Using fNIRS brain sensing in realistic HCI settings

Future conflicts will necessitate the ability to conduct effective military operations in a contested information environment. The building and maintaining of robust situational awareness, protection of decision-making effectiveness of individuals and teams, fighting through information attacks from both in, and through, the cyberspace domain, will be essential. Increasing the knowledge of the mechanisms involved in degrading task performance and decision-making during cyber attacks will enable the development of advanced human-centered defensive techniques that aid fight-through capability. In this position paper, the development and evaluation of software that simulates realtime and persistent manipulation of the information environment is discussed. Results of the evaluation indicated that the task performance of a team of decision-makers performing collaborative tasks could be degraded through real-time manipulation of cyberspace content and operation. The paper concludes with a discussion of focus and direction for future research and development. It is suggested that the building of a deeper understanding of the perceptual and cognitive factors that are significant in the relationship between information environment manipulation and reduction in task performance is required. This understanding will aid in the defence of cyberspace attacks, will aid in fight through and mission assurance, and will aid the Information Operations community.

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“…Prior research has shown that this minimal movement does not corrupt the fNIRS signal with motion artifacts [19].…”

Section: Brain Imaging Of Changes To Cognitive Processing and Situatimentioning

confidence: 94%

Decision-making and emotions in the contested information environment

Haas¹,

Hirshfield²,

Ponangi³

et al. 2013

ICST Transactions on Security and Safety

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“…In addition, it is resilient to head movement, facial movement, ambient noise, heartbeat, and muscle movement [17,35]. This is critical for complex environments where the user must be able to function freely and normally.…”

Section: Fnirs and Prefrontal Cortexmentioning

confidence: 99%

“…It is a lightweight and non-intrusive sensor, and can be affixed to the forehead while allowing users to still function normally. Additionally, it has been found to be resistant to movement artifacts in comparison to other sensors [35]. Because of this, it is ideal for a passive input, where users still perform a task normally but the system knows and adjusts to their overall cognitive state.…”

Section: Introductionmentioning

confidence: 99%

Dynamic difficulty using brain metrics of workload

Afergan

Peck

Solovey

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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119

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Dynamic difficulty adjustments can be used in humancomputer systems in order to improve user engagement and performance. In this paper, we use functional near-infrared spectroscopy (fNIRS) to obtain passive brain sensing data and detect extended periods of boredom or overload. From these physiological signals, we can adapt a simulation in order to optimize workload in real-time, which allows the system to better fit the task to the user from moment to moment. To demonstrate this idea, we ran a laboratory study in which participants performed path planning for multiple unmanned aerial vehicles (UAVs) in a simulation. Based on their state, we varied the difficulty of the task by adding or removing UAVs and found that we were able to decrease errors by 35% over a baseline condition. Our results show that we can use fNIRS brain sensing to detect task difficulty in real-time and construct an interface that improves user performance through dynamic difficulty adjustment.

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“…fNIRS is an optical brain sensing technology that has seen increased use in HCI research, largely because of its simple setup and resistance to movement artifacts [1,15,19,34,35]. Using near-infrared light, fNIRS measures localized levels of oxygenated and deoxygenated hemoglobin in the brain, successfully replicating results from fMRI studies [36,38].…”

Section: Using Fnirs To Detect Preferencementioning

confidence: 99%

Investigation of fNIRS brain sensing as input to information filtering systems

Peck

Afergan

Jacob

2013

Proceedings of the 4th Augmented Human International Conference

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Today's users interact with an increasing amount of information, demanding a similar increase in attention and cognition. To help cope with information overload, recommendation engines direct users' attention to content that is most relevant to them. We suggest that functional near-infrared spectroscopy (fNIRS) brain measures can be used as an additional channel to information filtering systems. Using fNIRS, we acquire an implicit measure that correlates with user preference, thus avoiding the cognitive interruption that accompanies explicit preference ratings. We explore the use of fNIRS in information filtering systems by building and evaluating a brain-computer movie recommender. We find that our system recommends movies that are rated higher than in a control condition, improves recommendations with increased interaction with the system, and provides recommendations that are unique to each individual.

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Using fNIRS brain sensing in realistic HCI settings

Cited by 93 publications

References 18 publications

Decision-making and emotions in the contested information environment

Decision-making and emotions in the contested information environment

Dynamic difficulty using brain metrics of workload

Investigation of fNIRS brain sensing as input to information filtering systems

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