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      Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling

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          Abstract

          The brain–computer interface (BCI) spellers based on steady-state visual evoked potentials (SSVEPs) have recently been widely investigated for their high information transfer rates (ITRs). This paper aims to improve the practicability of the SSVEP-BCIs for high-speed spelling. The system acquired the electroencephalogram (EEG) data from a self-developed dedicated EEG device and the stimulation was arranged as a keyboard. The task-related component analysis (TRCA) spatial filter was modified (mTRCA) for target classification and showed significantly higher performance compared with the original TRCA in the offline analysis. In the online system, the dynamic stopping (DS) strategy based on Bayesian posterior probability was utilized to realize alterable stimulating time. In addition, the temporal filtering process and the programs were optimized to facilitate the online DS operation. Notably, the online ITR reached 330.4 ± 45.4 bits/min on average, which is significantly higher than that of fixed stopping (FS) strategy, and the peak value of 420.2 bits/min is the highest online spelling ITR with a SSVEP-BCI up to now. The proposed system with portable EEG acquisition, friendly interaction, and alterable time of command output provides more flexibility for SSVEP-based BCIs and is promising for practical high-speed spelling.

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          Steady-state visually evoked potentials: focus on essential paradigms and future perspectives.

          François-Benoît Vialatte, Monique Maurice, Justin Dauwels … (2010)
          After 40 years of investigation, steady-state visually evoked potentials (SSVEPs) have been shown to be useful for many paradigms in cognitive (visual attention, binocular rivalry, working memory, and brain rhythms) and clinical neuroscience (aging, neurodegenerative disorders, schizophrenia, ophthalmic pathologies, migraine, autism, depression, anxiety, stress, and epilepsy). Recently, in engineering, SSVEPs found a novel application for SSVEP-driven brain-computer interface (BCI) systems. Although some SSVEP properties are well documented, many questions are still hotly debated. We provide an overview of recent SSVEP studies in neuroscience (using implanted and scalp EEG, fMRI, or PET), with the perspective of modern theories about the visual pathway. We investigate the steady-state evoked activity, its properties, and the mechanisms behind SSVEP generation. Next, we describe the SSVEP-BCI paradigm and review recently developed SSVEP-based BCI systems. Lastly, we outline future research directions related to basic and applied aspects of SSVEPs. Copyright 2009 Elsevier Ltd. All rights reserved.
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            Brain-Computer Interfaces for Communication and Control.

            D McFarland, Jonathan Wolpaw (2010)
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              A high-speed brain speller using steady-state visual evoked potentials.

              Masaki Nakanishi, Yijun Wang, Yu-Te Wang … (2014)
              Implementing a complex spelling program using a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) remains a challenge due to difficulties in stimulus presentation and target identification. This study aims to explore the feasibility of mixed frequency and phase coding in building a high-speed SSVEP speller with a computer monitor. A frequency and phase approximation approach was developed to eliminate the limitation of the number of targets caused by the monitor refresh rate, resulting in a speller comprising 32 flickers specified by eight frequencies (8-15 Hz with a 1 Hz interval) and four phases (0°, 90°, 180°, and 270°). A multi-channel approach incorporating Canonical Correlation Analysis (CCA) and SSVEP training data was proposed for target identification. In a simulated online experiment, at a spelling rate of 40 characters per minute, the system obtained an averaged information transfer rate (ITR) of 166.91 bits/min across 13 subjects with a maximum individual ITR of 192.26 bits/min, the highest ITR ever reported in electroencephalogram (EEG)-based BCIs. The results of this study demonstrate great potential of a high-speed SSVEP-based BCI in real-life applications.
              Article 0 comments Cited 70 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Sensors (Basel)
                Journal ID (iso-abbrev): Sensors (Basel)
                Journal ID (publisher-id): sensors
                Title: Sensors (Basel, Switzerland)
                Publisher: MDPI
                ISSN (Electronic): 1424-8220
                Publication date (Electronic): 28 July 2020
                Publication date Collection: August 2020
                Volume: 20
                Issue: 15
                Electronic Location Identifier: 4186
                Affiliations
                [1 ]Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; jiahe@ 123456tju.edu.cn (J.T.); minpeng.xu@ 123456tju.edu.cn (M.X.); jinhan@ 123456tju.edu.cn (J.H.); daitingfei@ 123456tju.edu.cn (T.D.); shanguang_chen@ 123456126.com (S.C.)
                [2 ]Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China; 3005202010@ 123456tju.edu.cn
                [3 ]National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing 100094, China
                Author notes
                Author information
                https://orcid.org/0000-0002-4439-1328
                https://orcid.org/0000-0002-6912-1738
                Article
                Publisher ID: sensors-20-04186
                DOI: 10.3390/s20154186
                PMC ID: 7435370
                PubMed ID: 32731432
                SO-VID: 4cc660d0-1d95-4d3e-8e51-87da4d5029b6
                Copyright © © 2020 by the authors.
                License:

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 25 June 2020
                Date accepted : 25 July 2020
                Categories
                Subject: Article

                ScienceOpen disciplines: Biomedical engineering
                Keywords: brain–computer interface (bci),steady-state visual evoked potential (ssvep),practical,dynamic stopping (ds),modified task-related component analysis (mtrca)
                Data availability:
                ScienceOpen disciplines: Biomedical engineering
                Keywords: brain–computer interface (bci), steady-state visual evoked potential (ssvep), practical, dynamic stopping (ds), modified task-related component analysis (mtrca)

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