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

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 s...

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Veröffentlicht in:Neuroscience letters 2009-09, Vol.462 (1), p.94-98
Hauptverfasser: Guger, Christoph, Daban, Shahab, Sellers, Eric, Holzner, Clemens, Krausz, Gunther, Carabalona, Roberta, Gramatica, Furio, Edlinger, Guenter
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container_end_page 98
container_issue 1
container_start_page 94
container_title Neuroscience letters
container_volume 462
creator Guger, Christoph
Daban, Shahab
Sellers, Eric
Holzner, Clemens
Krausz, Gunther
Carabalona, Roberta
Gramatica, Furio
Edlinger, Guenter
description 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 8 h 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%
doi_str_mv 10.1016/j.neulet.2009.06.045
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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 8 h 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. 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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 8 h 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). 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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.</description><subject>Adult</subject><subject>Biofeedback, Psychology</subject><subject>Biological and medical sciences</subject><subject>Brain - physiology</subject><subject>Brain–computer interface (BCI)</subject><subject>Electroencephalogram (EEG)</subject><subject>Electroencephalography - methods</subject><subject>Event-Related Potentials, P300</subject><subject>Female</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Humans</subject><subject>Male</subject><subject>Neuropsychological Tests</subject><subject>P300 potential</subject><subject>Practice (Psychology)</subject><subject>Rehabilitation</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Spelling device</subject><subject>Surveys and Questionnaires</subject><subject>User-Computer Interface</subject><subject>Vertebrates: nervous system and sense organs</subject><subject>Writing</subject><issn>0304-3940</issn><issn>1872-7972</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkM2q1TAQgIMo3uPVNxDJRtFF6yRNmmaj6EG9Fy4oouuQphPIoW1q0ip35zv4hj6JuZ6D7nQxP4tvhpmPkIcMagasfX6oZ9xGXGsOoGtoaxDyFtmxTvFKacVvkx00IKpGCzgj93I-AIBkUtwlZ0xLIVtgO_LxIn6jk52v6YJxGZHaVKIvzRqpi_Oa4kgt_dAAVL3NONA-2TD__P7DxWnZVkw0zCV765A-fb2_fPbyPrnj7Zjxwamek89v33zaX1RX799d7l9dVU50aq28V9YyiawX0OumYRZ456UfOqUa5YSzjgmEnrfYMQXY9oPuvBIdaN0MnWvOyZPj3iXFLxvm1UwhOxxHO2PcsmmVZFwz-V-QAwcpOCugOIIuxZwTerOkMNl0bRiYG-nmYI7SzY10A60p0svYo9P-rZ9w-Dt0slyAxyfAZmdHn-zsQv7DcabKU7-5F0cOi7avAZPJLuDscAgJ3WqGGP59yS_ds6Bz</recordid><startdate>20090901</startdate><enddate>20090901</enddate><creator>Guger, Christoph</creator><creator>Daban, Shahab</creator><creator>Sellers, Eric</creator><creator>Holzner, Clemens</creator><creator>Krausz, Gunther</creator><creator>Carabalona, Roberta</creator><creator>Gramatica, Furio</creator><creator>Edlinger, Guenter</creator><general>Elsevier Ireland Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TK</scope><scope>7X8</scope></search><sort><creationdate>20090901</creationdate><title>How many people are able to control a P300-based brain–computer interface (BCI)?</title><author>Guger, Christoph ; Daban, Shahab ; Sellers, Eric ; Holzner, Clemens ; Krausz, Gunther ; Carabalona, Roberta ; Gramatica, Furio ; Edlinger, Guenter</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c487t-ff7aa15e1b40b9331a028f5fd87737c4cac14e0b26e8170e6bd98f7480993d8c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Adult</topic><topic>Biofeedback, Psychology</topic><topic>Biological and medical sciences</topic><topic>Brain - physiology</topic><topic>Brain–computer interface (BCI)</topic><topic>Electroencephalogram (EEG)</topic><topic>Electroencephalography - methods</topic><topic>Event-Related Potentials, P300</topic><topic>Female</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Humans</topic><topic>Male</topic><topic>Neuropsychological Tests</topic><topic>P300 potential</topic><topic>Practice (Psychology)</topic><topic>Rehabilitation</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Spelling device</topic><topic>Surveys and Questionnaires</topic><topic>User-Computer Interface</topic><topic>Vertebrates: nervous system and sense organs</topic><topic>Writing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Guger, Christoph</creatorcontrib><creatorcontrib>Daban, Shahab</creatorcontrib><creatorcontrib>Sellers, Eric</creatorcontrib><creatorcontrib>Holzner, Clemens</creatorcontrib><creatorcontrib>Krausz, Gunther</creatorcontrib><creatorcontrib>Carabalona, Roberta</creatorcontrib><creatorcontrib>Gramatica, Furio</creatorcontrib><creatorcontrib>Edlinger, Guenter</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Neurosciences Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Neuroscience letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Guger, Christoph</au><au>Daban, Shahab</au><au>Sellers, Eric</au><au>Holzner, Clemens</au><au>Krausz, Gunther</au><au>Carabalona, Roberta</au><au>Gramatica, Furio</au><au>Edlinger, Guenter</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>How many people are able to control a P300-based brain–computer interface (BCI)?</atitle><jtitle>Neuroscience letters</jtitle><addtitle>Neurosci Lett</addtitle><date>2009-09-01</date><risdate>2009</risdate><volume>462</volume><issue>1</issue><spage>94</spage><epage>98</epage><pages>94-98</pages><issn>0304-3940</issn><eissn>1872-7972</eissn><coden>NELED5</coden><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 8 h 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.</abstract><cop>Shannon</cop><pub>Elsevier Ireland Ltd</pub><pmid>19545601</pmid><doi>10.1016/j.neulet.2009.06.045</doi><tpages>5</tpages></addata></record>
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source MEDLINE; Elsevier ScienceDirect Journals Complete
subjects Adult
Biofeedback, Psychology
Biological and medical sciences
Brain - physiology
Brain–computer interface (BCI)
Electroencephalogram (EEG)
Electroencephalography - methods
Event-Related Potentials, P300
Female
Fundamental and applied biological sciences. Psychology
Humans
Male
Neuropsychological Tests
P300 potential
Practice (Psychology)
Rehabilitation
Signal Processing, Computer-Assisted
Spelling device
Surveys and Questionnaires
User-Computer Interface
Vertebrates: nervous system and sense organs
Writing
title How many people are able to control a P300-based brain–computer interface (BCI)?
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