Literature DB >> 33594631

EEG-based hybrid QWERTY mental speller with high information transfer rate.

Er Akshay Katyal1, Rajesh Singla2.   

Abstract

BACKGROUND: Brain-computer interface (BCI) spellers detect variations in brain waves to help subjects communicate with the world. This study introduces a P300-SSVEP hybrid BCI-based QWERTY speller.
METHODS: The proposed hybrid speller, combines SSVEP and P300 features using a hybrid paradigm. P300 was used as time division multiplexing index which results in the use of lesser number of assumed frequencies for SSVEP elicitation. Each flickering frequency was also assigned a unique colour, to enhance system accuracy.
RESULTS: On the basis of 20 subjects, an average accuracy of classification of 96.42% and a mean information transfer rate (ITR) of 131.0 bits per min. (BPM) was achieved during the free spelling trial (trial-F). COMPARISON: The t test results revealed that the hybrid QWERTY speller performed significantly better (on the basis of mean classification accuracy and ITR) as compared to the traditional P300 speller) and the QWERTY SSVEP speller. Also, the amount of time taken to spell a word was significantly lesser in the case of hybrid QWERTY speller in contrast to traditional P300 speller while it was almost the same as compared to QWERTY SSVEP speller.
CONCLUSION: QWERTY speller outperformed the stereotypical P300 speller as well as QWERTY SSVEP speller.

Keywords:  BCI; ITR; P300; SSVEP; Speller

Year:  2021        PMID: 33594631     DOI: 10.1007/s11517-020-02310-w

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  34 in total

1.  Toward a hybrid brain-computer interface based on imagined movement and visual attention.

Authors:  B Z Allison; C Brunner; V Kaiser; G R Müller-Putz; C Neuper; G Pfurtscheller
Journal:  J Neural Eng       Date:  2010-03-23       Impact factor: 5.379

2.  Deactivation of brain areas during self-regulation of slow cortical potentials in seizure patients.

Authors:  Ute Strehl; Tracy Trevorrow; Ralf Veit; Thilo Hinterberger; Boris Kotchoubey; Michael Erb; Niels Birbaumer
Journal:  Appl Psychophysiol Biofeedback       Date:  2006-03

3.  A practical VEP-based brain-computer interface.

Authors:  Yijun Wang; Ruiping Wang; Xiaorong Gao; Bo Hong; Shangkai Gao
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2006-06       Impact factor: 3.802

4.  Brain-computer interface using a simplified functional near-infrared spectroscopy system.

Authors:  Shirley M Coyle; Tomás E Ward; Charles M Markham
Journal:  J Neural Eng       Date:  2007-04-27       Impact factor: 5.379

5.  Correlation-based channel selection and regularized feature optimization for MI-based BCI.

Authors:  Jing Jin; Yangyang Miao; Ian Daly; Cili Zuo; Dewen Hu; Andrzej Cichocki
Journal:  Neural Netw       Date:  2019-07-15

6.  Performance Prediction for a Near-Infrared Spectroscopy-Brain-Computer Interface Using Resting-State Functional Connectivity of the Prefrontal Cortex.

Authors:  Jaeyoung Shin; Chang-Hwan Im
Journal:  Int J Neural Syst       Date:  2018-05-11       Impact factor: 5.866

7.  On the influence of task relevance and stimulus probability on event-related-potential components.

Authors:  K C Squires; E Donchin; R I Herning; G McCarthy
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1977-01

8.  Towards correlation-based time window selection method for motor imagery BCIs.

Authors:  Jiankui Feng; Erwei Yin; Jing Jin; Rami Saab; Ian Daly; Xingyu Wang; Dewen Hu; Andrzej Cichocki
Journal:  Neural Netw       Date:  2018-03-02

9.  P300 brain computer interface: current challenges and emerging trends.

Authors:  Reza Fazel-Rezai; Brendan Z Allison; Christoph Guger; Eric W Sellers; Sonja C Kleih; Andrea Kübler
Journal:  Front Neuroeng       Date:  2012-07-17

Review 10.  Steady-State Somatosensory Evoked Potential for Brain-Computer Interface-Present and Future.

Authors:  Sangtae Ahn; Kiwoong Kim; Sung Chan Jun
Journal:  Front Hum Neurosci       Date:  2016-01-14       Impact factor: 3.169

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.