System And Method For The Real-Time Evaluation Of Time-Locked Physiological Measures

• US 20090082692A1
• Filed: 09/25/2008
• Published: 03/26/2009
• Est. Priority Date: 09/25/2007
• Status: Abandoned Application

• Current Assignee: Advanced Brain Monitoring, Design Interactive, Inc.
• Original Assignee: Advanced Brain Monitoring, Design Interactive, Inc.
• Inventors: Hale, Kelly S., Berka, Christine, Fuchs, Sven
• Application Number:
  US12/237,985
• Publication Number:
  US 20090082692A1
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 3/113 for determining or recording eye movement
• CPC Class Codes: A61B 5/0531 Measuring skin impedance
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/16 Devices for psychotechnics using teaching or educational appliances G09B1/00 - G09B7/00; Testing reaction times ; Devices for evaluating the psychological state
• CPC Class Codes: A61B 5/163 by tracking eye movement, gaze, or pupil change
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

In one embodiment, a method is provided for classifying cognitive activity in an individual. In the method, a candidate time interval is identified from a first type of physiological data within which cognitive processing is expected to occur for the individual. In addition, a second type of physiological data is obtained that comprises data representative of a cognitive state of the individual. Further, the data representative of a cognitive state of the individual is extracted from the second type of physiological data based on the identified candidate time interval.

Claims

1. A method for classifying cognitive activity in an individual comprising:

(a) identifying a candidate time interval from a first type of physiological data within which cognitive processing is expected to occur for the individual;

(b) obtaining a second type of physiological data comprising data representative of a cognitive state of the individual; and

(c) extracting the data representative of a cognitive state of the individual from the second type of physiological data based on the identified candidate time interval.

2. The method of claim 1, further comprising step (d) of identifying the cognitive state of the individual by comparing the extracted data to known standards representing a particular cognitive state.

3. The method of claim 2, wherein the cognitive processing identified by said step (a) of identifying is evoked by an event, and wherein the method further comprises representing the event to the individual after said step (d) of identifying until a change in the cognitive state is identified.

4. The method of claim 1, wherein the cognitive state is at least one of attention, perception, comprehension, situation awareness, recognition, cognitive workload, alertness, engagement, drowsiness, bias, or confusion.

5. The method of claim 1, wherein the candidate time interval identified by said step (a) of identifying is evoked by a spontaneous event in real-time or near real-time.

6. The method of claim 1, wherein the first type of physiological data and the second type of physiological data are obtained via a first sensor and a second sensor respectively, and wherein the method further comprises synchronizing an output of the first sensor and the second sensor.

7. The method of claim 1, wherein said (a) of identifying is done via a first sensor and a processor, wherein the first sensor is an eye tracking sensor configured to obtain eye activity from the individual, and wherein the processor is configured to determine the candidate time interval within which eye activity occurs.

8. The method of claim 7, wherein the processor determines the candidate time interval by a duration of an ocular fixation.

9. The method of claim 1, wherein the candidate time interval comprises a first endpoint and a second endpoint.

10. The method of claim 1, wherein the candidate time interval comprises a period of time before or after an endpoint.

11. The method of claim 1, wherein the second type of physiological data is in the form of a continuous data stream, and wherein the data representative of a cognitive state of the individual is embedded in the continuous data stream.

12. The method of claim 1, wherein said step (b) of obtaining is done via a second sensor, and wherein the second sensor is an EEG sensor.

13. A system for classifying cognitive activity in an individual comprising:

a first sensor configured to acquire a first type of physiological data from the individual;

a second sensor configured to acquire a second type of physiological data from the individual, wherein the second type of physiological data comprises data representative of a cognitive state of the individual; and

a processor coupled to the first sensor and the second sensor and configured to;

identify a candidate time interval in the first type of physiological data within which cognitive processing is expected to occur for the individual;

extract the data representative of a cognitive state of the individual from the second type of physiological data based on the candidate time interval; and

identify the cognitive state of the individual by comparing the extracted data to at least one standard stored in a memory.

14. The system of claim 13, wherein the cognitive state is at least one of attention, perception, comprehension, situation awareness, recognition, cognitive workload, alertness, engagement, drowsiness, bias, or confusion.

15. The system of claim 13, wherein the cognitive processing is event-evoked cognitive processing.

16. The system of claim 13, wherein the processor is further configured to represent an event to the individual until a change in the cognitive state is identified.

17. The system of claim 13, wherein the processor is further configured to synchronize an output of the first sensor and the second sensor.

18. The system of claim 13, wherein the first sensor is an eye tracking sensor configured to obtain eye activity from the individual, and wherein the processor is configured to determine the candidate time interval from the eye activity.

19. The system of claim 18, wherein the processor is further configured to determine the time interval by a duration of an ocular fixation.

20. The system of claim 13, wherein the second type of physiological data is in the form of a continuous data stream, and wherein the data representative of a cognitive state of the individual is embedded in the continuous data stream.

21. The system of claim 13, wherein the second sensor is an EEG sensor.

22. The system of claim 13, wherein the time interval comprises a first endpoint and a second endpoint.

23. The system of claim 13, wherein the processor is configured to process the data from the first sensor and the second sensor in real-time.