Abstract
The classical interaction between human and a computer or a machine relies solely on explicit behaviour (input with keyboard, mouse, gestures etc.). In many situations and tasks, the access to implicit information about the user could enhance human-computer interaction (HCI). Recent research has shown a number of examples of how such hidden user states could be extracted from signals of peripheral physiology and of the brain. While these approaches are still premature and not readily available for real application, further exploration seems worthwhile. Here, we present an approach towards monitoring the level of cognitive processing. A special experimental paradigm has been designed to detect event-related potentials (ERPs) of brain activity related to cognitive processes using tasks in different cognitive domains. Neural correlates indicating different levels of cognitive processing have been singled out and the classifiability was quantified using multivariate decoding methods. The results indicate the feasibility of monitoring the depth of cognitive processing for neurotechnological applications in BCI and industrial scenarios.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Venthur, B., Blankertz, B., Gugler, M.F., Curio, G.: Novel applications of BCI technology: psychophysiological optimization of working conditions in industry. In: Proceedings of the 2010 IEEE Conference on Systems, Man and Cybernetics (2010)
Nicolae, I.E., Acqualagna, L., Blankertz, B.: Neural indicators of the depth of cognitive processing for user-adaptive neurotechnological applications. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milan, Italy, August 25–29 (2015)
Craik, F.I.M., Tulving, E.: Depth of processing and the retention of words in episodic memory. Journal of Experimental Psychology: General 104, 268–294 (1975)
Polich, J., Kokb, A.: Cognitive and biological determinants of P300: an integrative review. Biological Psychology 41, 103–146 (1995)
Chen, Y.N., Mitra, S., Schlaghecken, F.: Sub-processes of working memory in the N-back task: an investigation using ERPs. Clin Neurophysiol 119, 1546–1559 (2008)
Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods 134, 9–21 (2004)
Winkler, I., Haufe, S., Tangermann, M.: Automatic classification of artifactual ICA-components for artifact removal in EEG signals. Behavioral and Brain Functions, 7(30) (2011)
Blankertz, B., Lemm, S., Treder, M., Haufe, S., Muller, K.R.: Single-trial analysis and classification of ERP components a tutorial. NeuroImage 56(2), 814–825 (2011)
Polich, J.: Task difficulty, probability, and inter-stimulus interval as determinants of P300 from auditory stimuli. Electroencephalography and Clinical Neurophysiology 68(4), 311–320 (1987). Elsevier
Griggs, R.A.: Psychology: A Concise Introduction. p. 69 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Nicolae, IE., Acqualagna, L., Blankertz, B. (2015). Tapping Neural Correlates of the Depth of Cognitive Processing for Improving Human Computer Interaction. In: Blankertz, B., Jacucci, G., Gamberini, L., Spagnolli, A., Freeman, J. (eds) Symbiotic Interaction. Symbiotic 2015. Lecture Notes in Computer Science(), vol 9359. Springer, Cham. https://doi.org/10.1007/978-3-319-24917-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-24917-9_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24916-2
Online ISBN: 978-3-319-24917-9
eBook Packages: Computer ScienceComputer Science (R0)