Literature DB >> 23369924

A hybrid BCI speller paradigm combining P300 potential and the SSVEP blocking feature.

Minpeng Xu1, Hongzhi Qi, Baikun Wan, Tao Yin, Zhipeng Liu, Dong Ming.   

Abstract

OBJECTIVE: Hybrid brain-computer interfaces (BCIs) have been proved to be more effective in mental control by combining another channel of physiologic control signals. Among those studies, little attention has been paid to the combined use of a steady-state visual evoked potential (SSVEP) and P300 potential, both providing the fastest and the most reliable EEG based BCIs. In this paper, a novel hybrid BCI speller is developed to elicit P300 potential and SSVEP blocking (SSVEP-B) distinctly and simultaneously with the same target stimulus. APPROACH: Twelve subjects were involved in the study and every one performed offline spelling twice in succession with two different speller paradigms for comparison: hybrid speller and control P300-speller. Feature analysis was adopted from the view of time domain, frequency domain and spatial distribution; the performances were evaluated by character accuracy and information transfer rate (ITR). MAIN
RESULTS: Signal analysis of the hybrid paradigm shows that SSVEPs are an evident EEG component during the nontarget phase but are dismissed and replaced by P300 potentials after target stimuli. The absence of an SSVEP, called SSVEP-B, mostly appearing in channel Oz, presents a sharp distinction between target responses and nontarget responses. The r(2) value of SSVEP-B in channel Oz is comparable to that of P300 in channel Cz. Compared with the control P300-speller, the hybrid speller achieves significantly higher accuracy and ITR with combined features. SIGNIFICANCE: The results indicate that the combination of P300 with an SSVEP-B improves target discrimination greatly; the proposed hybrid paradigm is superior to the control paradigm in spelling performance. Thus, our findings provide a new approach to improve BCI performances.

Entities:  

Mesh:

Year:  2013        PMID: 23369924     DOI: 10.1088/1741-2560/10/2/026001

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  24 in total

1.  Recursive Bayesian Coding for BCIs.

Authors:  Matt Higger; Fernando Quivira; Murat Akcakaya; Mohammad Moghadamfalahi; Hooman Nezamfar; Mujdat Cetin; Deniz Erdogmus
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2016-07-13       Impact factor: 3.802

Review 2.  Integrating language models into classifiers for BCI communication: a review.

Authors:  W Speier; C Arnold; N Pouratian
Journal:  J Neural Eng       Date:  2016-05-06       Impact factor: 5.379

3.  Spatio-Temporal EEG Models for Brain Interfaces.

Authors:  P Gonzalez-Navarro; M Moghadamfalahi; M Akcakaya; D Erdogmus
Journal:  Signal Processing       Date:  2016-08-06       Impact factor: 4.662

4.  Three-Dimensional Brain-Computer Interface Control Through Simultaneous Overt Spatial Attentional and Motor Imagery Tasks.

Authors:  Jianjun Meng; Taylor Streitz; Nicholas Gulachek; Daniel Suma; Bin He
Journal:  IEEE Trans Biomed Eng       Date:  2018-10-01       Impact factor: 4.538

Review 5.  Review of brain encoding and decoding mechanisms for EEG-based brain-computer interface.

Authors:  Lichao Xu; Minpeng Xu; Tzyy-Ping Jung; Dong Ming
Journal:  Cogn Neurodyn       Date:  2021-04-10       Impact factor: 3.473

Review 6.  Bacomics: a comprehensive cross area originating in the studies of various brain-apparatus conversations.

Authors:  Dezhong Yao; Yangsong Zhang; Tiejun Liu; Peng Xu; Diankun Gong; Jing Lu; Yang Xia; Cheng Luo; Daqing Guo; Li Dong; Yongxiu Lai; Ke Chen; Jianfu Li
Journal:  Cogn Neurodyn       Date:  2020-03-17       Impact factor: 3.473

7.  An efficient frequency recognition method based on likelihood ratio test for SSVEP-based BCI.

Authors:  Yangsong Zhang; Li Dong; Rui Zhang; Dezhong Yao; Yu Zhang; Peng Xu
Journal:  Comput Math Methods Med       Date:  2014-08-28       Impact factor: 2.238

8.  Simultaneous detection of P300 and steady-state visually evoked potentials for hybrid brain-computer interface.

Authors:  Adrien Combaz; Marc M Van Hulle
Journal:  PLoS One       Date:  2015-03-27       Impact factor: 3.240

9.  SSVEP response is related to functional brain network topology entrained by the flickering stimulus.

Authors:  Yangsong Zhang; Peng Xu; Yingling Huang; Kaiwen Cheng; Dezhong Yao
Journal:  PLoS One       Date:  2013-09-09       Impact factor: 3.240

10.  Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface.

Authors:  M Jawad Khan; Melissa Jiyoun Hong; Keum-Shik Hong
Journal:  Front Hum Neurosci       Date:  2014-04-28       Impact factor: 3.169

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.