An Investigation of the Spatial Properties of Steady-state Visual Evoked Potentials around the Ear
Authors: 
Lingjie Zhao, Ruixin Luo, Dian Li, Xiaolin Xiao, Minpeng Xu, Dong MingAuthors Info & Claims
Pages 120 - 124
Abstract
Objective: Electroencephalography (EEG) around the ear is promising for building practical steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, the SSVEPs collected around the ear exhibit weak features and low signal-to-noise ratio (SNR), posing challenges for practical applications. Currently, there is a lack of efficient encoding methods to enhance the discriminability of periauricular SSVEPs. Method: This study conducted a spatial-coding experiment for SSVEP-BCIs around the ear, which contained four single-flicker targets placed in four spatial directions, i.e., up, down, left, and right. The collected periauricular SSVEPs were analyzed in time, frequency, and spatial domains, and classified using task-related component analysis (TRCA). Results: The periauricular SSVEPs elicited by stimuli of different directions were varying in amplitude, phase, and spatial distribution. Besides, the binary classification accuracy of periauricular SSVEPs achieved up to 80.33% for the up and down stimuli, and 77.27% for the left and right stimuli. The quaternary classification accuracy of all four stimuli for single-subject was up to 86%. Conclusion: These results demonstrate the feasibility of spatial coding for SSVEP-BCIs around the ear. Significance: This study has potential applications in developing practical BCIs.
References
[1]
Ruixin Luo, Minpeng Xu, Xiaoyu Zhou, Xiaolin Xiao, Tzyy-Ping Jung, and Dong Ming. 2022. Data Augmentation of SSVEPs Using Source Aliasing Matrix Estimation for Brain-Computer Interfaces. IEEE Trans Biomed Eng 70, 6 (Jun), 1775-1785. DOI=
[2]
Rajesh C. Panicker, Sadasivan Puthusserypady, and Ying Sun. 2011. An asynchronous P300 BCI with SSVEP-based control state detection. IEEE Trans Biomed Eng 58, 6 (Jun), 1781-1788. DOI=.
[3]
Shangkai Gao, Yijun Wang, Xiaorong Gao, Member, and Bo Hong. 2014. Visual and auditory brain-computer interfaces. IEEE Trans Biomed Eng 61, 5 (May), 1436-1447. DOI=.
[4]
D. Regan. 1989. Human Brain Electrophysiology: Evoked Potentials and Evoked Magnetic Fields in Science and Medicine. New York, NY, USA: Elsevier, p. 672.
[5]
Yu-Te Wang, Masaki Nakanishi, Yijun Wang, Chun-Shu Wei, Chung-Kuan Cheng and Tzyy-Ping Jung. 2017. An Online Brain-Computer Interface Based on SSVEPs Measured From Non-Hair-Bearing Areas. IEEE Trans Neural Syst Rehabil Eng 25, 1 (Jan), 11-18. DOI=.
[6]
Yu-Te Wang, Yijun Wang, Chung-Kuan Cheng, and Tzyy-Ping Jung. 2012. Measuring steady-state visual evoked potentials from non-hair-bearing areas. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE
[7]
Floriano, Alan, Pablo F. Diez, and Teodiano Freire Bastos-Filho. 2018. Evaluating the influence of chromatic and luminance stimuli on SSVEPs from behind-the-ears and occipital areas. Sensors (Basel) 18, 2 (Feb 17). DOI=.
[8]
Alan Floriano, Denis Delisle-Rodriguez, Pablo F. Diez, and Teodiano Freire Bastos-Filho. 2020. Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field. Comput Methods Programs Biomed 184(Feb), 105271. DOI=
[9]
Luciano Carmona, Pablo F. Diez. Eric Laciar, and Vicente Mut. 2020. Multisensory stimulation and EEG recording below the hair-line: a new paradigm on brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng 28, 4 (Apr), 825-831. DOI=
[10]
Huiqing Zhao, Li Zheng, Miao Yuan Xiaorong Gao, Ruping Liu, and Weihua Pei. 2023. OJ Neural Eng 20, 4 (Jul 20). DOI=
[11]
Xueyuan Li, Taichi Haba, Gaochao Cui, Fumiya Kinoshita, and Hideaki Touyama. 2024. The classification of SSVEP-BCI based on ear-EEG via RandOm Convolutional KErnel Transform with Morlet wavelet. Discover Applied Sciences 6, 4. DOI=.
[12]
Lin Jiang, Yueheng Peng, Runyang He, Qingqing Yang, Chanlin Yi, Yuqin Li, Bin Zhu, Yajing Si, Tao Zhang, Bharat B. Biswal, Dezhong Yao, Lan Xiong, Fali Li, and Peng Xu. 2023. Transcriptomic and Macroscopic Architectures of Multimodal Covariance Network Reveal Molecular-Structural-Functional Co-alterations. Research (Wash D C) 6, 0171. DOI=
[13]
Jiayan Zhang, Junshi Li, Zhe Huang, Dong Huang, Huaiqiang Yu, and Zhihong Li. 2023. Recent Progress in Wearable Brain-Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review. Health Data Sci 3, 0096. DOI=.
[14]
Liyan Liang, Guangyu Bin, Xiaogang Chen, Yijun Wang, Shangkai Gao, and Xiaorong Gao. 2021. Optimizing a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear. J Neural Eng 18, 6 (Dec 28). DOI=.
[15]
Minpeng Xu, Xiaolin Xiao, Yijun Wang, Hongzhi Qi, Tzyy-Ping Jung, and Dong Ming, et al. 2018. A brain-computer interface based on miniature-event-related potentials induced by very small lateral visual stimuli. IEEE Trans Biomed Eng 65, 5 (May), 1166-1175. DOI=.
[16]
Masaki Nakanishi, Yijun Wang, Xiaogang Chen, Yu-Te Wang, Xiaorong Gao, and Tzyy-Ping Jung. 2018. Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis. IEEE Trans Biomed Eng 65, 1 (Jan), 104-112. DOI=.
Index Terms
- An Investigation of the Spatial Properties of Steady-state Visual Evoked Potentials around the Ear
Recommendations
Statistical Characterization of Steady-State Visual Evoked Potentials and Their Use in Brain---Computer Interfaces
Steady-state visual evoked potential (SSVEP)-based brain---computer interfaces (BCIs) use the spectral power of the potentials for classification as they can be voluntarily enhanced or diminished by the subject by means of selective attention. The ...
A comprehensive review of deep learning power in steady-state visual evoked potentials
AbstractBrain–computer interfacing (BCI) research, fueled by deep learning, integrates insights from diverse domains. A notable focus is on steady-state visual evoked potential (SSVEP) in BCI applications, requiring in-depth assessment through deep ...
Modeling of evoked potentials of electroencephalograms: An overview
Electroencephalograms (EEGs) are brain waves, which are recorded using scalp electrodes. Generally, signal attenuate on recording and amplitude of the evoked potentials (EPs) are low when merged with the base brain waves. Therefore, mathematical tools ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
November 2024
284 pages
ISBN:9798400718274
DOI:10.1145/3707127
Copyright © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 06 February 2025
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
ICBBE 2024
ICBBE 2024: 2024 11th International Conference on Biomedical and Bioinformatics Engineering
November 8 - 11, 2024
Osaka, Japan
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 23Total Downloads
- Downloads (Last 12 months)23
- Downloads (Last 6 weeks)23
Reflects downloads up to 07 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in