Measuring Concentration While Programming with Low-Cost BCI Devices: Differences Between Debugging and Creativity Tasks

González, Víctor; Robbes, Romain; Góngora, Gabriela; Medina, Salvador

doi:10.1007/978-3-319-20816-9_58

Cited by 13 publications

(7 citation statements)

References 8 publications

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“…The decision-making module processes fuzzy information based on a set of linguistic rules contained in itheuristics, which are defined by the entrepreneur in the form of a language similar to the natural language of the type "If P, then C" (González et. al.…”

mentioning

confidence: 99%

Artificial intelligence components and fuzzy regulators in entrepreneurship development

Bogachov¹,

Kwiliński²,

Miethlich³

et al. 2020

JESI

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The article provides a comparative study of the possibility of entrepreneurship development based on fuzzy signals of business activity and applied elements of artificial intelligence. The principal research methods that determine the logic and practical basis of the application of fuzzy logic in entrepreneurship are highlighted. It has been determined that fuzzy modeling is effective when technological processes are too complex for analysis using generally accepted quantitative methods, or when available sources of information in the business environment are interpreted poorly, inaccurately, and indefinitely. It has been shown experimentally that fuzzy logic gives better results compared to those obtained with generally accepted algorithms for analyzing the quality of doing business. A model of a neurofuzzy regulator has been developed and measures for its implementation in the business environment have been proposed. A neural network model in entrepreneurial development has been formed. Studies have shown the possibility of effective use of the principles of artificial intelligence and modeling in solving problems of developing entrepreneurial potential and making business decisions under conditions of uncertainty. This ensures objective and well-grounded decision-making in solving various applied problems of business development and taking into account environmental factors. The applied tasks of supporting the adoption of entrepreneurial decisions in the conditions are formulated; uncertainty; indicating that approaches to decision-making under conditions of uncertainty based on artificial intelligence and fuzzy logic tools are universal and require appropriate careful study and adaptation to a specific applied problem in the business environment.

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mentioning

confidence: 99%

Artificial intelligence components and fuzzy regulators in entrepreneurship development

Bogachov¹,

Kwiliński²,

Miethlich³

et al. 2020

JESI

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“…), which are intended for consumer uses, has led to new research paradigms where comfortable, affordable, wireless, and easy-to-use at-home measurement devices collect neuroscientific data "in-the-wild" at a large scale and make it possible for the first time to measure brain responses from diverse audiences within their natural, real-world environment. Many of the wearable devices offer decoding outputs beyond the raw sensor data, and these "off-the-shelf" decoding outputs include attention, relaxation, and other states (Abiri et al, 2019;Bird et al, 2019;González et al, 2015;N.-H. Liu et al, 2013;Rebolledo-Mendez et al, 2009). It is important to note, however, that although decoder algorithms exist in the market for consumer uses, verifying their reliability to accurately capture attention, valence, arousal, stress and other attributes of human experience at a high temporal resolution and research quality has remained a challenge.…”

Section: Attention and Emotion Decoding From Brain Signalmentioning

confidence: 99%

Modeling The Effect of Background Sounds on Human Focus Using Brain Decoding Technology

Haruvi

Kopito

Brande-Eilat

et al. 2021

Preprint

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The goal of this study was to learn what properties of sound affect human focus the most. Participants (N=62, 18-65y) performed various tasks while listening to either no background sound (silence), popular music playlists for increasing focus (pre-recorded songs), or personalized soundscapes (audio composed in real-time to increase a specific individual's focus). While performing tasks on a tablet, participants wore headphones and brain signals were recorded using a portable electroencephalography headband. Participants completed four one-hour long sessions, each with different audio content, at home. We successfully generated brain-based models to predict individual participant focus levels over time and used these models to analyze the effects of various audio content during different tasks. We found that while participants were working, personalized soundscapes increased their focus significantly above silence (p=0.008), while music playlists did not have a significant effect. For the young adult demographic (18-36y), silence was significantly less effective at producing focus than audio content of any type tested (p=0.001-0.009). Personalized soundscapes enhanced focus the most relative to silence, but professionally crafted playlists of pre-recorded songs also increased focus during specific time intervals, especially for the youngest audience demographic. We also found that focus levels can be predicted from physical properties of sound, enabling human and artificial intelligence composers to test and refine audio to produce increases or decreases in listener focus with high temporal (millisecond) precision. Future research includes real-time adjustment of sound for other functional objectives, such as affecting listener enjoyment, calm, or memory.

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“…Steps toward replacing teachers with teacher-bots is no more a fiction, but a crossroad where we are standing at present [13,14]. As an example, a BCI device is capable of measuring the extent to which a student is following or staying focused on the learning tasks provided [15,16]. Gonzales et al [16] have used ANOVA to analyze EEG data obtained directly from subjects' brains through NeuroSky MindWave devices attached to their heads.…”

Section: Brain-computer Interface (Bci)mentioning

confidence: 99%

“…As an example, a BCI device is capable of measuring the extent to which a student is following or staying focused on the learning tasks provided [15,16]. Gonzales et al [16] have used ANOVA to analyze EEG data obtained directly from subjects' brains through NeuroSky MindWave devices attached to their heads. It is reported that the concentration patterns of creative, debugging and systematic programming tasks are quite different in time and intensity.…”

Section: Brain-computer Interface (Bci)mentioning

confidence: 99%

Artificial intelligence in education and assessment methods

et al. 2020

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Today, artificial intelligence has proliferated to reach almost every wing of daily life, perhaps one of the most sensitive of these being education. While teaching, insofar as it involves training human minds, is still mostly a form of art rather than a regular science, the taking up of this elitist job by computers has triggered much debate and controversy, involving the teaching community as much as the select corporate AI giants who strive to create computers capable of teaching better than humans. This paper surveys the most relevant studies carried out in this field to date. First, it introduces AI and describes the different AI applications in field of education and course assessment. It then goes on to list the most common topics in the educational context that have been resolved through AI and machine learning techniques, and finally, some of the most promising future lines of research are discussed.

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Measuring Concentration While Programming with Low-Cost BCI Devices: Differences Between Debugging and Creativity Tasks

Cited by 13 publications

References 8 publications

Artificial intelligence components and fuzzy regulators in entrepreneurship development

Artificial intelligence components and fuzzy regulators in entrepreneurship development

Modeling The Effect of Background Sounds on Human Focus Using Brain Decoding Technology

Artificial intelligence in education and assessment methods

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