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Published in: Radiological Physics and Technology 2/2019

01-06-2019 | Electroencephalography

Development of a new image manipulation system based on detection of electroencephalogram signals from the operator’s brain: a feasibility study

Authors: Mitsuru Sato, Toshihiro Ogura, Sakuya Yamanouchi, Yasuaki Osaki, Kunio Doi

Published in: Radiological Physics and Technology | Issue 2/2019

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Abstract

Physicians require an adequate display system with a console within arm’s reach to view images during surgical operations and interventional radiological examinations. However, manipulation of the console by physicians themselves may not be possible because their hands may be otherwise engaged. In this study, an image manipulation system using an electroencephalogram (EEG) sensor mounted on the operator’s head was developed. In this system, data acquired by the device is used to manipulate images, and the output can be converted to commands for various actions such as paging, which can be controlled by the operator’s eye-blink, and zooming of a region indicated by the cursor, which can be controlled by the operator’s mental concentration. In this study, the MindWave Mobile headset was used as EEG sensor, and AZEWIN for the display system. Ten observers were enrolled and fitted with EEG device to determine the threshold values of blink strength and attention; threshold value of 100 for blink strength and 65 for attention were determined. Thirty-one observers were enrolled and fitted with EEG device to investigate average response-time; the average response time for detecting paging was 0.43 ± 0.02 s, and that for zooming was 5.85 ± 0.56 s. Thus, the proposed image manipulation system using the operator’s EEG signals enabled physicians to assess and manipulate images without using their hands.
Literature
1.
go back to reference Ogura T, Sato M, Ishida Y, Hayashi N, Doi K. Development of a novel method for manipulation of angiographic images by use of a motion sensor in operating rooms. Radiol Phys Technol. 2014;7(2):228–34.CrossRefPubMed Ogura T, Sato M, Ishida Y, Hayashi N, Doi K. Development of a novel method for manipulation of angiographic images by use of a motion sensor in operating rooms. Radiol Phys Technol. 2014;7(2):228–34.CrossRefPubMed
2.
go back to reference Sato M, Ogura T, Yasumoto. Y, Kadowaki Y, Hayashi N, Doi K. Development of an image operation system with a motion sensor in dental radiology. Radiol Phys Technol. 2015;8(2):243–7.CrossRefPubMed Sato M, Ogura T, Yasumoto. Y, Kadowaki Y, Hayashi N, Doi K. Development of an image operation system with a motion sensor in dental radiology. Radiol Phys Technol. 2015;8(2):243–7.CrossRefPubMed
4.
go back to reference Sałabun W. Processing and spectral analysis of the raw EEG signal from the MindWave. Przegląd Elektrotechniczny. 2014;90(2):169–73. Sałabun W. Processing and spectral analysis of the raw EEG signal from the MindWave. Przegląd Elektrotechniczny. 2014;90(2):169–73.
5.
go back to reference Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev. 1999;29:169–95.CrossRefPubMed Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev. 1999;29:169–95.CrossRefPubMed
6.
go back to reference Steriade M. Thalamocortical oscillations in the sleeping and aroused brain. Science. 1993;262(5134):679–85.CrossRef Steriade M. Thalamocortical oscillations in the sleeping and aroused brain. Science. 1993;262(5134):679–85.CrossRef
7.
go back to reference Sederberg PB. Theta and gamma oscillations during encoding predict subsequent recall. J Neurosci. 2003;23(34):10809–14.CrossRefPubMed Sederberg PB. Theta and gamma oscillations during encoding predict subsequent recall. J Neurosci. 2003;23(34):10809–14.CrossRefPubMed
8.
go back to reference Adrian ED, Matthews BHC. The berger rhythm: potential changes from the occipital lobes in man. Brain. 1934;57(4):355–85.CrossRef Adrian ED, Matthews BHC. The berger rhythm: potential changes from the occipital lobes in man. Brain. 1934;57(4):355–85.CrossRef
9.
go back to reference Yoshida K. Evaluation of the change of work using simple electroencephalography. Proc Comput Sci. 2013;22:855–62.CrossRef Yoshida K. Evaluation of the change of work using simple electroencephalography. Proc Comput Sci. 2013;22:855–62.CrossRef
10.
go back to reference Akhil PS. Brain computer interface for wheelchair movement using blink detection. Int Res J Eng Technol. 2018;5(4):4980–3. Akhil PS. Brain computer interface for wheelchair movement using blink detection. Int Res J Eng Technol. 2018;5(4):4980–3.
11.
go back to reference Navalyal GU. A dynamic attention assessment and enhancement tool using computer graphics. Hum Centric Comput Inf Sci. 2014;4(11):1–7. Navalyal GU. A dynamic attention assessment and enhancement tool using computer graphics. Hum Centric Comput Inf Sci. 2014;4(11):1–7.
12.
go back to reference Sezer A. An Investigation of University Students’ attention levels in real classroom settings with NeuroSky’s MindWave Mobile (EEG) Device. In: International educational technology conference, pp. 88–101; 2015. Sezer A. An Investigation of University Students’ attention levels in real classroom settings with NeuroSky’s MindWave Mobile (EEG) Device. In: International educational technology conference, pp. 88–101; 2015.
13.
go back to reference Liu NH. Recognizing the degree of human attention using EEG signals from mobile sensors. Sensors. 2013;13:10273–86.CrossRefPubMed Liu NH. Recognizing the degree of human attention using EEG signals from mobile sensors. Sensors. 2013;13:10273–86.CrossRefPubMed
14.
go back to reference Yamauchi T. Dynamic time warping: a single dry electrode EEG study in a self-paced learning task. In: International conference on affective computing and intelligent interaction, pp. 56–62; 2015. Yamauchi T. Dynamic time warping: a single dry electrode EEG study in a self-paced learning task. In: International conference on affective computing and intelligent interaction, pp. 56–62; 2015.
15.
go back to reference Lim CG. A brain–computer interface based attention training program for treating attention deficit hyperactivity disorder. PLoS One. 2012;7(10):1–8.CrossRef Lim CG. A brain–computer interface based attention training program for treating attention deficit hyperactivity disorder. PLoS One. 2012;7(10):1–8.CrossRef
16.
go back to reference Twigg P. Exploration of the effect of electroencephalograph levels in experienced archers. Meas Control. 2014;47(6):185–90.CrossRef Twigg P. Exploration of the effect of electroencephalograph levels in experienced archers. Meas Control. 2014;47(6):185–90.CrossRef
17.
go back to reference Maria CC, Julieta R, et al. Gender differences in the EEG During cognitive activity. Int J Neurosci. 1993;72:257–64.CrossRef Maria CC, Julieta R, et al. Gender differences in the EEG During cognitive activity. Int J Neurosci. 1993;72:257–64.CrossRef
19.
go back to reference Jacob RJK. The use of eye movements in human–computer interaction techniques: what you look at is what you get. ACM Trans Inf Syst. 1991;9(3):152–69.CrossRef Jacob RJK. The use of eye movements in human–computer interaction techniques: what you look at is what you get. ACM Trans Inf Syst. 1991;9(3):152–69.CrossRef
Metadata
Title
Development of a new image manipulation system based on detection of electroencephalogram signals from the operator’s brain: a feasibility study
Authors
Mitsuru Sato
Toshihiro Ogura
Sakuya Yamanouchi
Yasuaki Osaki
Kunio Doi
Publication date
01-06-2019
Publisher
Springer Singapore
Published in
Radiological Physics and Technology / Issue 2/2019
Print ISSN: 1865-0333
Electronic ISSN: 1865-0341
DOI
https://doi.org/10.1007/s12194-019-00508-8

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