Cognitive performance detection using entropy-based features and lead-specific approach
| Autor: | Ritesh Kumar Saraswat, Ramesh Kumar Sunkaria, Lakhan Dev Sharma |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
medicine.diagnostic_test
Computer science business.industry Stationary wavelet transform Continuous monitoring 020206 networking & telecommunications Pattern recognition Workload 02 engineering and technology Electroencephalography Signal Sampling (signal processing) Signal Processing 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering Entropy (energy dispersal) business Reliability (statistics) |
| Zdroj: | Signal, Image and Video Processing. 15:1821-1828 |
| ISSN: | 1863-1711 1863-1703 |
| Popis: | Detecting cognitive performance during mental arithmetic allows researchers to observe and identify the brain’s response to stimuli. Existing non-invasive methods for automated cognitive performance detection need improvements in terms of accuracy. In this work, a novel approach for cognitive performance has been proposed which uses short-duration electroencephalography (EEG) signal (4.094 s). Stationary wavelet transform (SWT) has been used to decompose the signal followed by extraction of entropy-based features and classification using selected attributes. To tackle the imbalanced data issue, adaptive synthetic sampling approach has been used. The proposed technique works in two modes: multi-lead approach (MLA), where EEG signal from multiple leads was used, and a novel lead-specific approach (LSA), where EEG signal from a single lead (F4) was used. A high accuracy of 94.00% in MLA and 93.70% in LSA reflects reliability of the proposed technique. The use of short-duration single-lead EEG signal makes this technique suitable for continuous monitoring system of cognitive performance during mental workload. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink