A semi-supervised inattention detection method using biological signal

Choi, Ye Rim; Park, Jonghun; Shin, Dongmin

doi:10.1007/s10479-017-2406-6

Cited by 4 publications

(2 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be attractive from the cognitive and behavioral point of view to conduct experiments focused on determining relations between visual strategies and mechanisms of making decisions about similarities modeled by neuromatrices introduced in this paper. Analogous studies may also be conducted by other tools used in neuroscience such as electroencephalography [114,115], magnetoencephalography, and facial recognition. Reviews and discussion on the usefulness of various neuroscience methods can be found, for example, in [116,117].…”

Section: Validation Studiesmentioning

confidence: 99%

Modeling human thinking about similarities by neuromatrices in the perspective of fuzzy logic

Grobelny

Michalski

Weber

2020

Neural Comput & Applic

View full text Add to dashboard Cite

In this work, we propose a new method for modeling human reasoning about objects’ similarities. We assume that similarity depends on perceived intensities of objects’ attributes expressed by natural language expressions such as low, medium, and high. We show how to find the underlying structure of the matrix with intensities of objects’ similarities in the factor-analysis-like manner. The demonstrated approach is based on fuzzy logic and set theory principles, and it uses only maximum and minimum operators. Similarly to classic eigenvector decomposition, we aim at representing the initial linguistic ordinal-scale (LOS) matrix as a max–min product of other LOS matrix and its transpose. We call this reconstructing matrix a neuromatrix because we assume that such a process takes place at the neural level in our brain. We show and discuss on simple, illustrative examples, how the presented way of modeling grasps natural way of reasoning about similarities. The unique characteristics of our approach are treating smaller attribute intensities as less important in making decisions about similarities. This feature is consistent with how the human brain is functioning at a biological level. A neuron fires and passes information further only if input signals are strong enough. The proposal of the heuristic algorithm for finding the decomposition in practice is also introduced and applied to exemplary data from classic psychological studies on perceived similarities between colors and between nations. Finally, we perform a series of simulation experiments showing the effectiveness of the proposed heuristic.

show abstract

Section: Validation Studiesmentioning

confidence: 99%

Modeling human thinking about similarities by neuromatrices in the perspective of fuzzy logic

Grobelny

Michalski

Weber

2020

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…EEG-signal is known to be rapidly affected by attention fluctuations [ 16 ], while it can be acquired in a non-invasive manner. This suggests that the proposed system is able to detect inattention and alert UAV operators with minimal disturbance on the operators.…”

Section: Introductionmentioning

confidence: 99%

A BCI Based Alerting System for Attention Recovery of UAV Operators

Park

Shin

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

As unmanned aerial vehicles have become popular, the number of accidents caused by an operator’s inattention have increased. To prevent such accidents, the operator should maintain an attention status. However, limited research has been conducted on the brain-computer interface (BCI)-based system with an alerting module for the operator’s attention recovery of unmanned aerial vehicles. Therefore, we introduce a detection and alerting system that prevents an unmanned aerial vehicle operator from falling into inattention status by using the operator’s electroencephalogram signal. The proposed system consists of the following three components: a signal processing module, which collects and preprocesses an electroencephalogram signal of an operator, an inattention detection module, which determines whether an inattention status occurred based on the preprocessed signal, and, lastly, an alert providing module that presents stimulus to an operator when inattention is detected. As a result of evaluating the performance with a real-world dataset, it was shown that the proposed system successfully contributed to the recovery of operator attention in the evaluating dataset, although statistical significance could not be established due to the small number of subjects.

show abstract