Literature DB >> 20177780

An SSVEP-actuated brain computer interface using phase-tagged flickering sequences: a cursor system.

Po-Lei Lee1, Jyun-Jie Sie, Yu-Ju Liu, Chi-Hsun Wu, Ming-Huan Lee, Chih-Hung Shu, Po-Hung Li, Chia-Wei Sun, Kuo-Kai Shyu.   

Abstract

This study presents a new steady-state visual evoked potential (SSVEP)-based brain computer interface (BCI). SSVEPs, induced by phase-tagged flashes in eight light emitting diodes (LEDs), were used to control four cursor movements (up, right, down, and left) and four button functions (on, off, right-, and left-clicks) on a screen menu. EEG signals were measured by one EEG electrode placed at Oz position, referring to the international EEG 10-20 system. Since SSVEPs are time-locked and phase-locked to the onsets of SSVEP flashes, EEG signals were bandpass-filtered and segmented into epochs, and then averaged across a number of epochs to sharpen the recorded SSVEPs. Phase lags between the measured SSVEPs and a reference SSVEP were measured, and targets were recognized based on these phase lags. The current design used eight LEDs to flicker at 31.25 Hz with 45 degrees phase margin between any two adjacent SSVEP flickers. The SSVEP responses were filtered within 29.25-33.25 Hz and then averaged over 60 epochs. Owing to the utilization of high-frequency flickers, the induced SSVEPs were away from low-frequency noises, 60 Hz electricity noise, and eye movement artifacts. As a consequence, we achieved a simple architecture that did not require eye movement monitoring or other artifact detection and removal. The high-frequency design also achieved a flicker fusion effect for better visualization. Seven subjects were recruited in this study to sequentially input a command sequence, consisting of a sequence of eight cursor functions, repeated three times. The accuracy and information transfer rate (mean +/- SD) over the seven subjects were 93.14 +/- 5.73% and 28.29 +/- 12.19 bits/min, respectively. The proposed system can provide a reliable channel for severely disabled patients to communicate with external environments.

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Year:  2010        PMID: 20177780     DOI: 10.1007/s10439-010-9964-y

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  19 in total

1.  Studying modulation on simultaneously activated SSVEP neural networks by a cognitive task.

Authors:  Zhenghua Wu
Journal:  J Biol Phys       Date:  2014-01-13       Impact factor: 1.365

2.  Practical real-time MEG-based neural interfacing with optically pumped magnetometers.

Authors:  Marc M Van Hulle; Richard Bowtell; Matthew J Brookes; Benjamin Wittevrongel; Niall Holmes; Elena Boto; Ryan Hill; Molly Rea; Arno Libert; Elvira Khachatryan
Journal:  BMC Biol       Date:  2021-08-10       Impact factor: 7.431

3.  Performance assessment in brain-computer interface-based augmentative and alternative communication.

Authors:  David E Thompson; Stefanie Blain-Moraes; Jane E Huggins
Journal:  Biomed Eng Online       Date:  2013-05-16       Impact factor: 2.819

4.  Multiple frequencies sequential coding for SSVEP-based brain-computer interface.

Authors:  Yangsong Zhang; Peng Xu; Tiejun Liu; Jun Hu; Rui Zhang; Dezhong Yao
Journal:  PLoS One       Date:  2012-03-06       Impact factor: 3.240

5.  Multi-phase cycle coding for SSVEP based brain-computer interfaces.

Authors:  Jijun Tong; Danhua Zhu
Journal:  Biomed Eng Online       Date:  2015-01-16       Impact factor: 2.819

Review 6.  A dynamic selection method for reference electrode in SSVEP-based BCI.

Authors:  Zhenghua Wu; Sheng Su
Journal:  PLoS One       Date:  2014-08-06       Impact factor: 3.240

7.  Improvement of classification accuracy in a phase-tagged steady-state visual evoked potential-based brain computer interface using multiclass support vector machine.

Authors:  Chia-Lung Yeh; Po-Lei Lee; Wei-Ming Chen; Chun-Yen Chang; Yu-Te Wu; Gong-Yau Lan
Journal:  Biomed Eng Online       Date:  2013-05-21       Impact factor: 2.819

8.  SSVEP extraction based on the similarity of background EEG.

Authors:  Zhenghua Wu
Journal:  PLoS One       Date:  2014-04-07       Impact factor: 3.240

9.  Objective evaluation of fatigue by EEG spectral analysis in steady-state visual evoked potential-based brain-computer interfaces.

Authors:  Teng Cao; Feng Wan; Chi Man Wong; Janir Nuno da Cruz; Yong Hu
Journal:  Biomed Eng Online       Date:  2014-03-12       Impact factor: 2.819

10.  Generating visual flickers for eliciting robust steady-state visual evoked potentials at flexible frequencies using monitor refresh rate.

Authors:  Masaki Nakanishi; Yijun Wang; Yu-Te Wang; Yasue Mitsukura; Tzyy-Ping Jung
Journal:  PLoS One       Date:  2014-06-11       Impact factor: 3.240

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