Counting on AR: EEG responses to incongruent information with real-world context

被引:0
|
作者
Wimmer, Michael [1 ,2 ]
Pepicelli, Alex [3 ]
Volmer, Ben [3 ]
ElSayed, Neven [1 ]
Cunningham, Andrew [3 ]
Thomas, Bruce H. [3 ]
Müller-Putz, Gernot R. [2 ,4 ]
Veas, Eduardo E. [1 ,5 ]
机构
[1] Know Center Research GmbH, Graz, Austria
[2] Institute of Neural Engineering, Graz University of Technology, Graz, Austria
[3] Wearable Computer Lab, University of South Australia, Adelaide,SA, Australia
[4] BioTechMed, Graz, Austria
[5] Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, Austria
关键词
Brain - Electrocardiography - Electrotherapeutics - Human computer interaction - Image coding;
D O I
10.1016/j.compbiomed.2024.109483
中图分类号
学科分类号
摘要
Augmented Reality (AR) technologies enhance the real world by integrating contextual digital information about physical entities. However, inconsistencies between physical reality and digital augmentations, which may arise from errors in the visualized information or the user's mental context, can considerably impact user experience. This work characterizes the brain dynamics associated with processing incongruent information within an AR environment. To study these effects, we designed an interactive paradigm featuring the manipulation of a Rubik's cube serving as a physical referent. Congruent and incongruent information regarding the cube's current status was presented via symbolic (digits) and non-symbolic (graphs) stimuli, thus examining the impact of different means of data representation. The analysis of electroencephalographic signals from 19 participants revealed the presence of centro-parietal N400 and P600 components following the processing of incongruent information, with significantly increased latencies for non-symbolic stimuli. Additionally, we explored the feasibility of exploiting incongruency effects for brain-computer interfaces. Hence, we implemented decoders using linear discriminant analysis, support vector machines, and EEGNet, achieving comparable performances with all methods. Therefore, this work contributes to the design of adaptive AR systems by demonstrating that above-chance detection of incongruent information based on physiological signals is feasible. The successful decoding of incongruency-induced modulations can inform systems about the current mental state of users without making it explicit, aiming for more coherent and contextually appropriate AR interactions. © 2024
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