Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field
•Distinguishable high-frequency SSVEP was elicited from hairless areas using a Depth-of-Field setup.•SSVEP measured from below-the-hairline areas could be modulated by eye focusing mechanism.•BCI proposed achieved effective communication with binary choice.•BCI proposed uses comfortable stimuli (hig...
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Published in | Computer methods and programs in biomedicine Vol. 184; p. 105271 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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Elsevier B.V
01.02.2020
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ISSN | 0169-2607 1872-7565 1872-7565 |
DOI | 10.1016/j.cmpb.2019.105271 |
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Abstract | •Distinguishable high-frequency SSVEP was elicited from hairless areas using a Depth-of-Field setup.•SSVEP measured from below-the-hairline areas could be modulated by eye focusing mechanism.•BCI proposed achieved effective communication with binary choice.•BCI proposed uses comfortable stimuli (high-frequency range), practical electrodes placement and does not require a calibration phase by users.
Recently, a promising Brain-Computer Interface based on Steady-State Visual Evoked Potential (SSVEP-BCI) was proposed, which composed of two stimuli presented together in the center of the subject’s field of view, but at different depth planes (Depth-of-Field setup). Thus, users were easily able to select one of them by shifting their eye focus. However, in that work, EEG signals were collected through electrodes placed on occipital and parietal regions (hair-covered areas), which demanded a long preparation time. Also, that work used low-frequency stimuli, which can produce visual fatigue and increase the risk of photosensitive epileptic seizures. In order to improve the practicality and visual comfort, this work proposes a BCI based on Depth-of-Field using the high-frequency SSVEP response measured from below-the-hairline areas (behind-the-ears).
Two high-frequency stimuli (31 Hz and 32 Hz) were used in a Depth-of-Field setup to study the SSVEP response from behind-the-ears (TP9 and TP10). Multivariate Spectral F-test (MSFT) method was used to verify the elicited response. Afterwards, a BCI was proposed to command a mobile robot in a virtual reality environment. The commands were recognized through Temporally Local Multivariate Synchronization Index (TMSI) method.
The data analysis reveal that the focused stimuli elicit distinguishable SSVEP response when measured from hairless areas, in spite of the fact that the non-focused stimulus is also present in the field of view. Also, our BCI shows a satisfactory result, reaching average accuracy of 91.6% and Information Transfer Rate (ITR) of 5.3 bits/min.
These findings contribute to the development of more safe and practical BCI. |
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AbstractList | •Distinguishable high-frequency SSVEP was elicited from hairless areas using a Depth-of-Field setup.•SSVEP measured from below-the-hairline areas could be modulated by eye focusing mechanism.•BCI proposed achieved effective communication with binary choice.•BCI proposed uses comfortable stimuli (high-frequency range), practical electrodes placement and does not require a calibration phase by users.
Recently, a promising Brain-Computer Interface based on Steady-State Visual Evoked Potential (SSVEP-BCI) was proposed, which composed of two stimuli presented together in the center of the subject’s field of view, but at different depth planes (Depth-of-Field setup). Thus, users were easily able to select one of them by shifting their eye focus. However, in that work, EEG signals were collected through electrodes placed on occipital and parietal regions (hair-covered areas), which demanded a long preparation time. Also, that work used low-frequency stimuli, which can produce visual fatigue and increase the risk of photosensitive epileptic seizures. In order to improve the practicality and visual comfort, this work proposes a BCI based on Depth-of-Field using the high-frequency SSVEP response measured from below-the-hairline areas (behind-the-ears).
Two high-frequency stimuli (31 Hz and 32 Hz) were used in a Depth-of-Field setup to study the SSVEP response from behind-the-ears (TP9 and TP10). Multivariate Spectral F-test (MSFT) method was used to verify the elicited response. Afterwards, a BCI was proposed to command a mobile robot in a virtual reality environment. The commands were recognized through Temporally Local Multivariate Synchronization Index (TMSI) method.
The data analysis reveal that the focused stimuli elicit distinguishable SSVEP response when measured from hairless areas, in spite of the fact that the non-focused stimulus is also present in the field of view. Also, our BCI shows a satisfactory result, reaching average accuracy of 91.6% and Information Transfer Rate (ITR) of 5.3 bits/min.
These findings contribute to the development of more safe and practical BCI. Recently, a promising Brain-Computer Interface based on Steady-State Visual Evoked Potential (SSVEP-BCI) was proposed, which composed of two stimuli presented together in the center of the subject's field of view, but at different depth planes (Depth-of-Field setup). Thus, users were easily able to select one of them by shifting their eye focus. However, in that work, EEG signals were collected through electrodes placed on occipital and parietal regions (hair-covered areas), which demanded a long preparation time. Also, that work used low-frequency stimuli, which can produce visual fatigue and increase the risk of photosensitive epileptic seizures. In order to improve the practicality and visual comfort, this work proposes a BCI based on Depth-of-Field using the high-frequency SSVEP response measured from below-the-hairline areas (behind-the-ears). Two high-frequency stimuli (31 Hz and 32 Hz) were used in a Depth-of-Field setup to study the SSVEP response from behind-the-ears (TP9 and TP10). Multivariate Spectral F-test (MSFT) method was used to verify the elicited response. Afterwards, a BCI was proposed to command a mobile robot in a virtual reality environment. The commands were recognized through Temporally Local Multivariate Synchronization Index (TMSI) method. The data analysis reveal that the focused stimuli elicit distinguishable SSVEP response when measured from hairless areas, in spite of the fact that the non-focused stimulus is also present in the field of view. Also, our BCI shows a satisfactory result, reaching average accuracy of 91.6% and Information Transfer Rate (ITR) of 5.3 bits/min. These findings contribute to the development of more safe and practical BCI. Recently, a promising Brain-Computer Interface based on Steady-State Visual Evoked Potential (SSVEP-BCI) was proposed, which composed of two stimuli presented together in the center of the subject's field of view, but at different depth planes (Depth-of-Field setup). Thus, users were easily able to select one of them by shifting their eye focus. However, in that work, EEG signals were collected through electrodes placed on occipital and parietal regions (hair-covered areas), which demanded a long preparation time. Also, that work used low-frequency stimuli, which can produce visual fatigue and increase the risk of photosensitive epileptic seizures. In order to improve the practicality and visual comfort, this work proposes a BCI based on Depth-of-Field using the high-frequency SSVEP response measured from below-the-hairline areas (behind-the-ears).BACKGROUND AND OBJECTIVERecently, a promising Brain-Computer Interface based on Steady-State Visual Evoked Potential (SSVEP-BCI) was proposed, which composed of two stimuli presented together in the center of the subject's field of view, but at different depth planes (Depth-of-Field setup). Thus, users were easily able to select one of them by shifting their eye focus. However, in that work, EEG signals were collected through electrodes placed on occipital and parietal regions (hair-covered areas), which demanded a long preparation time. Also, that work used low-frequency stimuli, which can produce visual fatigue and increase the risk of photosensitive epileptic seizures. In order to improve the practicality and visual comfort, this work proposes a BCI based on Depth-of-Field using the high-frequency SSVEP response measured from below-the-hairline areas (behind-the-ears).Two high-frequency stimuli (31 Hz and 32 Hz) were used in a Depth-of-Field setup to study the SSVEP response from behind-the-ears (TP9 and TP10). Multivariate Spectral F-test (MSFT) method was used to verify the elicited response. Afterwards, a BCI was proposed to command a mobile robot in a virtual reality environment. The commands were recognized through Temporally Local Multivariate Synchronization Index (TMSI) method.METHODSTwo high-frequency stimuli (31 Hz and 32 Hz) were used in a Depth-of-Field setup to study the SSVEP response from behind-the-ears (TP9 and TP10). Multivariate Spectral F-test (MSFT) method was used to verify the elicited response. Afterwards, a BCI was proposed to command a mobile robot in a virtual reality environment. The commands were recognized through Temporally Local Multivariate Synchronization Index (TMSI) method.The data analysis reveal that the focused stimuli elicit distinguishable SSVEP response when measured from hairless areas, in spite of the fact that the non-focused stimulus is also present in the field of view. Also, our BCI shows a satisfactory result, reaching average accuracy of 91.6% and Information Transfer Rate (ITR) of 5.3 bits/min.RESULTSThe data analysis reveal that the focused stimuli elicit distinguishable SSVEP response when measured from hairless areas, in spite of the fact that the non-focused stimulus is also present in the field of view. Also, our BCI shows a satisfactory result, reaching average accuracy of 91.6% and Information Transfer Rate (ITR) of 5.3 bits/min.These findings contribute to the development of more safe and practical BCI.CONCLUSIONThese findings contribute to the development of more safe and practical BCI. |
ArticleNumber | 105271 |
Author | Diez, Pablo F. Delisle-Rodriguez, Denis Floriano, Alan Bastos-Filho, Teodiano Freire |
Author_xml | – sequence: 1 givenname: Alan surname: Floriano fullname: Floriano, Alan email: afloriano.ufes@gmail.com organization: Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria, Brazil – sequence: 2 givenname: Denis surname: Delisle-Rodriguez fullname: Delisle-Rodriguez, Denis email: delisle05@gmail.com organization: Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria, Brazil – sequence: 3 givenname: Pablo F. surname: Diez fullname: Diez, Pablo F. email: pdiez@gateme.unsj.edu.ar organization: Gabinete de Tecnologia Medica (GATEME), Facultad de Ingenieria, Universidad Nacional de San Juan, San Juan, Argentina – sequence: 4 givenname: Teodiano Freire surname: Bastos-Filho fullname: Bastos-Filho, Teodiano Freire email: teodiano.bastos@ufes.br organization: Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria, Brazil |
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Keywords | Brain-computer interface Hairless areas Depth-of-Field EEG |
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SubjectTerms | Adult Brain-computer interface Brain-Computer Interfaces Depth-of-Field EEG Electroencephalography Evoked Potentials, Visual Hairless areas Humans Multivariate Analysis Photic Stimulation Vision, Ocular |
Title | Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field |
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