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23
Dynamic Difficulty Using Brain Metrics of Workload
- In Proc. ACM CHI 2014, ACM Press
, 2014
"... 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 physiologic ..."
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Cited by 14 (8 self)
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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. Author Keywords BCI; passive brain-computer interface; dynamic difficulty;
Using fNIRS Brain Sensing to Evaluate Information Visualization Interfaces
"... We show how brain sensing can lend insight to the evaluation of visual interfaces and establish a role for fNIRS in visualization. Research suggests that the evaluation of visual design benefits by going beyond performance measures or questionnaires to measurements of the user’s cognitive state. Unf ..."
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Cited by 11 (5 self)
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We show how brain sensing can lend insight to the evaluation of visual interfaces and establish a role for fNIRS in visualization. Research suggests that the evaluation of visual design benefits by going beyond performance measures or questionnaires to measurements of the user’s cognitive state. Unfortunately, objectively and unobtrusively monitoring the brain is difficult. While functional near-infrared spectroscopy (fNIRS) has emerged as a practical brain sensing technology in HCI, visual tasks often rely on the brain’s quick, massively parallel visual system, which may be inaccessible to this measurement. It is unknown whether fNIRS can distinguish differences in cognitive state that derive from visual design alone. In this paper, we use the classic comparison of bar graphs and pie charts to test the viability of fNIRS for measuring the impact of a visual design on the brain. Our results demonstrate that we can indeed measure this impact, and furthermore measurements indicate that there are not universal differences in bar graphs and pie charts. Author Keywords fNIRS; BCI; visualization; brain sensing; evaluation.
Investigation of fNIRS Brain Sensing as Input to Information Filtering Systems
"... 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 spectro ..."
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Cited by 8 (5 self)
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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.
Brain-Based Target Expansion
"... The bubble cursor is a promising cursor expansion technique, improving a user’s movement time and accuracy in pointing tasks. We introduce a brain-based target expansion system, which improves the efficacy of bubble cursor by increasing the expansion of high importance targets at the optimal time ba ..."
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Cited by 5 (3 self)
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The bubble cursor is a promising cursor expansion technique, improving a user’s movement time and accuracy in pointing tasks. We introduce a brain-based target expansion system, which improves the efficacy of bubble cursor by increasing the expansion of high importance targets at the optimal time based on brain measurements correlated to a particular type of multitasking. We demonstrate through controlled experiments that brain-based target expansion can deliver a graded and continuous level of assistance to a user according to their cognitive state, thereby improving task and speed-accuracy metrics, even without explicit visual changes to the system. Such an adaptation is ideal for use in complex systems to steer users toward higher priority goals during times of increased demand. Author Keywords brain-computer interface; BCI; fNIRS; adaptive interface; bubble cursor.
Classifying Driver Workload Using Physiological and Driving Performance Data: Two Field Studies
"... Understanding the driver’s cognitive load is important for evaluating in-vehicle user interfaces. This paper describes experiments to assess machine learning classification algo-rithms on their ability to automatically identify elevated cognitive workload levels in drivers, leading towards the devel ..."
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Cited by 4 (0 self)
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Understanding the driver’s cognitive load is important for evaluating in-vehicle user interfaces. This paper describes experiments to assess machine learning classification algo-rithms on their ability to automatically identify elevated cognitive workload levels in drivers, leading towards the development of robust tools for automobile user interface evaluation. We look at using both driver performance as well as physiological data. These measures can be collected in real-time and do not interfere with the primary task of driving the vehicle. We report classification accuracies of up to 90 % for detecting elevated levels of cognitive load, and show that the inclusion of physiological data leads to higher classification accuracy than vehicle sensor data evaluated alone. Finally, we show results suggesting that models can be built to classify cognitive load across indi-viduals, instead of building individual models for each per-son. By collecting data from drivers in two large field stud-ies on the highway (20 drivers and 99 drivers), this work extends prior work and demonstrates feasibility and poten-tial of such measures for HCI research in vehicles. Author Keywords Cognitive workload; driving; physiological computing; heart rate; skin conductance; machine learning.
Functional Near-Infrared Spectroscopy for Adaptive Human Computer Interfaces
"... We present a brain-computer interface (BCI) that detects, analyzes and responds to user cognitive state in real-time using machine learning classifications of functional near-infrared spectroscopy (fNIRS) data. Our work is aimed at increasing the narrow communication bandwidth between the human and ..."
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Cited by 2 (1 self)
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We present a brain-computer interface (BCI) that detects, analyzes and responds to user cognitive state in real-time using machine learning classifications of functional near-infrared spectroscopy (fNIRS) data. Our work is aimed at increasing the narrow communication bandwidth between the human and computer by implicitly measuring users ’ cognitive state without any additional effort on the part of the user. Traditionally, BCIs have been designed to explicitly send signals as the primary input. However, such systems are usually designed for people with severe motor disabilities and are too slow and inaccurate for the general population. In this paper, we demonstrate with previous work1 that a BCI that implicitly measures cognitive workload can improve user performance and awareness compared to a control condition by adapting to user cognitive state in real-time. We also discuss some of the other applications we have used in this field to measure and respond to cognitive states such as cognitive workload, multitasking, and user preference.
ICD 3: Towards a 3-Dimensional Model of Individual Cognitive Differences
"... The effects of individual differences on user interaction is a topic that has been explored for the last 25 years in HCI. Recently, the importance of this subject has been carried into the field of information visualization and consequently, there has been a wide range of research conducted in this ..."
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Cited by 1 (1 self)
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The effects of individual differences on user interaction is a topic that has been explored for the last 25 years in HCI. Recently, the importance of this subject has been carried into the field of information visualization and consequently, there has been a wide range of research conducted in this area. However, there has been no consensus on which evaluation methods best answer the unique needs of information visualization. In this position paper we introduce the ICD 3 Model (Individual Cognitive Differences), whereby individual differences are evaluated in 3 dimensions: cognitive traits, cognitive states and experience/bias. Our proposed model systematically evaluates the effects of users ’ individual differences on information visualization and visual analytics, thereby responding to Yi’s [72] call for “creating a standardized measurement tool for individual differences”. In this position paper, we pursue this problem by introducing the ICD 3 Model (Individual Cognitive Differences)- a 3-dimensional model that encompasses the cognitive facets of individual indifferences. A necessary step in defining ICD 3 was to seek an underlying structure of previous research by identifying which factors are dependent and which are independent of one another. Doing so, we propose that individual differences can be categorized into three orthogonal dimensions: cognitive traits, cognitive states and experience/bias. 1.
The Adaptive User: Priming to Improve Interaction
, 2013
"... Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, ..."
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Cited by 1 (0 self)
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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.
Position paper: Towards a 3-dimensional model of individual cognitive differences
- In BELIV’12: Beyond
, 2012
"... The effects of individual differences on user interaction is a topic that has been explored for the last 25 years in HCI. Recently, the importance of this subject has been carried into the field of information visualization and consequently, there has been a wide range of research conducted in this ..."
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Cited by 1 (1 self)
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The effects of individual differences on user interaction is a topic that has been explored for the last 25 years in HCI. Recently, the importance of this subject has been carried into the field of information visualization and consequently, there has been a wide range of research conducted in this area. However, there has been no consensus on which evalu-ation methods best answer the unique needs of information visualization. In this position paper we propose that individ-ual differences are evaluated in three dominant dimensions: cognitive traits, cognitive states and experience/bias. We believe that this is a first step in systematically evaluating the effects of users ’ individual differences on information vi-sualization and visual analytics. 1.
Using Passive Input to Adapt Visualization Systems to the Individual
, 2013
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