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

Li, Y. and Li, X. and Ratcliffe, M. and Liu, L. and Qi, Y. and Liu, Q. (2011) A real-time EEG-based BCI system for attention recognition in ubiquitous environment. In: 2011 ACM Workshop on Ubiquitous Affective Awareness and Intelligent Interaction, UAAII'11, Co-located with UbiComp 2011, 18 September 2011 through 18 September 2011, Beijing; China.

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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. © 2011 ACM.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: attention, bci, distance learning, eeg, Accuracy rate, attention, Attention level, bci, Biological signals, Electro-oculogram, Electrodermal activity, Electromyogram, Human subjects, k-NN classifier, Mental tasks, Pervasive systems, Self-assessment, Skin temperatures, Time latency, Time performance, Ubiquitous environments, Brain computer interface, Distance education, Interfaces (computer), Electroencephalography
Subjects: G400 Computer Science
Divisions: UoA Collections > UoA11: Computer Science and Informatics
Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Hussen Farooq
Date Deposited: 20 Jul 2016 10:03
Last Modified: 20 Jul 2016 10:03
URI: http://www.open-access.bcu.ac.uk/id/eprint/2074

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