Effect of Channel and Reference Selection on a Non-occipital Steady-State Visual Evoked Potential-Based Brain-Computer Interface

Wenli Lan; Hui Yang; Jing He; Yue Leng; Ruimin Wang; Keiji Iramina; Sheng Ge

doi:10.1109/ITNEC52019.2021.9586876

Effect of Channel and Reference Selection on a Non-occipital Steady-State Visual Evoked Potential-Based Brain-Computer Interface

Wenli Lan, Hui Yang, Jing He, Yue Leng, Ruimin Wang, Keiji Iramina, Sheng Ge

Intelligence Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

The requirements of occipital electroencephalogram signals restrict the practicality of steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCI). This study evaluated the feasibility and performance of using non-occipital signals to identify SSVEPs. Channel and reference selections were investigated to determine the optimal non-occipital channel combinations and reference electrode. The spectra and signal-to-noise ratio results indicated that signals from temporal and central regions are available for non-occipital SSVEP BCI. The target identification results confirmed that appropriate combinations of non-occipital channels and reference can achieve sufficient accuracy of approximately 80%.

Original language	English
Title of host publication	IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021
Editors	Bing Xu, Kefen Mou
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1274-1280
Number of pages	7
ISBN (Electronic)	9781665415989
DOIs	https://doi.org/10.1109/ITNEC52019.2021.9586876
Publication status	Published - Oct 15 2021
Event	5th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021 - Xi'an, China Duration: Oct 15 2021 → Oct 17 2021

Publication series

Name	IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021

Conference

Conference	5th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021
Country/Territory	China
City	Xi'an
Period	10/15/21 → 10/17/21

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Information Systems
Information Systems and Management
Electrical and Electronic Engineering
Control and Optimization
Safety, Risk, Reliability and Quality

Access to Document

10.1109/ITNEC52019.2021.9586876

Cite this

Lan, W., Yang, H., He, J., Leng, Y., Wang, R., Iramina, K., & Ge, S. (2021). Effect of Channel and Reference Selection on a Non-occipital Steady-State Visual Evoked Potential-Based Brain-Computer Interface. In B. Xu, & K. Mou (Eds.), IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021 (pp. 1274-1280). (IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITNEC52019.2021.9586876

Effect of Channel and Reference Selection on a Non-occipital Steady-State Visual Evoked Potential-Based Brain-Computer Interface. / Lan, Wenli; Yang, Hui; He, Jing et al.
IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021. ed. / Bing Xu; Kefen Mou. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1274-1280 (IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lan, W, Yang, H, He, J, Leng, Y, Wang, R, Iramina, K & Ge, S 2021, Effect of Channel and Reference Selection on a Non-occipital Steady-State Visual Evoked Potential-Based Brain-Computer Interface. in B Xu & K Mou (eds), IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021. IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 1274-1280, 5th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021, Xi'an, China, 10/15/21. https://doi.org/10.1109/ITNEC52019.2021.9586876

Lan W, Yang H, He J, Leng Y, Wang R, Iramina K et al. Effect of Channel and Reference Selection on a Non-occipital Steady-State Visual Evoked Potential-Based Brain-Computer Interface. In Xu B, Mou K, editors, IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 1274-1280. (IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021). doi: 10.1109/ITNEC52019.2021.9586876

Lan, Wenli ; Yang, Hui ; He, Jing et al. / Effect of Channel and Reference Selection on a Non-occipital Steady-State Visual Evoked Potential-Based Brain-Computer Interface. IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021. editor / Bing Xu ; Kefen Mou. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 1274-1280 (IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021).

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abstract = "The requirements of occipital electroencephalogram signals restrict the practicality of steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCI). This study evaluated the feasibility and performance of using non-occipital signals to identify SSVEPs. Channel and reference selections were investigated to determine the optimal non-occipital channel combinations and reference electrode. The spectra and signal-to-noise ratio results indicated that signals from temporal and central regions are available for non-occipital SSVEP BCI. The target identification results confirmed that appropriate combinations of non-occipital channels and reference can achieve sufficient accuracy of approximately 80%.",

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AB - The requirements of occipital electroencephalogram signals restrict the practicality of steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCI). This study evaluated the feasibility and performance of using non-occipital signals to identify SSVEPs. Channel and reference selections were investigated to determine the optimal non-occipital channel combinations and reference electrode. The spectra and signal-to-noise ratio results indicated that signals from temporal and central regions are available for non-occipital SSVEP BCI. The target identification results confirmed that appropriate combinations of non-occipital channels and reference can achieve sufficient accuracy of approximately 80%.

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Effect of Channel and Reference Selection on a Non-occipital Steady-State Visual Evoked Potential-Based Brain-Computer Interface

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