Name
Title | ||
|---|---|---|
Fusion of state space and frequency-domain features for improved microsleep detection |
Abstract | ||
|---|---|---|
A novel approach for Microsleep Event detection is presented. This is achieved based on multisensor electroencephalogram (EEG) and electrooculogram (EOG) measurements recorded during an overnight driving simulation task. First, using video clips of the driving, clear Microsleep (MSE) and Non-Microsleep (NMSE) events were identified. Next, segments of EEG and EOG of the selected events were analyzed and features were extracted using Power Spectral Density and Delay Vector Variance. The so obtained features are used in several combinations for MSE detection and classification by means of populations of Learning Vector Quantization (LVQ) networks. Best classification results, with test errors down to 13%, were obtained by a combination of all the recorded EEG and EOG channels, all features, and with feature relevance adaptation using Genetic Algorithms. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11550907_120 | ICANN (2) |
Keywords | Field | DocType |
delay vector variance,genetic algorithms,frequency-domain feature,overnight driving simulation task,learning vector quantization,clear microsleep,improved microsleep detection,eog channel,microsleep event detection,best classification result,state space,recorded eeg,mse detection,power spectral density,frequency domain,genetic algorithm | Frequency domain,Pattern recognition,Computer science,Microsleep,Learning vector quantization,Mean squared error,Vector quantization,Spectral density,Artificial intelligence,State space,Electroencephalography | Conference |
Volume | ISSN | ISBN |
3697 | 0302-9743 | 3-540-28755-8 |
Citations | PageRank | References |
7 | 1.03 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Sommer | 1 | 46 | 7.99 |
| Mo Chen | 2 | 29 | 4.50 |
| Martin Golz | 3 | 46 | 10.68 |
| Udo Trutschel | 4 | 39 | 6.50 |
| Danilo Mandic | 5 | 1641 | 173.32 |