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PD ISO/IEC TR 27599:2025 Information Technology. Brain-computer Interfaces. Use Cases, 2025
- undefined
- CONTENTS
- FOREWORD
- INTRODUCTION
- 1 Scope
- 2 Normative references
- 3 Terms and definitions
- 4 Abbreviated terms
- 5 Data analysis of BCI use cases [Go to Page]
- 5.1 List of use cases
- Table 1 – List of use cases [Go to Page]
- 5.2 Application domains
- 5.3 Data characteristics and processing methods [Go to Page]
- 5.3.1 General
- 5.3.2 The characteristics of EEG
- Figures [Go to Page]
- Figure 1 – Application domains of all the collected BCI use cases [Go to Page]
- 5.3.3 EEG signal processing methods
- 5.4 BCI common challenges and issues [Go to Page]
- 5.4.1 General
- Figure 2 – Common challenges among BCI use cases [Go to Page]
- 5.4.2 Automatic labelling
- 5.4.3 Experimental preparation
- 5.4.4 Mind focusing requirement
- 5.4.5 Data security
- 5.4.6 Minimized damage for implantable BCI
- 5.4.7 Signal acquisition (noise, interference, etc.)
- 5.4.8 Effect difference on inter-subjects or inter-devices
- 5.4.9 Algorithm-related signal classification accuracy
- 5.4.10 Portable and comfortable BCI system
- 5.4.11 Amount of data
- 5.5 Types, setup, benefits and portability [Go to Page]
- 5.5.1 Invasive or non-invasive
- 5.5.2 Required acquisition setup
- 5.5.3 Benefits
- Figure 3 – Invasiveness of all the collected BCI use cases [Go to Page]
- 5.5.4 Portability
- Figure 4 – Benefit of the BCI use cases
- Figure 5 – Portability of the use cases
- 6 Standardization requirements [Go to Page]
- 6.1 Summary of standardization requirements of collected BCI use cases
- Table 2 – The list of standardization requirements from all the collected BCI use cases [Go to Page]
- 6.2 Standardization requirements analysis of collected BCI use cases [Go to Page]
- 6.2.1 Standardization requirements categorization
- Figure 6 – Standardization requirements statistics of the collected BCI use cases [Go to Page]
- 6.2.2 Standardization requirements statistics
- 6.2.3 Standardization requirements discussion
- 6.3 Conclusion
- 7 Use cases [Go to Page]
- 7.1 Overview
- 7.2 General information on use case
- 7.3 Smart environment [Go to Page]
- 7.3.1 Passive brain-computer interface-based adaptive automation (use case 1)
- 7.3.2 BCI-based smart ward system (use case 2)
- 7.3.3 Brain-machine interface (BMI) enabled assistive communication system (use case 3)
- 7.3.4 Monitoring and early warning technology for the fitness of special operations personnel based on EEG signals (use case 4)
- 7.4 Medical and health [Go to Page]
- 7.4.1 Minimally invasive implanted closed-loop brain-computer interface system (use case 5)
- 7.4.2 Neural state dependent closed-loop deep brain stimulation (use case 6)
- 7.4.3 Invasive brain cursor control system (use case 7)
- 7.4.4 Multi-site closed-loop neurostimulation for clinical seizure modulation (use case 8)
- 7.4.5 AR-based brain-computer interface for upper limb rehabilitation (use case 9)
- 7.4.6 Brain-controlled robot grabbing to assist daily life (use case 10)
- 7.4.7 Rehabilitation training system based on MI-BCI (use case 11)
- 7.4.8 Brain-computer interface in diagnosis and treatment of depression (use case 12)
- 7.4.9 The M-score: motor function assessment using BCI (use case 13)
- 7.4.10 Shen Gong robotics: BCI-driven rehabilitation training system (use case 14)
- 7.4.11 Music intervention based on brain-computer interface system (use case 15)
- 7.4.12 Wearable seizure onset detection system (use case 16)
- 7.4.13 Clinical diagnosis and prognosis in patients with disorders of consciousness (DOC) (use case 17)
- 7.4.14 An adaptive AR display to improve situational awareness using BCI in stressful and fatigue-inducing situations (use case 18)
- 7.4.15 Portable brain-related symptom screening, monitoring and surveillance system using non-invasive electroencephalograph (use case 19)
- 7.4.16 Portable brain-related symptom management system using non-invasive brain stimulation (use case 20)
- 7.4.17 Automated seizure detection and prediction (use case 21)
- 7.4.18 Near-infrared BCI intervention in patients with stroke (use case 22)
- 7.4.19 Fusion of multi-modal fNIRs and EEG information for motor imagery classification (use case 23)
- 7.4.20 BCI controlled exoskeleton with seven degrees of freedom for assistance and rehabilitation applications (use case 24)
- 7.4.21 BCI controlled wheelchair for assistance and rehabilitation (use case 25)
- 7.5 Learning, education and training [Go to Page]
- 7.5.1 Reading assessment apparatus (RAA) (use case 26)
- 7.5.2 BCI-based biofeedback for accelerated learning (use case 27)
- 7.6 Industrial controls [Go to Page]
- 7.6.1 Brain-computer interface in aerospace applications (use case 28)
- 7.6.2 T-Drone (use case 29)
- 7.7 Gaming [Go to Page]
- 7.7.1 Cognitive regulation based on brain-computer interface game (use case 30)
- 7.7.2 The MindGomoku: an online P300 BCI game (use case 31)
- 7.8 Security and authentication [Go to Page]
- 7.8.1 Non-invasive brain signal-based biometrics system (use case 32)
- Figure A.1 – Experimental demonstration of pBCI-based adaptive automation
- Annex A (informative) Figures from the collected use cases [Go to Page]
- A.1 Passive brain-computer interface (pBCI)-based adaptive automation (AA)
- Figure A.2 – The BCI-based smart ward
- A.2 BCI-based smart ward system
- A.3 Brain-machine interface (BMI) enabled assistive communication system
- Figure A.3 – A BCI based real-time sensing equipment and early warning system
- Figure A.4 – Minimally invasive implanted closed-loop brain-computer interface system
- A.4 Monitoring and early warning technology for the fitness of special operations personnel based on EEG signals
- A.5 Minimally invasive implanted closed-loop brain-computer interface system
- Figure A.5 – Neural state discrimination method
- A.6 Neural state dependent closed-loop deep brain stimulation
- Figure A.6 – Neurostimulator for deep brain stimulation therapy
- Figure A.7 – Invasive brain cursor control system
- A.7 Invasive brain cursor control system
- Figure A.8 – Framework of hardware
- A.8 Multi-site closed-loop neurostimulation for clinical seizure modulation
- Figure A.9 – Framework of the classifier for seizure detection
- Figure A.10 – Flowchart of the AR-based brain-computer interface
- Figure A.11 – AR environment of the AR-based brain-computer interface
- A.9 AR-based brain-computer interface
- Figure A.12 – Feedback of the AR-based brain-computer interface
- Figure A.13 – Brain-controlled robot grabbing to assist daily life
- A.10 Brain-controlled robot grabbing to assist daily life
- Figure A.14 – Graphical user interface of brain-controlled robot grabbing to assist daily life
- Figure A.15 – Rehabilitation training system based on MI-BCI
- A.11 Rehabilitation training system based on MI-BCI
- Figure A.16 – Graphical user interface of the rehabilitation training system based on MI-BCI
- Figure A.17 – Complete system setup and application environment
- A.12 Brain-computer interface in diagnosis and treatment of depression
- Figure A.18 – The hardware system of the user case
- A.13 The M-score: motor function assessment using BCI
- Figure A.19 – The system of Shen Gong robotics
- Figure A.20 – User gained improvement in handwriting after treatment using the Shen Gong robotic series
- A.14 Shen Gong robotics: BCI-driven rehabilitation training system
- Figure A.21 – Product example
- Figure A.22 – Principles of brain-wave music
- A.15 Music intervention based on the brain-computer interface system
- Figure A.23 – Framework of system
- Figure A.24 – Wearable seizure onset detection system
- A.16 Wearable seizure onset detection system
- Figure A.25 – Patient with cognitive motor dissociation (CMD) selecting his or her own photograph from two candidates using an EEG-based BCI
- Figure A.26 – A patient with disorders of consciousness is detecting awareness using an EEG-based BCI
- A.17 Clinical diagnosis and prognosis in patients with DOC
- Figure A.27 – The data processing and decision-making procedure of a trial for BCI-based awareness detection
- Figure A.28 – An example of a stress-inducing experimental paradigm
- A.18 An adaptive AR display to improve situational awareness using BCI in stressful and fatigue-inducing situations
- Figure A.29 – The outline of the experimental task to assess cognitive performance when fatigued
- Figure A.30 – Clinic-to-home electroceutical platform
- A.19 Portable brain-related symptom screening, monitoring and surveillance system using non-invasive electroencephalograph
- Figure A.31 – Two high degree of freedom exoskeletons utilized in this use case, one on the left is developed with portable design and the one on the right is developed with stationary design
- A.20 Portable brain-related symptom management system using non-invasive brain stimulation
- A.21 Automated seizure detection and prediction
- A.22 Near-infrared BCI intervention in patients with stroke
- A.23 Fusion of multi-modal fNIRs and EEG information for motor imagery classification
- A.24 BCI controlled exoskeleton with seven degrees of freedom for assistance and rehabilitation applications
- Figure A.32 – The graphical user interface used in this use case
- Figure A.33 – Brain-controlled wheelchair with intelligent obstacle avoidance
- A.25 BCI controlled wheelchair for assistance and rehabilitation
- Figure A.34 – Reading assessment apparatus (RAA)
- Figure A.35 – A developed biofeedback-supported intelligent training system
- A.26 Reading assessment apparatus (RAA)
- A.27 BCI-based biofeedback for accelerated learning
- Figure A.36 – Overall architecture of the developed biofeedback-supported intelligent training system
- Figure A.37 – The graphical user interface of the user case
- A.28 Brain-computer interface in aerospace applications
- Figure A.38 – The hardware system of the user case
- A.29 T-Drone
- Figure A.39 – Product example
- A.30 Cognitive regulation based on brain-computer interface game
- Figure A.40 – Framework of BCI game
- Figure A.41 – The framework of the BCI game consisting of three subsystems: (a) data acquisition, (b) data processing, and (c) visual and game terminal
- A.31 The MindGomoku: an online P300 BCI game
- Figure A.42 – An illustration of MindGomoku
- Figure A.43 – Non-invasive brain signal-based biometrics system
- A.32 Non-invasive brain signal-based biometrics system
- Annex B (informative) Use case template
- Table B.1 – Use case template
- Bibliography [Go to Page]
