Literature DB >> 21436517

A cell-phone-based brain-computer interface for communication in daily life.

Yu-Te Wang1, Yijun Wang, Tzyy-Ping Jung.   

Abstract

Moving a brain-computer interface (BCI) system from a laboratory demonstration to real-life applications still poses severe challenges to the BCI community. This study aims to integrate a mobile and wireless electroencephalogram (EEG) system and a signal-processing platform based on a cell phone into a truly wearable and wireless online BCI. Its practicality and implications in a routine BCI are demonstrated through the realization and testing of a steady-state visual evoked potential (SSVEP)-based BCI. This study implemented and tested online signal processing methods in both time and frequency domains for detecting SSVEPs. The results of this study showed that the performance of the proposed cell-phone-based platform was comparable, in terms of the information transfer rate, with other BCI systems using bulky commercial EEG systems and personal computers. To the best of our knowledge, this study is the first to demonstrate a truly portable, cost-effective and miniature cell-phone-based platform for online BCIs.

Mesh:

Year:  2011        PMID: 21436517     DOI: 10.1088/1741-2560/8/2/025018

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


  21 in total

1.  Investigation of different classifiers and channel configurations of a mobile P300-based brain-computer interface.

Authors:  Simone A Ludwig; Jun Kong
Journal:  Med Biol Eng Comput       Date:  2017-05-29       Impact factor: 2.602

2.  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

3.  A collaborative brain-computer interface for improving human performance.

Authors:  Yijun Wang; Tzyy-Ping Jung
Journal:  PLoS One       Date:  2011-05-31       Impact factor: 3.240

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.  A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials.

Authors:  Masaki Nakanishi; Yijun Wang; Yu-Te Wang; Tzyy-Ping Jung
Journal:  PLoS One       Date:  2015-10-19       Impact factor: 3.240

6.  Physiological artifacts in scalp EEG and ear-EEG.

Authors:  Simon L Kappel; David Looney; Danilo P Mandic; Preben Kidmose
Journal:  Biomed Eng Online       Date:  2017-08-11       Impact factor: 2.819

7.  Single-trial classification of gait and point movement preparation from human EEG.

Authors:  Priya D Velu; Virginia R de Sa
Journal:  Front Neurosci       Date:  2013-06-11       Impact factor: 4.677

8.  Assessing the quality of steady-state visual-evoked potentials for moving humans using a mobile electroencephalogram headset.

Authors:  Yuan-Pin Lin; Yijun Wang; Chun-Shu Wei; Tzyy-Ping Jung
Journal:  Front Hum Neurosci       Date:  2014-03-31       Impact factor: 3.169

9.  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

10.  Assessing the feasibility of online SSVEP decoding in human walking using a consumer EEG headset.

Authors:  Yuan-Pin Lin; Yijun Wang; Tzyy-Ping Jung
Journal:  J Neuroeng Rehabil       Date:  2014-08-09       Impact factor: 4.262

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