The prediction of attentional status with task difficulty based on EEG signals

Zhong, Tianchen; Li, Xinhe; Tu, Xiaoguang; Zhao, Su; Zhou, Shaoqing

doi:10.1109/compcomm.2016.7924808

Cited by 3 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Six studies attempted to identify real-time attention with machine learning algorithms, including a support vector machine (SVM) classifier (Liu et al, 2013;Rebolledo-Méndez et al, 2010;Zhong et al, 2016), a boosting and bagging of decision tree classifier (Qu et al, 2019), the K-means classifier (Qu et al, 2018), and a Logistic Regression Classifier and Multi-task Learning Classifier (Liu et al, 2017). Unlike the standard classification approaches mentioned above, Shaw et al (2022) utilized an unsupervised learning approach to obtaining handcrafted EEG for capturing attention.…”

Section: Algorithm and Software Development For Attention Monitoring ...mentioning

confidence: 99%

Portable EEG for assessing attention in educational settings: A scoping review

Wang¹,

Zhang²,

Johnstone³

2023

Preprint

View full text Add to dashboard Cite

Background: Attention plays a critical role in learning. While multiple behavioral and neuroimaging methods can measure attention, portable EEG provides the opportunity to measure attention in a more naturalistic environment. However, the field is still in its infancy, with varied research aims and methodologies. The current scoping review aims to clarify: (1) the research aims of the studies, (2) the portable EEG collection methodologies, and (3) the EEG measures of attention. Method: The review followed the Preferred Reporting Items for Systematic Review and Meta-Analysis - Scoping Review extension (PRISMA-ScR). Two authors extracted data items from 45 eligible studies selected from five databases. Results: Three research aims were identified in previous studies: examining the effects of learning-related factors on attention measured by portable EEG (n = 23), developing attention classification algorithms (n = 7) and software for monitoring and promoting attention (n = 10), and verifying the signal quality of EEG derived from portable EEG in attentional tasks (n = 5). Most studies sampled fewer than 40 college students and recorded EEG no longer than 40 minutes with portable EEG devices developed by Neurosky. The testing sites and tasks were predominantly out-of-lab controlled settings and structured learning materials. EEG measures were selected by theory-based (n = 8) or data-driven approaches (n = 37), with most studies quantifying task-related attention with the spectrum power in multiple EEG bands. Discussion: Portable EEG has been a promising approach to measuring attention in educational settings. Meanwhile, there are challenges and opportunities related to the better translation of cognitive neuroscience research into practice.

show abstract

Section: Algorithm and Software Development For Attention Monitoring ...mentioning

confidence: 99%

Portable EEG for assessing attention in educational settings: A scoping review

Wang¹,

Zhang²,

Johnstone³

2023

Preprint

View full text Add to dashboard Cite

show abstract

Arduino based mobile robot controlled by voluntary eye-blinks using LabVIEW GUI & NeuroSky Mindwave Mobile Headset

Rușanu

Cristea

Luculescu

2020

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

This paper proposes an experimental system of a brain-computer interface aimed to use the voluntary eye-blinks detected across the EEG signal acquired from a NeuroSky headset to control the movement directions of a mobile robot. Bluetooth protocol was used to achieve the communication between a LabVIEW application and a program uploaded in an Arduino Mega board which controls the movement direction of a mobile robot. Three parallel algorithms were implemented using LabVIEW, one for the voluntary eye-blinks counting, one for switching across buttons and one for selection of a certain command. Transition across commands (stop, go forward, go backward, turn left, turn right) is executed by one voluntary eye-blink, while two intentional eye-blinks determine the selection of a specific command.

show abstract