Literature DB >> 19545601

How many people are able to control a P300-based brain-computer interface (BCI)?

Christoph Guger1, Shahab Daban, Eric Sellers, Clemens Holzner, Gunther Krausz, Roberta Carabalona, Furio Gramatica, Guenter Edlinger.   

Abstract

An EEG-based brain-computer system can be used to control external devices such as computers, wheelchairs or Virtual Environments. One of the most important applications is a spelling device to aid severely disabled individuals with communication, for example people disabled by amyotrophic lateral sclerosis (ALS). P300-based BCI systems are optimal for spelling characters with high speed and accuracy, as compared to other BCI paradigms such as motor imagery. In this study, 100 subjects tested a P300-based BCI system to spell a 5-character word with only 5 min of training. EEG data were acquired while the subject looked at a 36-character matrix to spell the word WATER. Two different versions of the P300 speller were used: (i) the row/column speller (RC) that flashes an entire column or row of characters and (ii) a single character speller (SC) that flashes each character individually. The subjects were free to decide which version to test. Nineteen subjects opted to test both versions. The BCI system classifier was trained on the data collected for the word WATER. During the real-time phase of the experiment, the subject spelled the word LUCAS, and was provided with the classifier selection accuracy after each of the five letters. Additionally, subjects filled out a questionnaire about age, sex, education, sleep duration, working duration, cigarette consumption, coffee consumption, and level of disturbance that the flashing characters produced. 72.8% (N=81) of the subjects were able to spell with 100% accuracy in the RC paradigm and 55.3% (N=38) of the subjects spelled with 100% accuracy in the SC paradigm. Less than 3% of the subjects did not spell any character correctly. People who slept less than 8h performed significantly better than other subjects. Sex, education, working duration, and cigarette and coffee consumption were not statistically related to differences in accuracy. The disturbance of the flashing characters was rated with a median score of 1 on a scale from 1 to 5 (1, not disturbing; 5, highly disturbing). This study shows that high spelling accuracy can be achieved with the P300 BCI system using approximately 5 min of training data for a large number of non-disabled subjects, and that the RC paradigm is superior to the SC paradigm. 89% of the 81 RC subjects were able to spell with accuracy 80-100%. A similar study using a motor imagery BCI with 99 subjects showed that only 19% of the subjects were able to achieve accuracy of 80-100%. These large differences in accuracy suggest that with limited amounts of training data the P300-based BCI is superior to the motor imagery BCI. Overall, these results are very encouraging and a similar study should be conducted with subjects who have ALS to determine if their accuracy levels are similar.

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Year:  2009        PMID: 19545601     DOI: 10.1016/j.neulet.2009.06.045

Source DB:  PubMed          Journal:  Neurosci Lett        ISSN: 0304-3940            Impact factor:   3.046


  113 in total

1.  Control of a visual keyboard using an electrocorticographic brain-computer interface.

Authors:  Dean J Krusienski; Jerry J Shih
Journal:  Neurorehabil Neural Repair       Date:  2010-10-04       Impact factor: 3.919

2.  Predictive spelling with a P300-based brain-computer interface: Increasing the rate of communication.

Authors:  D B Ryan; G E Frye; G Townsend; D R Berry; S Mesa-G; N A Gates; E W Sellers
Journal:  Int J Hum Comput Interact       Date:  2011-01-01       Impact factor: 3.353

3.  Does the 'P300' speller depend on eye gaze?

Authors:  P Brunner; S Joshi; S Briskin; J R Wolpaw; H Bischof; G Schalk
Journal:  J Neural Eng       Date:  2010-09-21       Impact factor: 5.379

4.  A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns.

Authors:  G Townsend; B K LaPallo; C B Boulay; D J Krusienski; G E Frye; C K Hauser; N E Schwartz; T M Vaughan; J R Wolpaw; E W Sellers
Journal:  Clin Neurophysiol       Date:  2010-03-26       Impact factor: 3.708

5.  Brain-computer interface: current and emerging rehabilitation applications.

Authors:  Janis J Daly; Jane E Huggins
Journal:  Arch Phys Med Rehabil       Date:  2015-03       Impact factor: 3.966

6.  Designing a hands-on brain computer interface laboratory course.

Authors:  Bahar Khalighinejad; Laura Kathleen Long; Nima Mesgarani
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2016-08

7.  Heading for new shores! Overcoming pitfalls in BCI design.

Authors:  Ricardo Chavarriaga; Melanie Fried-Oken; Sonja Kleih; Fabien Lotte; Reinhold Scherer
Journal:  Brain Comput Interfaces (Abingdon)       Date:  2016-12-30

8.  Optimized stimulus presentation patterns for an event-related potential EEG-based brain-computer interface.

Authors:  Jing Jin; Brendan Z Allison; Eric W Sellers; Clemens Brunner; Petar Horki; Xingyu Wang; Christa Neuper
Journal:  Med Biol Eng Comput       Date:  2010-10-02       Impact factor: 2.602

9.  Improved P300 speller performance using electrocorticography, spectral features, and natural language processing.

Authors:  William Speier; Itzhak Fried; Nader Pouratian
Journal:  Clin Neurophysiol       Date:  2013-03-05       Impact factor: 3.708

10.  The influence of psychological state and motivation on brain-computer interface performance in patients with amyotrophic lateral sclerosis - a longitudinal study.

Authors:  Femke Nijboer; Niels Birbaumer; Andrea Kübler
Journal:  Front Neurosci       Date:  2010-07-21       Impact factor: 4.677

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