ed7d73aaf579708fd6481e2cfe5661d7 sen_a2024m2v24n4p1174.pdf 6aee7f31c067862e4f34836efe6ff817acc0ebcc sen_a2024m2v24n4p1174.pdf 3f6511a45175b0a64f1935731d930c6b8d1d32dcca4529e158cf3a1470a057f5 sen_a2024m2v24n4p1174.pdf Title: EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment Subject: High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models. Keywords: mental workload; serious games; flight simulation; EEG physiological data; deep learning; transfer learning Author: Aura Hernández-Sabaté, José Yauri, Pau Folch, Daniel Álvarez and Debora Gil Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Sun Feb 18 10:22:43 2024 CET ModDate: Sun Feb 18 10:28:38 2024 CET Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 26 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 3171181 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- YKMTKI+URWPalladioL-Bold Type 1 Custom yes yes yes 10 0 BVUEUL+URWPalladioL-Roma Type 1 Custom yes yes yes 16 0 TWFCSM+URWPalladioL-Ital Type 1 Custom yes yes yes 21 0 RVSEMT+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 37 0 VQEPBT+PazoMath-Italic Type 1 Builtin yes yes yes 58 0 SYFPBV+CMMI10 Type 1 Builtin yes yes yes 63 0 DHSTTU+CMSY10 Type 1 Builtin yes yes yes 86 0 KKVLMQ+URWPalladioL-BoldItal Type 1 Custom yes yes yes 102 0 XJNTTY+SFTT1000 Type 1 Custom yes yes yes 125 0 MTLXPL+CMR10 Type 1 Builtin yes yes yes 169 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-05-14 02:43:31 CEST RepresentationInformation: sen_a2024m2v24n4p1174.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2024-05-13 16:07:05 CEST Size: 3171181 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 339 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Data Acquisition Description Destination: section.2 Children: Item: Title: EEG Device Characteristics Destination: subsection.2.1 Item: Title: Participants Destination: subsection.2.2 Item: Title: N-Back Test Experiment Destination: subsection.2.3 Item: Title: Heat-the-Chair Experiment Destination: subsection.2.4 Item: Title: Flight Simulation Experiment Destination: subsection.2.5 Item: Title: Data Format and Structure Destination: section.3 Children: Item: Title: N-Back Test Destination: subsection.3.1 Item: Title: Heat-the-Chair Game Destination: subsection.3.2 Item: Title: Flight Simulator Destination: subsection.3.3 Item: Title: Data Validation Destination: section.4 Children: Item: Title: Correlation to the Perceived Difficulty Destination: subsection.4.1 Item: Title: Analysis of Differences in Temporal Patterns Destination: subsection.4.2 Item: Title: Usefulness for Training AI Models Destination: subsection.4.3 Item: Title: Conclusions Destination: section.5 Item: Title: AppendixA Destination: appendix.A. Item: Title: References Destination: appendix.B. Info: Title: EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment Author: Aura Hernández-Sabaté, José Yauri, Pau Folch, Daniel Álvarez and Debora Gil Subject: High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models. 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