Adaptive figurai abstraction test with generated exercises

Radostyan, Bertalan; Forstner, Bertalan; Szegletes, Luca; Pomazi, Krisztian; Gazdi, Laszlo

doi:10.1109/coginfocom.2016.7804570

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CogInfoCom covers several disciplines appearing in applications and research areas also. CogInfoCom is available technology from socio-cognitive ICT [55][56][57][58][59][60][61][62][63][64][65] to cognitive aided engineering [66][67][68][69][70][71][72][73][74][75][76][77] and its related aspects in terms of online collaborative systems and virtual reality solutions [78][79][80], teaching-learning [81][82][83][84][85][86] and human cognitive interfaces such as braincomputer interfaces (BCIs) [87][88][89][90][91][92] and medicals [93][94][95][96].…”

Section: Human-computer Interfaces and Cognitive Infocommunicationmentioning

confidence: 99%

Examining the Learning Efficiency by a Brain-Computer Interface System

2018

APH

View full text Add to dashboard Cite

Brain research is one of the most significant research areas of the last decades, in which many developments and modern engineering technologies are applied. The electroencephalogram (EEG)-based brain activity observation processes are very promising and have been used in several engineering research fields. Objective: The main goal of this research was to develop a Brain-Computer Interface (BCI) system to observe the level of vigilance calculated by Think Gear-ASIC Module (TGAM1) technology and to evaluate the output with learning efficiency tests applied in cognitive neuroscience. Methods: The performance of the BCI system is evaluated in a comparative study. The BCI system was tested by thirty-two test subjects and the attention level output was compared by the Psychology Experiment Building Language's (PEBL's) Corsi block test (P CORSI) and PEBL's Ebbinghaus procedure (P EBBINGHAUS) tasks. Results: Using the BCI, we have shown statistically significant results between the BCI and the conventional cognitive neuroscience tests. The correlation between the tests and the average attention of the BCI was slightly strong for P CORSI Total Score (r=.63, p<.01 (2-tailed) and slightly strong for P EBBINGHAUS Total Correct (r=-.71, p<.01 (2-tailed). The average level of attention measured by the BCI system during the P CORSI test was 49.00%, while in case of the P EBBINGHAUS test it was 52.41% on all samples. Conclusion: The developed BCI system has a significant correlation with P CORSI and P EBBINGHAUS cognitive neuroscience tests. The BCI system is capable of observing attentional vigilance continuously. Significance: The developed BCI system is applicable to observe vigilance level in realtime while the level of attention depends on activities.

show abstract

Section: Human-computer Interfaces and Cognitive Infocommunicationmentioning

confidence: 99%

Examining the Learning Efficiency by a Brain-Computer Interface System

2018

APH

View full text Add to dashboard Cite

show abstract

Adaptive figurai abstraction test with generated exercises

Cited by 1 publication

References 9 publications

Examining the Learning Efficiency by a Brain-Computer Interface System

Examining the Learning Efficiency by a Brain-Computer Interface System

Contact Info

Product

Resources

About