Machine Learning Stop Signal Test (ML-SST): ML-based Mouse Tracking Enhances Adult ADHD Diagnosis

Leontyev, Anton; Yamauchi, Takashi; Razavi, Moein

doi:10.1109/aciiw.2019.8925073

Cited by 9 publications

(10 citation statements)

References 15 publications

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“…They also showed that the statistical results of analyzing EEG and GSR together were more reliable than analyzing them individually. Leontyev et al combined user response time and mouse movement features with machine learning technics and found an improvement in the accuracy of predicting attention-deficit/hyperactivity disorder (ADHD) [9][10][11]. Yamauchi et al combined behavioral measures and multiple mouse motion features to better predict people's emotions and cognitive conflict in computer tasks [12][13][14][15].…”

Section: Related Workmentioning

confidence: 99%

Multimodal-Multisensory Experiments

Razavi¹,

Yamauchi²,

Janfaza³

et al. 2020

Preprint

Self Cite

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The human mind is multimodal. Yet most behavioral studies rely on century-old measures of behavior—task accuracy and latency (response time). Multimodal and multisensory analysis of human behavior creates a better understanding of how the mind works. The problem is that designing and implementing these experiments is technically complex and costly. This paper introduces versatile and economical means of developing multimodal-multisensory human experiments. We provide an experimental design framework that automatically integrates and synchronizes measures including electroencephalogram (EEG), galvanic skin response (GSR), eye-tracking, virtual reality (VR), body movement, mouse/cursor motion and response time. Unlike proprietary systems (e.g., iMotions), our system is free and open-source; it integrates PsychoPy, Unity and Lab Streaming Layer (LSL). The system embeds LSL inside PsychoPy/Unity for the synchronization of multiple sensory signals—gaze motion, electroencephalogram (EEG), galvanic skin response (GSR), mouse/cursor movement, and body motion—with low-cost consumer-grade devices in a simple behavioral task designed by PsychoPy and a virtual reality environment designed by Unity. This tutorial shows a step-by-step process by which a complex multimodal-multisensory experiment can be designed and implemented in a few hours. When conducting the experiment, all of the data synchronization and recoding of the data to disk will be done automatically.

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Section: Related Workmentioning

confidence: 99%

Multimodal-Multisensory Experiments

Razavi¹,

Yamauchi²,

Janfaza³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Leontyev et al. combined user response time and mouse movement features with machine learning technics and found an improvement in the accuracy of predicting attention-deficit/hyperactivity disorder (ADHD) [9][10][11]. Yamauchi et al combined behavioral measures and multiple mouse motion features to better predict people's emotions and cognitive conflict in computer tasks [12,13].…”

Section: Related Workmentioning

confidence: 99%

Multimodal-Multisensory Experiments: Design and Implementation

Razavi

Yamauchi

Janfaza

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

The human mind is multimodal. Yet most behavioral studies rely on century-old measures of behavior - task accuracy and latency (response time). Multimodal and multisensory analysis of human behavior creates a better understanding of how the mind works. The problem is that designing and implementing these experiments is technically complex and costly. This paper introduces versatile and economical means of developing multimodal-multisensory human experiments. We provide an experimental design framework that automatically integrates and synchronizes measures including electroencephalogram (EEG), galvanic skin response (GSR), eye-tracking, virtual reality (VR), body movement, mouse/cursor motion and response time. Unlike proprietary systems (e.g., iMotions), our system is free and open-source; it integrates PsychoPy, Unity and Lab Streaming Layer (LSL). The system embeds LSL inside PsychoPy/Unity for the synchronization of multiple sensory signals - gaze motion, electroencephalogram (EEG), galvanic skin response (GSR), mouse/cursor movement, and body motion - with low-cost consumer-grade devices in a simple behavioral task designed by PsychoPy and a virtual reality environment designed by Unity. This tutorial shows a step-by-step process by which a complex multimodal-multisensory experiment can be designed and implemented in a few hours. When conducting the experiment, all of the data synchronization and recoding of the data to disk will be done automatically.

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“…Studies have also shown that mouse movement measures can capture semantic incongruency that is processed subliminally (Xiao & Yamauchi, 2014; they even allow automated recognition of emotion, gender and feelings of computer users (Yamauchi & Xiao, 2018;Yamauchi & Bowman, 2014), and are applicable for the assessment of psychopathology, such as attention-deficit/hyperactivity disorder (Leontyev, Sun, Wolfe, & Yamauchi, 2018;Leontyev, Yamauchi, & Razavi, 2019).…”

Section: Figure 1 Comparison Between Keypress (Upper Panel) and Mousmentioning

confidence: 99%

“…Stop-signal task requires individuals to react in some trials ("go" trials) and to inhibit the reaction in other trials ("stop" trials). Recent studies show , Leontyev et al, 2018, Leontyev, Yamauchi & Razavi, 2019) that mouse movement measures provide a stronger relationship with questionnaire-based metrics of impulsivity than conventional stop-signal task measures including SSRT. Among these measures are mean maximum velocity and acceleration of mouse movement.…”

Section: Figure 1 Comparison Between Keypress (Upper Panel) and Mousmentioning

confidence: 99%

Discerning Mouse Trajectory Features with Drift Diffusion Model

Leontyev¹,

Yamauchi²

2020

Preprint

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In decision-making tasks, participants are commonly instructed to respond by pressing a key. This design provides information about how fast and accurate an individual can respond but does not allow a researcher to directly study the process of response selection. Recently, mouse cursor tracking has been applied to offset this limitation. However, it is unclear whether RT/accuracy-based measures and mouse movement features (e.g. velocity) assess the same cognitive processes. To clarify the relationship between mouse movement features and cognitive processes, we developed mouse-tracking versions of the stop-signal and delay discounting tasks and investigated a) whether people respond similarly in the tasks with traditional design and tasks employing mouse cursor tracking; b) which features of the decision-making process mouse movement measures correspond with. Although participants responded similarly in tasks with traditional and mouse tracking design, only a few mouse movement features were related to the elements of the decision-making process.

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Machine Learning Stop Signal Test (ML-SST): ML-based Mouse Tracking Enhances Adult ADHD Diagnosis

Cited by 9 publications

References 15 publications

Multimodal-Multisensory Experiments

Multimodal-Multisensory Experiments

Multimodal-Multisensory Experiments: Design and Implementation

Discerning Mouse Trajectory Features with Drift Diffusion Model

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