Fast mental states decoding in mixed reality

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• dc.contributor.author De Massari, Danieleca
• dc.contributor.author Pacheco, Danielca
• dc.contributor.author Malekshahi, Rahimca
• dc.contributor.author Betella, Albertoca
• dc.contributor.author Verschure, Paul F. M. J.ca
• dc.contributor.author Birbaumer, Nielsca
• dc.contributor.author Caria, Andreaca
• dc.date.accessioned 2016-02-10T13:46:04Z
• dc.date.available 2016-02-10T13:46:04Z
• dc.date.issued 2014
• dc.description.abstract The combination of Brain-Computer Interface (BCI) technology, allowing online monitoring and decoding of brain activity, with virtual and mixed reality (MR) systems may help to shape and guide implicit and explicit learning using ecological scenarios. Real-time information of ongoing brain states acquired through BCI might be exploited for controlling data presentation in virtual environments. Brain states discrimination during mixed reality experience is thus critical for adapting specific data features to contingent brain activity. In this study we recorded electroencephalographic (EEG) data while participants experienced MR scenarios implemented through the eXperience Induction Machine (XIM). The XIM is a novel framework modeling the integration of a sensing system that evaluates and measures physiological and psychological states with a number of actuators and effectors that coherently reacts to the user's actions. We then assessed continuous EEG-based discrimination of spatial navigation, reading and calculation performed in MR, using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Dynamic single trial classification showed high accuracy of LDA and SVM classifiers in detecting multiple brain states as well as in differentiating between high and low mental workload, using a 5 s time-window shifting every 200 ms. Our results indicate overall better performance of LDA with respect to SVM and suggest applicability of our approach in a BCI-controlled MR scenario. Ultimately, successful prediction of brain states might be used to drive adaptation of data representation in order to boost information processing in MR.
• dc.description.sponsorship The present study was supported by EU grants: FP7-ICT-2009-258749 CEEDs: The Collective Experience of Empathic Data Systems; FP7-ICT-2013- 609593 BNCI Horizon 2020. The Future of Brain/Neural Computer Interaction: Horizon 2020; Italian Ministry of Health, GR-2009-1591908.
• dc.format.mimetype application/pdfca
• dc.identifier.citation De Massari D, Pacheco D, Malekshahi R, Betella A, Verschure PFMJ, Birbaumer N, Caria A. Fast mental states decoding in mixed reality. Front. Behav. Neurosci. 2014;415(8):1-9. DOI: 10.3389/fnbeh.2014.00415.
• dc.identifier.doi http://dx.doi.org/10.3389/fnbeh.2014.00415
• dc.identifier.issn 1662-5153
• dc.identifier.uri http://hdl.handle.net/10230/25770
• dc.language.iso engca
• dc.publisher Frontiers
• dc.relation.ispartof Frontiers in behavioral neuroscience 2014;415(8):1-9.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/258749
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/609593
• dc.rights © 2014 De Massari, Pacheco, Malekshahi, Betella, Verschure, Birbaumer and Caria. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.ca
• dc.rights.accessRights info:eu-repo/semantics/openAccessca
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Mental states decoding
• dc.subject.keyword EEG
• dc.subject.keyword Mixed reality
• dc.subject.keyword XIM
• dc.title Fast mental states decoding in mixed realityca
• dc.type info:eu-repo/semantics/articleca
• dc.type.version info:eu-repo/semantics/publishedVersionca