Toward unobtrusive measurement of reading comprehension using low-cost EEG

Yuan, Yueran; Chang, Kai-Min; Taylor, Jessica; Mostow, Jack

doi:10.1145/2567574.2567624

Cited by 13 publications

(4 citation statements)

References 5 publications

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“…Electroencephalograms (EEG) have been shown to reflect workload levels and sustained attention during training and learning. However, a limited number of studies have examined their performance in operational settings (Bernhardt et al, 2019;Mathan & Yeung, 2015;Mills et al, 2017;Yuan et al, 2014).…”

Section: Collegiate Aviation Review Internationalmentioning

confidence: 99%

Untitled

2021

CARI

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The University Aviation Association publishes the Collegiate Aviation Review International throughout each calendar year. Papers published in each volume and issue are selected from submissions that were subjected to a double-blind peer review process.The University Aviation Association is the only professional organization representing all levels of the non-engineering/technology element in collegiate aviation education and research. Working through its officers, trustees, committees, and professional staff, the University Aviation Association plays a vital role in collegiate aviation and in the aerospace industry. The University Aviation Association accomplishes its goals through a number of objectives:

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Section: Collegiate Aviation Review Internationalmentioning

confidence: 99%

Untitled

2021

CARI

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“…Tactile tasks’ physical movements result in sense of touch, such as mouse clicks, keyboard strokes, and carrying a backpack (e.g., Hsiao et al, 2015 ; Lee and Nicholls, 1999 ). Cognitive tasks use the brain to process new information, as well as recall or retrieve information from memory (e.g., Kunze et al, 2013a ; Yuan et al, 2014 ; Salehzadeh et al, 2020 ). Visual task examples include identifying different objects, and reading (e.g., Bulling et al, 2010 ; Ishimaru et al, 2017 ; Srivastava et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Multi-dimensional task recognition for human-robot teaming: literature review

Baskaran,

Adams

2023

Front. Robot. AI

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Human-robot teams collaborating to achieve tasks under various conditions, especially in unstructured, dynamic environments will require robots to adapt autonomously to a human teammate’s state. An important element of such adaptation is the robot’s ability to infer the human teammate’s tasks. Environmentally embedded sensors (e.g., motion capture and cameras) are infeasible in such environments for task recognition, but wearable sensors are a viable task recognition alternative. Human-robot teams will perform a wide variety of composite and atomic tasks, involving multiple activity components (i.e., gross motor, fine-grained motor, tactile, visual, cognitive, speech and auditory) that may occur concurrently. A robot’s ability to recognize the human’s composite, concurrent tasks is a key requirement for realizing successful teaming. Over a hundred task recognition algorithms across multiple activity components are evaluated based on six criteria: sensitivity, suitability, generalizability, composite factor, concurrency and anomaly awareness. The majority of the reviewed task recognition algorithms are not viable for human-robot teams in unstructured, dynamic environments, as they only detect tasks from a subset of activity components, incorporate non-wearable sensors, and rarely detect composite, concurrent tasks across multiple activity components.

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“…However, most current research involving emerging neurophysiological technology in classroom settings do not engage students directly with topics related to neuroscience. Instead, neurophysiological data is often collected from students to augment learning experiences (Antle et al 2015;Huang et al 2014a;Szafir and Mutlu 2012) or measure performance (Yuan et al 2014). While this approach is useful, there is a critical gap in the knowledge base regarding approaches that teach students new forms of digital literacies involving neurophysiological technology.…”

Section: Introductionmentioning

confidence: 99%

Brains and Blocks

Crawford

Gilbert

2019

ACM Trans. Comput. Educ.

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Brain-Computer Interface (BCI) hardware is becoming more affordable and accessible. However, there is limited work investigating ways to design software that broadens participation with BCI technology. In this article, we present a block-based programming environment designed to assist novice programmers with creating BCI applications. We also discuss learning barriers encountered by novice programmers developing neurofeedback applications. Our findings suggest that visual programming assists novice programmers with building basic BCI applications; however, students may experience understanding and learning barriers initially. CCS Concepts: • Social and professional topics → Computing education; • Human-centered computing → Interactive systems and tools;

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Toward unobtrusive measurement of reading comprehension using low-cost EEG

Cited by 13 publications

References 5 publications

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Multi-dimensional task recognition for human-robot teaming: literature review

Brains and Blocks

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