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Title: Comparison and Quantification of Cognitive Load generated by Sternberg Test using EEG Signals
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.
Authors:
Award ID(s):
1937878
Publication Date:
NSF-PAR ID:
10152098
Journal Name:
Construction Research Congress 2020
Sponsoring Org:
National Science Foundation
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Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. 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  5. 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.