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Article: Designing a brain-computer interface device for neurofeedback using virtual environments
Title | Designing a brain-computer interface device for neurofeedback using virtual environments |
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Authors | |
Keywords | Attention Deficit Hyperactivity Disorder (Adhd) Brain-Computer Interface (Bci) Electroencephalogram (Eeg) Neurofeedback Virtual Environments (Ve) |
Issue Date | 2008 |
Citation | Journal Of Medical And Biological Engineering, 2008, v. 28 n. 3, p. 167-172 How to Cite? |
Abstract | From 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 Identifier | http://hdl.handle.net/10722/175308 |
ISSN | 2023 Impact Factor: 1.6 2023 SCImago Journal Rankings: 0.421 |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yan, N | en_US |
dc.contributor.author | Wang, J | en_US |
dc.contributor.author | Liu, M | en_US |
dc.contributor.author | Zong, L | en_US |
dc.contributor.author | Jiao, Y | en_US |
dc.contributor.author | Yue, J | en_US |
dc.contributor.author | Lv, Y | en_US |
dc.contributor.author | Yang, Q | en_US |
dc.contributor.author | Lan, H | en_US |
dc.contributor.author | Liu, Z | en_US |
dc.date.accessioned | 2012-11-26T08:58:05Z | - |
dc.date.available | 2012-11-26T08:58:05Z | - |
dc.date.issued | 2008 | en_US |
dc.identifier.citation | Journal Of Medical And Biological Engineering, 2008, v. 28 n. 3, p. 167-172 | en_US |
dc.identifier.issn | 1609-0985 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/175308 | - |
dc.description.abstract | From 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.language | eng | en_US |
dc.relation.ispartof | Journal of Medical and Biological Engineering | en_US |
dc.subject | Attention Deficit Hyperactivity Disorder (Adhd) | en_US |
dc.subject | Brain-Computer Interface (Bci) | en_US |
dc.subject | Electroencephalogram (Eeg) | en_US |
dc.subject | Neurofeedback | en_US |
dc.subject | Virtual Environments (Ve) | en_US |
dc.title | Designing a brain-computer interface device for neurofeedback using virtual environments | en_US |
dc.type | Article | en_US |
dc.identifier.email | Yan, N: nyan@hku.hk | en_US |
dc.identifier.authority | Yan, N=rp00978 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.scopus | eid_2-s2.0-57049185820 | en_US |
dc.identifier.hkuros | 183214 | - |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-57049185820&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 28 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.spage | 167 | en_US |
dc.identifier.epage | 172 | en_US |
dc.publisher.place | Taiwan, Republic of China | en_US |
dc.identifier.scopusauthorid | Yan, N=7102919410 | en_US |
dc.identifier.scopusauthorid | Wang, J=15066366300 | en_US |
dc.identifier.scopusauthorid | Liu, M=22835742800 | en_US |
dc.identifier.scopusauthorid | Zong, L=36767777800 | en_US |
dc.identifier.scopusauthorid | Jiao, Y=25723427000 | en_US |
dc.identifier.scopusauthorid | Yue, J=24178443800 | en_US |
dc.identifier.scopusauthorid | Lv, Y=36900281200 | en_US |
dc.identifier.scopusauthorid | Yang, Q=53870934800 | en_US |
dc.identifier.scopusauthorid | Lan, H=36779172600 | en_US |
dc.identifier.scopusauthorid | Liu, Z=25723519600 | en_US |
dc.identifier.issnl | 1609-0985 | - |