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Cross-task cognitive workload measurement based on the sample selection of the EEG data

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Abstract

Assessing the cognitive workload of operators can predict the risk of human performance degradation and improves the safety and reliability of human-machine systems. The level of the cognitive workload can be decoded by electroencephalographs (EEGs) with a passive brain-computer interface. However, occupied mental resources reflected by EEGs can be induced by different task demands. It leads to difficulty in building a generic computational model that maps EEG data to interpretable workload levels across a wide range of human-machine tasks. To discover stable workload indicators under different task environments, in this study we propose a novel approach to learn spatial-frequency feature abstractions from the EEG. We used several common classification models based on feature selection to explore the factors that affect crosstask cognitive workload. We collected two EEG databases under an n-back task and a multi-tasking simultaneous capacity testing (SIMKAP) task, respectively. In total, the 14-channel EEG of two groups of participants was recorded by a commercial wireless headset. The EEG database from one task is used to build the workload classifier and the other is used to validate the predicting accuracy of the trained classifier. The result demonstrates that when the training set size is sufficient, the classification accuracy of cross-task cognitive workload is improved with the introduction of testing set data into the training set. The removal of specific subjects in the training set had an impact on the classification accuracy of cross-task cognitive workload, but it was mainly attributed to the change in data set size.

Abstract

Assessing the cognitive workload of operators can predict the risk of human performance degradation and improves the safety and reliability of human-machine systems. The level of the cognitive workload can be decoded by electroencephalographs (EEGs) with a passive brain-computer interface. However, occupied mental resources reflected by EEGs can be induced by different task demands. It leads to difficulty in building a generic computational model that maps EEG data to interpretable workload levels across a wide range of human-machine tasks. To discover stable workload indicators under different task environments, in this study we propose a novel approach to learn spatial-frequency feature abstractions from the EEG. We used several common classification models based on feature selection to explore the factors that affect crosstask cognitive workload. We collected two EEG databases under an n-back task and a multi-tasking simultaneous capacity testing (SIMKAP) task, respectively. In total, the 14-channel EEG of two groups of participants was recorded by a commercial wireless headset. The EEG database from one task is used to build the workload classifier and the other is used to validate the predicting accuracy of the trained classifier. The result demonstrates that when the training set size is sufficient, the classification accuracy of cross-task cognitive workload is improved with the introduction of testing set data into the training set. The removal of specific subjects in the training set had an impact on the classification accuracy of cross-task cognitive workload, but it was mainly attributed to the change in data set size.

Chapters in this book

  1. Frontmatter I
  2. Prologue I VII
  3. Prologue II XI
  4. Prologue III XIII
  5. Preface XVII
  6. Overview XIX
  7. Contents XXXIII
  8. Part I: Theories
  9. Part I-A: Overarching theory
  10. Introduction 1
  11. Universal axioms in classical Chinese philosophy 5
  12. Category theory for structural characterization 15
  13. Axiomatic bipolar dynamics and their control 45
  14. Part I-B: Systems theories
  15. Introduction 75
  16. Stochastic formalization of agent-oriented systems 79
  17. Simplification of high-dimensional multitempo dynamic models 109
  18. Ideas of symmetry as a biophysical basis of system biomedicine 123
  19. Disorder of multiscale control 149
  20. Part II: Person’s life-sphere
  21. Part II-A: Person’s biosphere
  22. Introduction 185
  23. Mutations as activators of biological evolutionary processes at population levels 189
  24. Immunometabolism of T-cells in COVID-19 209
  25. Part II-A.2: Body’s vital functions
  26. Introduction 245
  27. Structural modeling of vascular networks 249
  28. Mathematical modeling of AI application for the diagnosis of blood flow disorders 283
  29. Modeling of glucose and insulin regulation within the framework of a self-consistent model of the cardiovascular system 303
  30. Hemodynamics in residual myocardial ischemia 319
  31. The quasi-one-dimensional model of the lymph flow in the human lymphatic system 335
  32. An integrate-and-fire mechanism for modeling rhythmicity in the neuroendocrine system 365
  33. Kinetic network modeling of the neuroendocrine hypothalamic-pituitary-adrenal axis dynamics with particular attention on the role of alcohol as a digestif 377
  34. Inflammation and immune response in atherosclerosis 393
  35. Part II-A.3: Body’s motor functions
  36. Introduction 423
  37. A magnetic resonance spectroscopy approach to quantitatively measure GABA and phosphorus level changes in the primary motor cortex elicited by transcranial direct current stimulation 427
  38. Part II-A.4: Body’s operational functions
  39. Introduction 441
  40. The fermionic mind hypothesis–a category-theoretic verification of consciousness 445
  41. Cross-task cognitive workload measurement based on the sample selection of the EEG data 459
  42. Part II-B: Person’s eco-sphere exposures
  43. Introduction 475
  44. The spread of SARS-CoV-2 in Russia and the evolution of the properties of the pathogen 479
  45. Agent-based modeling of epidemic spread via kinetic Monte Carlo method 491
  46. Control of SARS-nCoV outbreaks in China 2020 513
  47. Part II-B.2: Civilization
  48. Introduction 531
  49. Pesticide exposure: Toward holistic environmental modeling 535
  50. Part II-C: Person’s sociosphere exposures
  51. Introduction 559
  52. Evolution of the health system in Shanghai, China, 2016–2020 563
  53. Part III: Technologies
  54. Introduction 577
  55. Design-process automation using functional process blocks 581
  56. Slow/fast dynamic models with applications to engineering problems 601
  57. Part III-B: Information sciences
  58. Introduction 613
  59. Numerical modeling of medical ultrasound using the grid-characteristic method 617
  60. The direct and the inverse magnetic encephalography problem 635
  61. Part III-C: Data-analytic sciences
  62. Introduction 653
  63. Assessing the bioequivalence of two different drugs with the same active ingredient 655
  64. Estimation of adjusted relative risks in log-binomial regression using the Bekhit–Schöpe–Wagenpfeil algorithm 665
  65. Part IV: Clinical medicine
  66. Introduction 679
  67. Finding optimal two-stage combined treatment protocols for a blood cancer model 681
  68. Unraveling the mysteries: Mathematical perspectives on traditional Chinese medicine meridians 697
  69. Epilogue 721
  70. Index 723
https://doi.org/10.1515/9783111341996-026

Chapters in this book

  1. Frontmatter I
  2. Prologue I VII
  3. Prologue II XI
  4. Prologue III XIII
  5. Preface XVII
  6. Overview XIX
  7. Contents XXXIII
  8. Part I: Theories
  9. Part I-A: Overarching theory
  10. Introduction 1
  11. Universal axioms in classical Chinese philosophy 5
  12. Category theory for structural characterization 15
  13. Axiomatic bipolar dynamics and their control 45
  14. Part I-B: Systems theories
  15. Introduction 75
  16. Stochastic formalization of agent-oriented systems 79
  17. Simplification of high-dimensional multitempo dynamic models 109
  18. Ideas of symmetry as a biophysical basis of system biomedicine 123
  19. Disorder of multiscale control 149
  20. Part II: Person’s life-sphere
  21. Part II-A: Person’s biosphere
  22. Introduction 185
  23. Mutations as activators of biological evolutionary processes at population levels 189
  24. Immunometabolism of T-cells in COVID-19 209
  25. Part II-A.2: Body’s vital functions
  26. Introduction 245
  27. Structural modeling of vascular networks 249
  28. Mathematical modeling of AI application for the diagnosis of blood flow disorders 283
  29. Modeling of glucose and insulin regulation within the framework of a self-consistent model of the cardiovascular system 303
  30. Hemodynamics in residual myocardial ischemia 319
  31. The quasi-one-dimensional model of the lymph flow in the human lymphatic system 335
  32. An integrate-and-fire mechanism for modeling rhythmicity in the neuroendocrine system 365
  33. Kinetic network modeling of the neuroendocrine hypothalamic-pituitary-adrenal axis dynamics with particular attention on the role of alcohol as a digestif 377
  34. Inflammation and immune response in atherosclerosis 393
  35. Part II-A.3: Body’s motor functions
  36. Introduction 423
  37. A magnetic resonance spectroscopy approach to quantitatively measure GABA and phosphorus level changes in the primary motor cortex elicited by transcranial direct current stimulation 427
  38. Part II-A.4: Body’s operational functions
  39. Introduction 441
  40. The fermionic mind hypothesis–a category-theoretic verification of consciousness 445
  41. Cross-task cognitive workload measurement based on the sample selection of the EEG data 459
  42. Part II-B: Person’s eco-sphere exposures
  43. Introduction 475
  44. The spread of SARS-CoV-2 in Russia and the evolution of the properties of the pathogen 479
  45. Agent-based modeling of epidemic spread via kinetic Monte Carlo method 491
  46. Control of SARS-nCoV outbreaks in China 2020 513
  47. Part II-B.2: Civilization
  48. Introduction 531
  49. Pesticide exposure: Toward holistic environmental modeling 535
  50. Part II-C: Person’s sociosphere exposures
  51. Introduction 559
  52. Evolution of the health system in Shanghai, China, 2016–2020 563
  53. Part III: Technologies
  54. Introduction 577
  55. Design-process automation using functional process blocks 581
  56. Slow/fast dynamic models with applications to engineering problems 601
  57. Part III-B: Information sciences
  58. Introduction 613
  59. Numerical modeling of medical ultrasound using the grid-characteristic method 617
  60. The direct and the inverse magnetic encephalography problem 635
  61. Part III-C: Data-analytic sciences
  62. Introduction 653
  63. Assessing the bioequivalence of two different drugs with the same active ingredient 655
  64. Estimation of adjusted relative risks in log-binomial regression using the Bekhit–Schöpe–Wagenpfeil algorithm 665
  65. Part IV: Clinical medicine
  66. Introduction 679
  67. Finding optimal two-stage combined treatment protocols for a blood cancer model 681
  68. Unraveling the mysteries: Mathematical perspectives on traditional Chinese medicine meridians 697
  69. Epilogue 721
  70. Index 723
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