalogram) must be flat to conclude the patient's brain death but it has been reported that the flat EEG test is sometimes difficult due to artifacts such as the contamination from the power supply and ECG (electrocardiogram, the signal from the heartbeat). ICA (independent component analysis) is an effective signal processing method that can separate such artifacts from the EEG signals. Applying ICA to EEG channels, we obtain several separated components among which some correspond to the brain activities while others contain artifacts. This paper aims at automatic selection of the separated components based on time series analysis. In the flat EEG test in brain death diagnosis, such automatic component selection is helpful.
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