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A real-time EEG-based BCI system for attention recognition in ubiquitous environment

Authors:

Martyn Ratcliffe,

Quanying LiuAuthors Info & Claims

UAAII '11: Proceedings of 2011 international workshop on Ubiquitous affective awareness and intelligent interaction

Pages 33 - 40

https://doi.org/10.1145/2030092.2030099

Published: 18 September 2011 Publication History

Abstract

Several types of biological signal, such as Electroencephalogram (EEG), electrooculogram(EOG), electrocardiogram(ECG), electromyogram (EMG), skin temperature variation and electrodermal activity, may be used to measure a human subject's attention level. Generally electroencephalogram (EEG) is considered the most effective and objective indicator of attention level. However, few systems based on EEG have actually been developed to measure attention levels. In this paper we describe a pervasive system, based on an electroencephalogram (EEG) Brain-Computer Interface, which measures attention level. After demonstrating the effectiveness of our system we then go on to compare our approach with traditional approaches. In our study, three attention levels were classified by a KNN classifier based on the Self-Assessment Manikin (SAM) model. In our experiment, subjects were given several mental tasks to undertake and asked to report on their attention level during the tasks using a set of attention classifications. The average accuracy rate is shown to reach 57.03% after seven sessions' EEG training. Moreover, our system works in real-time while maintaining this accuracy. This is demonstrated by our time performance evaluation results which show that the time latency is short enough for our system to recognize attention in real-time.

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Cited By

Li YFang HLiu WCheng CChen H(2024)Minimal Electrode EEG for BCI Emotion Detection2024 4th International Conference on Neural Networks, Information and Communication (NNICE)10.1109/NNICE61279.2024.10499167(379-383)Online publication date: 19-Jan-2024
https://doi.org/10.1109/NNICE61279.2024.10499167
Daza RGomez LFierrez JMorales ATolosana ROrtega-Garcia J(2024)DeepFace-Attention: Multimodal Face Biometrics for Attention Estimation With Application to e-LearningIEEE Access10.1109/ACCESS.2024.343729112(111343-111359)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3437291
Nair SMegalingam R(2024)Human attention detection system using deep learning and brain–computer interfaceNeural Computing and Applications10.1007/s00521-024-09628-836:18(10927-10940)Online publication date: 28-Mar-2024
https://doi.org/10.1007/s00521-024-09628-8
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Index Terms

A real-time EEG-based BCI system for attention recognition in ubiquitous environment
1. Human-centered computing

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Published In

cover image ACM Conferences

UAAII '11: Proceedings of 2011 international workshop on Ubiquitous affective awareness and intelligent interaction

September 2011

46 pages

ISBN:9781450309325

DOI:10.1145/2030092

General Chairs:
Bin Hu
Birmingham City University, UK
,
Jürg Gutknecht
Swiss Federal Institute of Technology Zurich, Switzerland
,
Program Chair:
Li Liu
Lanzhou University, China

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 18 September 2011

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Ubicomp '11

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Ubicomp '11: The 2011 ACM Conference on Ubiquitous Computing

September 18, 2011

Beijing, China

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Cited By

Li YFang HLiu WCheng CChen H(2024)Minimal Electrode EEG for BCI Emotion Detection2024 4th International Conference on Neural Networks, Information and Communication (NNICE)10.1109/NNICE61279.2024.10499167(379-383)Online publication date: 19-Jan-2024
https://doi.org/10.1109/NNICE61279.2024.10499167
Daza RGomez LFierrez JMorales ATolosana ROrtega-Garcia J(2024)DeepFace-Attention: Multimodal Face Biometrics for Attention Estimation With Application to e-LearningIEEE Access10.1109/ACCESS.2024.343729112(111343-111359)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3437291
Nair SMegalingam R(2024)Human attention detection system using deep learning and brain–computer interfaceNeural Computing and Applications10.1007/s00521-024-09628-836:18(10927-10940)Online publication date: 28-Mar-2024
https://doi.org/10.1007/s00521-024-09628-8
Wu QChen YSun YPan J(2024)CapsDA-Net: A Convolutional Capsule Domain-Adversarial Neural Network for EEG-Based Attention RecognitionArtificial Neural Networks and Machine Learning – ICANN 202410.1007/978-3-031-72353-7_2(15-28)Online publication date: 17-Sep-2024
https://doi.org/10.1007/978-3-031-72353-7_2
Khare SBajaj VGaikwad NSinha G(2023)Ensemble Wavelet Decomposition-Based Detection of Mental States Using Electroencephalography SignalsSensors10.3390/s2318786023:18(7860)Online publication date: 13-Sep-2023
https://doi.org/10.3390/s23187860
Chen DHuang HBao XPan JLi Y(2023)An EEG-based attention recognition method: fusion of time domain, frequency domain, and non-linear dynamics featuresFrontiers in Neuroscience10.3389/fnins.2023.119455417Online publication date: 12-Jul-2023
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Zhang YZheng XXu WLiu H(2023)RT-Blink: A Method Toward Real-Time Blink Detection From Single Frontal EEG SignalIEEE Sensors Journal10.1109/JSEN.2022.323217623:3(2794-2802)Online publication date: 1-Feb-2023
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Sadras NSani OAhmadipour PShanechi M(2023)Post-stimulus encoding of decision confidence in EEG: toward a brain–computer interface for decision makingJournal of Neural Engineering10.1088/1741-2552/acec1420:5(056012)Online publication date: 19-Sep-2023
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Jaipriya DSriharipriya K(2023)Brain Computer Interface-Based Signal Processing Techniques for Feature Extraction and Classification of Motor Imagery Using EEG: A Literature ReviewBiomedical Materials & Devices10.1007/s44174-023-00082-z2:2(601-613)Online publication date: 12-May-2023
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Ghaderi MBen Abdessalem HAntoine MFrasson C(2023)Estimation of Piloting Attention Level Based on the Correlation of Pupil Dilation and EEGAugmented Intelligence and Intelligent Tutoring Systems10.1007/978-3-031-32883-1_35(381-390)Online publication date: 22-May-2023
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