Literature DB >> 26913648

Pushing the P300-based brain-computer interface beyond 100 bpm: extending performance guided constraints into the temporal domain.

G Townsend1, V Platsko.   

Abstract

OBJECTIVE: A new presentation paradigm for the P300-based brain-computer interface (BCI) referred to as the 'asynchronous paradigm' (ASP) is introduced and studied. It is based on the principle of performance guided constraints (Townsend et al 2012 Neurosci. Lett. 531 63-8) extended from the spatial domain into the temporal domain. The traditional constraint of flashing targets in predefined constant epochs of time is eliminated and targets flash asynchronously with timing based instead on constraints intended to improve performance. APPROACH: We propose appropriate temporal constraints to derive the ASP and compare its performance to that of the 'checkerboard paradigm' (CBP), which has previously been shown to be superior to the standard 'row/column paradigm' introduced by Farwell and Donchin (1988 Electroencephalogr. Clin. Neurophysiol. 70 510-23). Ten participants were tested in the ASP and CBP conditions both with traditional flashing items and with flashing faces in place of the targets (see Zhang et al 2012 J. Neural Eng. 9 026018; Kaufmann and Kübler 2014 J. Neural Eng. 11 ; Chen et al 2015 J. Neurosci. Methods 239 18-27). Eleven minutes of calibration data were used as input to a stepwise linear discriminant analysis to derive classification coefficients used for online classification. MAIN
RESULTS: Accuracy was consistently high for both paradigms (87% and 93%) while information transfer rate was 45% higher for the ASP than the CBP. In a free spelling task, one subject spelled a 66 character sentence (from a 72 item matrix) with 100% accuracy in 3 min and 24 s demonstrating a practical throughput of 120 bits per minute (bpm) with a theoretical upper bound of 258 bpm. The subject repeated the task three times in a row without error. SIGNIFICANCE: This work represents an advance in P300 speller technology and raises the ceiling that was being reached on P300-based BCIs. Most importantly, the research presented here is a novel and effective general strategy for organising timing for flashing items. The ASP is only one possible implementation of this work since in general it can be used to describe all previous existing presentation paradigms as well as any possible new ones. This may be especially important for people with neuromuscular disabilities.

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Year:  2016        PMID: 26913648     DOI: 10.1088/1741-2560/13/2/026024

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


  7 in total

1.  High performance communication by people with paralysis using an intracortical brain-computer interface.

Authors:  Chethan Pandarinath; Paul Nuyujukian; Christine H Blabe; Brittany L Sorice; Jad Saab; Francis R Willett; Leigh R Hochberg; Krishna V Shenoy; Jaimie M Henderson
Journal:  Elife       Date:  2017-02-21       Impact factor: 8.140

Review 2.  Brain-Computer Interfaces for Augmentative and Alternative Communication: A Tutorial.

Authors:  Jonathan S Brumberg; Kevin M Pitt; Alana Mantie-Kozlowski; Jeremy D Burnison
Journal:  Am J Speech Lang Pathol       Date:  2018-02-06       Impact factor: 2.408

3.  Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction.

Authors:  B O Mainsah; G Reeves; L M Collins; C S Throckmorton
Journal:  J Neural Eng       Date:  2017-08       Impact factor: 5.379

4.  Comparison of Four Control Methods for a Five-Choice Assistive Technology.

Authors:  Sebastian Halder; Kouji Takano; Kenji Kansaku
Journal:  Front Hum Neurosci       Date:  2018-06-06       Impact factor: 3.169

5.  Separable EEG Features Induced by Timing Prediction for Active Brain-Computer Interfaces.

Authors:  Jiayuan Meng; Minpeng Xu; Kun Wang; Qiangfan Meng; Jin Han; Xiaolin Xiao; Shuang Liu; Dong Ming
Journal:  Sensors (Basel)       Date:  2020-06-25       Impact factor: 3.576

6.  Cortical control of a tablet computer by people with paralysis.

Authors:  Paul Nuyujukian; Jose Albites Sanabria; Jad Saab; Chethan Pandarinath; Beata Jarosiewicz; Christine H Blabe; Brian Franco; Stephen T Mernoff; Emad N Eskandar; John D Simeral; Leigh R Hochberg; Krishna V Shenoy; Jaimie M Henderson
Journal:  PLoS One       Date:  2018-11-21       Impact factor: 3.240

7.  Utilizing sensory prediction errors for movement intention decoding: A new methodology.

Authors:  Gowrishankar Ganesh; Keigo Nakamura; Supat Saetia; Alejandra Mejia Tobar; Eiichi Yoshida; Hideyuki Ando; Natsue Yoshimura; Yasuharu Koike
Journal:  Sci Adv       Date:  2018-05-09       Impact factor: 14.136

  7 in total

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