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Article: Designing a brain-computer interface device for neurofeedback using virtual environments

TitleDesigning a brain-computer interface device for neurofeedback using virtual environments
Authors
KeywordsAttention Deficit Hyperactivity Disorder (Adhd)
Brain-Computer Interface (Bci)
Electroencephalogram (Eeg)
Neurofeedback
Virtual Environments (Ve)
Issue Date2008
Citation
Journal Of Medical And Biological Engineering, 2008, v. 28 n. 3, p. 167-172 How to Cite?
AbstractFrom continuous feedback of electroencephalogram (EEG), people can learn how to change their brain electrical activity by a certain guideline. This technique is known as EEG biofeedback, or neurofeedback. It is a main application of brain-computer interface (BCI) systems in assistive technology, which has been widely used in research and clinical applications. However, there are two major limitations of current neurofeedback systems. One is that monotonous feedback methods cannot attract subjects to focus on them. The other one is that the area of EEG collection is limited in central areas. In response to these problems, a neurofeedback (NFB) system was established in this study, which utilized virtual reality (VR) to create appropriate feedback information in certain scenarios. This system collected three-channel EEG signals from frontal and central areas, and translated spontaneous EEG into "commands" signal which provided communication and control capabilities by virtual environment. This paper describes the system's configuration, hardware and software implementation and signal processing methodology. In addition, a pertinent experiment was performed with successful neurofeedback training sessions in order to test the feasibility and effectness of this system. Integrated visual and auditory-continuous performance test (IVA-CPT) results suggested that the attention of subjects had been strengthened after 20 training sessions. It showed that the NFB system could provide an effective therapy for treating children with attention deficit hyperactivity disorder (ADHD). Further research should be focused on mobile and wireless integration of our instrument, for providing mean more powerful and convenient application to clinical therapy.
Persistent Identifierhttp://hdl.handle.net/10722/175308
ISSN
2015 Impact Factor: 1.018
2015 SCImago Journal Rankings: 0.387
References

 

DC FieldValueLanguage
dc.contributor.authorYan, Nen_US
dc.contributor.authorWang, Jen_US
dc.contributor.authorLiu, Men_US
dc.contributor.authorZong, Len_US
dc.contributor.authorJiao, Yen_US
dc.contributor.authorYue, Jen_US
dc.contributor.authorLv, Yen_US
dc.contributor.authorYang, Qen_US
dc.contributor.authorLan, Hen_US
dc.contributor.authorLiu, Zen_US
dc.date.accessioned2012-11-26T08:58:05Z-
dc.date.available2012-11-26T08:58:05Z-
dc.date.issued2008en_US
dc.identifier.citationJournal Of Medical And Biological Engineering, 2008, v. 28 n. 3, p. 167-172en_US
dc.identifier.issn1609-0985en_US
dc.identifier.urihttp://hdl.handle.net/10722/175308-
dc.description.abstractFrom continuous feedback of electroencephalogram (EEG), people can learn how to change their brain electrical activity by a certain guideline. This technique is known as EEG biofeedback, or neurofeedback. It is a main application of brain-computer interface (BCI) systems in assistive technology, which has been widely used in research and clinical applications. However, there are two major limitations of current neurofeedback systems. One is that monotonous feedback methods cannot attract subjects to focus on them. The other one is that the area of EEG collection is limited in central areas. In response to these problems, a neurofeedback (NFB) system was established in this study, which utilized virtual reality (VR) to create appropriate feedback information in certain scenarios. This system collected three-channel EEG signals from frontal and central areas, and translated spontaneous EEG into "commands" signal which provided communication and control capabilities by virtual environment. This paper describes the system's configuration, hardware and software implementation and signal processing methodology. In addition, a pertinent experiment was performed with successful neurofeedback training sessions in order to test the feasibility and effectness of this system. Integrated visual and auditory-continuous performance test (IVA-CPT) results suggested that the attention of subjects had been strengthened after 20 training sessions. It showed that the NFB system could provide an effective therapy for treating children with attention deficit hyperactivity disorder (ADHD). Further research should be focused on mobile and wireless integration of our instrument, for providing mean more powerful and convenient application to clinical therapy.en_US
dc.languageengen_US
dc.relation.ispartofJournal of Medical and Biological Engineeringen_US
dc.subjectAttention Deficit Hyperactivity Disorder (Adhd)en_US
dc.subjectBrain-Computer Interface (Bci)en_US
dc.subjectElectroencephalogram (Eeg)en_US
dc.subjectNeurofeedbacken_US
dc.subjectVirtual Environments (Ve)en_US
dc.titleDesigning a brain-computer interface device for neurofeedback using virtual environmentsen_US
dc.typeArticleen_US
dc.identifier.emailYan, N: nyan@hku.hken_US
dc.identifier.authorityYan, N=rp00978en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-57049185820en_US
dc.identifier.hkuros183214-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-57049185820&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume28en_US
dc.identifier.issue3en_US
dc.identifier.spage167en_US
dc.identifier.epage172en_US
dc.publisher.placeTaiwan, Republic of Chinaen_US
dc.identifier.scopusauthoridYan, N=7102919410en_US
dc.identifier.scopusauthoridWang, J=15066366300en_US
dc.identifier.scopusauthoridLiu, M=22835742800en_US
dc.identifier.scopusauthoridZong, L=36767777800en_US
dc.identifier.scopusauthoridJiao, Y=25723427000en_US
dc.identifier.scopusauthoridYue, J=24178443800en_US
dc.identifier.scopusauthoridLv, Y=36900281200en_US
dc.identifier.scopusauthoridYang, Q=53870934800en_US
dc.identifier.scopusauthoridLan, H=36779172600en_US
dc.identifier.scopusauthoridLiu, Z=25723519600en_US

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