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1. A CNN-based personalized system for attention detection in wayfinding tasks

   https://doi.org/doi.org/10.1016/j.aei.2020.101180  

   Wang, Yanchao; Shi, Yangming; Du, Jing; Lin, Yingzi; Wang, Qi (October 2020, Advanced engineering informatics)

   null (Ed.)

   Firefighters are often exposed to extensive wayfinding information in various formats owing to the increasing complexity of the built environment. Because of the individual differences in processing assorted types of information, a personalized cognition-driven intelligent system is necessary to reduce the cognitive load and improve the performance in the wayfinding tasks. However, the mixed and multi-dimensional information during the wayfinding tasks bring severe challenges to intelligent systems in detecting and nowcasting the attention of users. In this research, a virtual wayfinding experiment is designed to simulate the human response when subjects are memorizing or recalling different wayfinding information. Convolutional neural networks (CNNs) are designed for automated attention detection based on the power spectrum density of electroencephalography (EEG) data collected during the experiment. The performance of the personalized model and the generalized model are compared and the result shows a personalized CNN is a powerful classifier in detecting the attention of users with high accuracy and efficiency. The study thus will serve a foundation to support the future development of personalized cognition-driven intelligent systems. 

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2. Comparison and Quantification of Cognitive Load generated by Sternberg Test using EEG Signals

   Wang, Y. (June 2020, Construction Research Congress 2020)

   Driven by the increasing complexity of built environments, firefighters are often exposed to extensive wayfinding information which could cause high cognitive load and ineffective or even dangerous decision making. To reduce injuries and fatal incidents in firefighters’ line of duty, this study aims at measuring the cognitive load and identifying the source of such cognitive overload in wayfinding information review. We developed a Sternberg Test to induce cognitive load on participants pertaining to working memory development, where participants were required to memorize colors, letters, numbers, directions, icons, words, and letter combinations that are relevant to wayfinding tasks. We used an Electroencephalogram (EEG) device to monitor neural activities especially in frontal, parietal, and occipital areas of brain. The fast Fourier transformation (FFT) was applied to separate the sub-band energy. The speed of response in Sternberg Test and the EEG signals were compared to show the coherence between the results of the two methods in representing the cognitive load in the review test. Results indicate that the cognitive load arises from diverse information can be measured to help customize wayfinding information for controlled cognitive load of firefighters in wayfinding tasks. 

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3. Cooperating yet distinct brain networks engaged during naturalistic paradigms: A meta-analysis of functional MRI results

   https://doi.org/10.1162/netn_a_00050  

   Bottenhorn, Katherine L.; Flannery, Jessica S.; Boeving, Emily R.; Riedel, Michael C.; Eickhoff, Simon B.; Sutherland, Matthew T.; Laird, Angela R. (January 2019, Network Neuroscience)

   Cognitive processes do not occur by pure insertion and instead depend on the full complement of co-occurring mental processes, including perceptual and motor functions. As such, there is limited ecological validity to human neuroimaging experiments that use highly controlled tasks to isolate mental processes of interest. However, a growing literature shows how dynamic, interactive tasks have allowed researchers to study cognition as it more naturally occurs. Collective analysis across such neuroimaging experiments may answer broader questions regarding how naturalistic cognition is biologically distributed throughout the brain. We applied an unbiased, data-driven, meta-analytic approach that uses k-means clustering to identify core brain networks engaged across the naturalistic functional neuroimaging literature. Functional decoding allowed us to, then, delineate how information is distributed between these networks throughout the execution of dynamical cognition in realistic settings. This analysis revealed six recurrent patterns of brain activation, representing sensory, domain-specific, and attentional neural networks that support the cognitive demands of naturalistic paradigms. Although gaps in the literature remain, these results suggest that naturalistic fMRI paradigms recruit a common set of networks that allow both separate processing of different streams of information and integration of relevant information to enable flexible cognition and complex behavior. 

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4. Personalize Wayfinding Information for Fire Responders based on Virtual Reality Training Data

   https://doi.org/http://hdl.handle.net/10125/59638  

   Jing Du, Qi Wang (January 2019, Proceedings of the 52nd Hawaii International Conference on System Sciences)

   Modern buildings with increasing complexity can cause serious difficulties for first responders in emergency wayfinding. While real-time data collection and information analytics become easier in indoor wayfinding, a new challenge has arisen: cognitive overload due to information redundancy. Standardized and universal spatial information systems are still widely used in emergency wayfinding, ignoring first responders’ individual difference in information intake. This paper proposes and tests the theoretical framework of a spatial information systems for first responders, which reflects their individual difference in information preference and helps reduce the cognitive load in line of duty. The proposed method includes the use of Virtual Reality (VR) experiments to simulate real world buildings, and the modeling of first responders’ reactions to different information formats and contents in simulated wayfinding tasks. This work is expected to set a foundation of future spatial information system that correctly and effectively responds to first responders’ needs. 

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5. Multimodal fusion of EEG-fNIRS: a mutual information-based hybrid classification framework

   https://doi.org/10.1364/BOE.413666  

   Deligani, Roohollah_Jafari; Borgheai, Seyyed_Bahram; McLinden, John; Shahriari, Yalda (February 2021, Biomedical Optics Express)

   Multimodal data fusion is one of the current primary neuroimaging research directions to overcome the fundamental limitations of individual modalities by exploiting complementary information from different modalities. Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are especially compelling modalities due to their potentially complementary features reflecting the electro-hemodynamic characteristics of neural responses. However, the current multimodal studies lack a comprehensive systematic approach to properly merge the complementary features from their multimodal data. Identifying a systematic approach to properly fuse EEG-fNIRS data and exploit their complementary potential is crucial in improving performance. This paper proposes a framework for classifying fused EEG-fNIRS data at the feature level, relying on a mutual information-based feature selection approach with respect to the complementarity between features. The goal is to optimize the complementarity, redundancy and relevance between multimodal features with respect to the class labels as belonging to a pathological condition or healthy control. Nine amyotrophic lateral sclerosis (ALS) patients and nine controls underwent multimodal data recording during a visuo-mental task. Multiple spectral and temporal features were extracted and fed to a feature selection algorithm followed by a classifier, which selected the optimized subset of features through a cross-validation process. The results demonstrated considerably improved hybrid classification performance compared to the individual modalities and compared to conventional classification without feature selection, suggesting a potential efficacy of our proposed framework for wider neuro-clinical applications. 

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