A BCI Speller with 120 Commands Encoded by Hybrid P300 and SSVEP Features

被引:0
|
作者
Xiao, Xiaolin [1 ,2 ]
Wen, Shengfu [1 ]
Han, Jin [2 ]
Yang, Man [1 ]
Yin, Erwei [3 ,4 ]
Xu, Minpeng [1 ,2 ]
Ming, Dong [1 ,2 ]
机构
[1] Tianjin Univ, Acad Med Engn & Translat Med, Tianjin, Peoples R China
[2] Tianjin Univ, Dept Biomed Engn, Coll Precis Instruments & Optoelect Engn, Tianjin, Peoples R China
[3] AMS, Def Innovat Inst, Beijing, Peoples R China
[4] TAIIC, Tianjin, Peoples R China
来源
HUMAN BRAIN AND ARTIFICIAL INTELLIGENCE, HBAI 2022 | 2023年 / 1692卷
基金
中国国家自然科学基金;
关键词
Brain-computer interface (BCI); Hybrid BCI; P300; Steady-state visual evoked potential (SSVEP); High-speed; Large command sets;
D O I
10.1007/978-981-19-8222-4_19
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Implementing higher speed and larger command sets for brain-computer interfaces (BCIs) has always been the pursuit of researchers, which is helpful to realize the technological applications. The hybrid BCIs jointly induce different electroencephalogram (EEG) signals and could improve system performance effectively. This study designed an online BCI with 120 commands and high-speed by hybrid P300 and steady-state visual evoked potential (SSVEP) features. A time-frequency-phase encoding strategy was used to encode 120 commands in a short time, this strategy used time-locked P300s and frequency and phase-locked SSVEPs with a wide frequency band. The step-wise linear discriminant analysis (SWLDA) and ensemble task-related component analysis (eTRCA) were severally used to decode P300s and SSVEPs. As a result, online average spelling ac-curacy across six subjects was 83.89%. Average and highest information transfer rate (ITR) for this system was 151.53 bits/min and 175.09 bits/min, respectively. Meanwhile, the shortest time for out-putting one command was only 1.45 s. These results demonstrate the feasibility and effectiveness of this highspeed BCI with 120 commands, furthermore, this study used a wider frequency band of SSVEPs to encode 120 commands, which is helpful to extend larger command sets and achieve higher system performance.
引用
收藏
页码:220 / 228
页数:9
相关论文
共 50 条
  • [41] Hybrid BCI Utilising SSVEP and P300 Event Markers for Reliable and Improved Classification Using LED Stimuli
    Mouli, Surej
    Palaniappan, Ramaswamy
    2017 IEEE SYMPOSIUM ON COMPUTER APPLICATIONS & INDUSTRIAL ELECTRONICS (ISCAIE), 2017, : 127 - 131
  • [42] Eliciting dual-frequency SSVEP using a hybrid SSVEP-P300 BCI
    Chang, Min Hye
    Lee, Jeong Su
    Heo, Jeong
    Park, Kwang Suk
    JOURNAL OF NEUROSCIENCE METHODS, 2016, 258 : 104 - 113
  • [43] THE P300 BCI IS MORE THAN AN ODDBALL AND MORE THAN A P300 BCI
    Shishkin, Sergei L.
    Ganin, Ilya P.
    Kaplan, Alexander Ya
    PSYCHOPHYSIOLOGY, 2009, 46 : S47 - S47
  • [44] Seeking RSVP Task Features Correlated with P300 Speller Performance
    Won, Kyungho
    Kwon, Moonyoung
    Lee, Sunghan
    Jang, Sehyeon
    Lee, Jongmin
    Ahn, Minkyu
    Jun, Sung Chan
    2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2018, : 1138 - 1143
  • [45] PolyMorph: A P300 Polymorphic Speller
    Casagrande, Alberto
    Jarmolowska, Joanna
    Turconi, Marcello
    Fabris, Francesco
    Battaglini, Piero Paolo
    BRAIN AND HEALTH INFORMATICS, 2013, 8211 : 297 - 306
  • [46] An approach to detecting ErrP elicited by feedback of P300 Speller BCI based on coefficients of determination
    Li, Ting
    Huang, Zhihua
    2020 13TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2020), 2020, : 506 - 510
  • [47] Analysis of Effect of RSVP Speller BCI Paradigm Along with CNN to Analysis P300 Signals
    Uma, M.
    Prabhu, S.
    Subramaniyam, Murali
    Min, Seung Nam
    AUGMENTED COGNITION, AC 2021, 2021, 12776 : 84 - 96
  • [48] A Brain-Computer Interface Speller using Peripheral Stimulus-based SSVEP and P300
    Hwang, Ji-Young
    Lee, Min-Ho
    Lee, Seong-Whan
    2017 5TH INTERNATIONAL WINTER CONFERENCE ON BRAIN-COMPUTER INTERFACE (BCI), 2017, : 77 - 78
  • [49] Channel selection methods for the P300 Speller
    Colwell, K. A.
    Ryan, D. B.
    Throckmorton, C. S.
    Sellers, E. W.
    Collins, L. M.
    JOURNAL OF NEUROSCIENCE METHODS, 2014, 232 : 6 - 15
  • [50] A comparison of classification techniques for the P300 Speller
    Krusienski, Dean J.
    Sellers, Eric W.
    Cabestaing, Francois
    Bayoudh, Sabri
    McFarland, Dennis J.
    Vaughan, Theresa M.
    Wolpaw, Jonathan R.
    JOURNAL OF NEURAL ENGINEERING, 2006, 3 (04) : 299 - 305