Detection of mental state and reduction of artifacts using functional near infrared spectroscopy (FNIRS)
Inventors
Harrivel, Angela • Hearn, Tristan
Assignees
National Aeronautics and Space Administration NASA
Publication Number
US-9848812-B1
Publication Date
2017-12-26
Expiration Date
2034-05-15
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Abstract
fNIRS may be used in real time or near-real time to detect the mental state of individuals. Phase measurement can be applied to drive an adaptive filter for the removal of motion artifacts in real time or near-real time. In this manner, the application of fNIRS may be extended to practical non-laboratory environments. For example, the mental state of an operator of a vehicle may be monitored, and alerts may be issued and/or an autopilot may be engaged when the mental state of the operator indicates that the operator is inattentive.
Core Innovation
Functional near infrared spectroscopy (fNIRS) can be used in real time or near-real time to detect the mental state of individuals. This is achieved by applying phase measurement to drive an adaptive filter that removes motion artifacts as the data is being collected. This technology extends the use of fNIRS from controlled laboratory environments to practical, non-laboratory settings, enabling continuous monitoring in operational environments.
The problem being addressed is that conventional fNIRS techniques work well only in laboratories and fail to perform effectively outside due to significant motion artifacts and poor optical coupling of probes, which degrade signal quality. There was no standard method to compensate for such artifacts dynamically while maintaining real-time measurement capability. The innovation provides automated bad channel detection and dynamic artifact removal using frequency domain phase shift information and Kalman filtering based on correlated changes in hemoglobin levels and phase shifts.
Claims Coverage
The patent includes independent claims covering a computer-implemented method, a computer program on a non-transitory medium, and a system, each involving use of phase-based artifact detection and Kalman filtering for fNIRS.
Artifact detection based on phase shift correlation
The method or system detects a potential artifact by measuring phase shift in frequency-domain fNIRS data comparing source and detected signal intensities, then confirming artifact presence through correlation between hemoglobin level changes and phase shift changes.
Adaptive artifact removal using Kalman filtering
Artifacts confirmed as correlated are reduced or removed in real time or near-real time using a Kalman filtering approach that adapts filter parameters based on phase variance and correlation, enabling dynamic tuning depending on current process state.
Mental state analysis and attentiveness determination
The method or program analyzes the individual's mental state from filtered hemoglobin signals to determine if the individual is attentive enough to perform a task, enabling real-time monitoring of operator cognitive state.
Automated action upon insufficient attentiveness
Upon detecting insufficient attentiveness, the method or system can send alerts to remote entities, generate audible alarms, engage autopilot, or disable vehicle operation to mitigate risk.
System architecture integrating sensor array, microcontroller, and computing system
The system includes a sensor array for analog hemoglobin data, a microcontroller converting it to digital data, and a computing system performing phase-based artifact detection, correlation analysis, Kalman filtering, and mental state evaluation, suitable for integration with various vehicle types.
The independent claims collectively cover novel use of frequency-domain phase shift measurements to detect motion artifacts, application of Kalman filters dynamically tuned by phase data for artifact reduction, and mental state assessment to support safety-critical task monitoring.
Stated Advantages
Enables real time or near-real time removal of motion artifacts to improve signal quality for fNIRS outside laboratory environments.
Automates bad channel detection and dynamic artifact removal without requiring auxiliary sensors.
Enhances accuracy of mental state and cognitive workload detection by reducing noise and artifacts during ongoing measurement.
Provides objective monitoring of operator attention that can trigger alerts or vehicle control changes to enhance safety.
The Kalman filter tuning based on phase measurements allows filtering only when needed, preserving desired signal changes and minimizing impact when no artifact is present.
Documented Applications
Monitoring the mental state and attentiveness of operators of vehicles including aircraft, spacecraft, cars, trucks, ships, and industrial vehicles.
Use by pilots and astronauts during safety-critical flight tasks to detect inattentiveness and trigger risk mitigation actions such as engaging autopilot.
Non-laboratory practical applications requiring lay person usability, including operator performance research and crew cognition research.
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