A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity.
Brain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its st...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2019-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0207351 |
| _version_ | 1818571894348054528 |
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| author | Claudia Sannelli Carmen Vidaurre Klaus-Robert Müller Benjamin Blankertz |
| author_facet | Claudia Sannelli Carmen Vidaurre Klaus-Robert Müller Benjamin Blankertz |
| author_sort | Claudia Sannelli |
| collection | DOAJ |
| description | Brain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one BCI session with resting state Encephalography, Motor Observation, Motor Execution and Motor Imagery recordings and 128 electrodes. A significant portion of the participants (40%) could not achieve BCI control (feedback performance > 70%). Based on the performance of the calibration and feedback runs, BCI users were stratified in three groups. Analyses directed to detect and elucidate the differences in the SMR activity of these groups were performed. Statistics on reactive frequencies, task prevalence and classification results are reported. Based on their SMR activity, also a systematic list of potential reasons leading to performance drops and thus hints for possible improvements of BCI experimental design are given. The categorization of BCI users has several advantages, allowing researchers 1) to select subjects for further analyses as well as for testing new BCI paradigms or algorithms, 2) to adopt a better subject-dependent training strategy and 3) easier comparisons between different studies. |
| first_indexed | 2024-12-14T18:50:17Z |
| format | Article |
| id | doaj.art-add28036c879477ca1fcc3d1421fd4a6 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-14T18:50:17Z |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-add28036c879477ca1fcc3d1421fd4a62022-12-21T22:51:17ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01141e020735110.1371/journal.pone.0207351A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity.Claudia SannelliCarmen VidaurreKlaus-Robert MüllerBenjamin BlankertzBrain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one BCI session with resting state Encephalography, Motor Observation, Motor Execution and Motor Imagery recordings and 128 electrodes. A significant portion of the participants (40%) could not achieve BCI control (feedback performance > 70%). Based on the performance of the calibration and feedback runs, BCI users were stratified in three groups. Analyses directed to detect and elucidate the differences in the SMR activity of these groups were performed. Statistics on reactive frequencies, task prevalence and classification results are reported. Based on their SMR activity, also a systematic list of potential reasons leading to performance drops and thus hints for possible improvements of BCI experimental design are given. The categorization of BCI users has several advantages, allowing researchers 1) to select subjects for further analyses as well as for testing new BCI paradigms or algorithms, 2) to adopt a better subject-dependent training strategy and 3) easier comparisons between different studies.https://doi.org/10.1371/journal.pone.0207351 |
| spellingShingle | Claudia Sannelli Carmen Vidaurre Klaus-Robert Müller Benjamin Blankertz A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity. PLoS ONE |
| title | A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity. |
| title_full | A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity. |
| title_fullStr | A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity. |
| title_full_unstemmed | A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity. |
| title_short | A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity. |
| title_sort | large scale screening study with a smr based bci categorization of bci users and differences in their smr activity |
| url | https://doi.org/10.1371/journal.pone.0207351 |
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