Print Email Facebook Twitter Estimating workload using EEG spectral power and ERPs in the n-back task Title Estimating workload using EEG spectral power and ERPs in the n-back task Author Brouwer, A.M. Hogervorst, M.A. van Erp, J.B.F. Heffelaar, T. Zimmerman, P.H. Oostenveld, R. Publication year 2012 Abstract Previous studies indicate that both EEG spectral power (in particular the alpha and theta band) and ERPs (in particular the P300) can be used as a measure of mental work or memory load. We compare their ability to estimate workload level in a well-controlled task. In addition, we combine both types of measures in a single classification model to examine whether this results in higher classification accuracy than either one alone. Participants watched a sequence of visually presented letters and indicated whether or not the current letter was the same as the one n instances before. Workload was varied by varying n. We developed different classification models using ERP features, frequency power features or a combination (fusion). Training and testing of the models simulated an online workload estimation situation. All our ERP, power and fusion models provide classification accuracies between 80 and 90% when distinguishing between the highest and the lowest workload condition after two minutes. For 32 out of 35 participants, classification was significantly higher than chance level after 2.5 s (or one letter) as estimated by the fusion model. Differences between the models are rather small, though the fusion model performs better than the other models when only short data segments are available for estimating workload.Binnen een gecontroleerd werklastexperiment schatten we in een gesimuleerde online situatie werklastniveau op basis van EEG spectral power, ERPs en een combinatie hiervan. Dit lukt voor 33 van de 35 proefpersonen met gemiddelden van 80-90% correct. Subject HumanPCS - Perceptual and Cognitive SystemsBSS - Behavioural and Societal SciencesBiomedical InnovationErgonomicsHealthy LivingWerklastEEGSpectral powerERPP300n-back taakpassieve BCI To reference this document use: http://resolver.tudelft.nl/uuid:cea5c26f-a9f3-4ed5-a57b-1680ff926a2a DOI https://doi.org/10.1088/1741-2560/9/4/045008 TNO identifier 448682 Source Journal of Neural Engineering, 9 (4) Article number 45008 Document type article Files To receive the publication files, please send an e-mail request to TNO Library.