Development of a real-time cognitive state estimator

The combination of advances in cognitive neuroscience & the development of powerful, yet portable, devices are enabling real-time assessments in education. Electroencephalography (EEG) & functional near-infrared spectroscopy (fNIRS) are two such neuroimaging tools being explored during instruction, with a current focus on student attention, engagement, & cognitive load. Neural synchrony has been linked with successful knowledge transfer, social interaction quality, engagement level, & shared attention. These studies, along with ergonomic considerations, are the focus of this literature review. Real-time learning assessments can optimize both teacher & student performance & satisfaction. A tailored instruction has the potential to create optimal learning experiences & more-proactively address learning difficulties.

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An evaluation of real-time cognitive state classification in a harsh operational environment

PsycEXTRA Dataset ◽

10.1037/e577842012-002 ◽

2007 ◽

Author(s):

Michael C. Dorneich ◽

Santosh Mathan ◽

Patricia May Ververs ◽

Stephen D. Whitlow

Keyword(s):

Real Time ◽

Cognitive State ◽

Operational Environment ◽

State Classification

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Using Real-Time Physiological Monitoring for Assessing Cognitive States

Digital Multimedia Perception and Design ◽

10.4018/978-1-59140-860-4.ch008 ◽

2006 ◽

pp. 170-186 ◽

Cited By ~ 3

Author(s):

Martha E. Crosby ◽

Curtis S. Ikehara

Keyword(s):

Real Time ◽

Information Filtering ◽

Cognitive State ◽

Performance Study ◽

Computer Performance ◽

Advantages And Disadvantages ◽

Cognitive States ◽

Computer Mouse ◽

Study Results ◽

Physiological Sensors

This chapter describes our research focused on deriving changing cognitive state information from the patterns of data acquired from the user, with the goal of using this information to improve the presentation of multimedia computer information. Detecting individual differences via performance and psychometric tools can be supplemented by using real-time physiological sensors. Described is an example computer task that demonstrates how cognitive load is manipulated. The different types of physiological and cognitive state measures are discussed along with their advantages and disadvantages. Experimental results from eye tracking and the pressures applied to a computer mouse are described in greater detail. Finally, adaptive information filtering is discussed as a model for using the physiological information to improve computer performance. Study results provide support that we can create effective ways to adapt to a person’s cognition in real time and thus facilitate real-world tasks.

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Driver Cognitive Distraction Detection Using Driving Performance Measures

Discrete Dynamics in Nature and Society ◽

10.1155/2012/432634 ◽

2012 ◽

Vol 2012 ◽

pp. 1-12 ◽

Cited By ~ 30

Author(s):

Lisheng Jin ◽

Qingning Niu ◽

Haijing Hou ◽

Huacai Xian ◽

Yali Wang ◽

...

Keyword(s):

Real Time ◽

Performance Measures ◽

Traffic Accidents ◽

Can Bus ◽

Driving Performance ◽

Cognitive State ◽

Area Network ◽

Characteristic Parameters ◽

Secondary Tasks ◽

Cognitive Distraction

Driver cognitive distraction is a hazard state, which can easily lead to traffic accidents. This study focuses on detecting the driver cognitive distraction state based on driving performance measures. Characteristic parameters could be directly extracted from Controller Area Network-(CAN-)Bus data, without depending on other sensors, which improves real-time and robustness performance. Three cognitive distraction states (no cognitive distraction, low cognitive distraction, and high cognitive distraction) were defined using different secondary tasks. NLModel, NHModel, LHModel, and NLHModel were developed using SVMs according to different states. The developed system shows promising results, which can correctly classify the driver’s states in approximately 74%. Although the sensitivity for these models is low, it is acceptable because in this situation the driver could control the car sufficiently. Thus, driving performance measures could be used alone to detect driver cognitive state.

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