Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
Ergebnis 20 von 31

Details

Autor(en) / Beteiligte
Titel
An EEG-based classification system of Passenger's motion sickness level by using feature extraction/selection technologies
Ist Teil von
  • The 2010 International Joint Conference on Neural Networks (IJCNN), 2010, p.1-6
Ort / Verlag
IEEE
Erscheinungsjahr
2010
Quelle
IEEE Electronic Library (IEL)
Beschreibungen/Notizen
  • Past studies reported that the main electrogastrography (EEG) dynamic changes related to motion sickness (MS) were occurred in occipital, parietal, and somatosensory brain area, especially in the power increasing of the alpha band (8-13 Hz) and theta band (4-7 Hz) which had positive correlation with the subjective MS level. Depend on these main findings correlated with MS, we attempt to develop an EEG based classification system to automatically classify subject's MS level and find the suitable EEG features via common feature extraction, selection and classifiers technologies in this study. If we can find the regulations and then develop an algorithm to predict MS occurring, it would be a great benefit to construct a safe and comfortable environment for all drivers and passengers when they are cruising in the car, bus, ship or airplane. EEG is one of the best methods for monitoring the brain dynamics induced by motion-sickness because of its high temporal resolution and portability. After collecting the EEG signals and subjective MS level in a realistic driving environment, we first do the data pre-processing part including ICA, component clustering analysis and time-frequency analysis. Then we adopt three common feature extractions and two feature selections (FE/FS) technologies to extract or select the correlated features such as principal component analysis (PCA), linear discriminate analysis (LDA), nonparametric weighted feature extraction (NWFE), forward feature selections (FFS) and backward feature selections (BFS) and feed the feature maps into three classifiers (Gaussian Maximum Likelihood Classifier (ML), k-Nearest-Neighbor Classifier (kNN) and Support Vector Machine (SVM)). Experimental results show that classification performance of all our proposed technologies can be reached almost over 95%. It means it is possible to apply the effective technology combination to predict the subject's MS level in the real life applications. The better combination in this study is using LDA and Gaussian based ML classifier. This advantage can be widely used in machine learning area for developing the prediction algorithms in the future.
Sprache
Englisch
Identifikatoren
ISBN: 9781424469161, 1424469163
ISSN: 2161-4393
eISSN: 2161-4407
DOI: 10.1109/IJCNN.2010.5596739
Titel-ID: cdi_ieee_primary_5596739

Weiterführende Literatur

Empfehlungen zum selben Thema automatisch vorgeschlagen von bX