Attention modulations of posterior alpha as a control signal for two-dimensional brain–computer interfaces
Research on brain–computer interfaces (BCIs) is gaining strong interest. This is motivated by BCIs being applicable for helping disabled, for gaming, and as a tool in cognitive neuroscience. Often, motor imagery is used to produce (binary) control signals. However, finding other types of control sig...
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Published in | Journal of neuroscience methods Vol. 179; no. 1; pp. 78 - 84 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Netherlands
Elsevier B.V
30.04.2009
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Subjects | |
Online Access | Get full text |
ISSN | 0165-0270 1872-678X 1872-678X |
DOI | 10.1016/j.jneumeth.2009.01.016 |
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Abstract | Research on brain–computer interfaces (BCIs) is gaining strong interest. This is motivated by BCIs being applicable for helping disabled, for gaming, and as a tool in cognitive neuroscience. Often, motor imagery is used to produce (binary) control signals. However, finding other types of control signals that allow the discrimination of multiple classes would help to increase the applicability of BCIs. We have investigated if modulation of posterior alpha activity by means of covert spatial attention in two dimensions can be reliably classified in single trials. Magnetoencephalography (MEG) data were collected for 15 subjects who were engaged in a task where they covertly had to visually attend left, right, up or down during a period of 2500 ms. We then classified the trials using support vector machines. The four orientations of covert attention could be reliably classified up to a maximum of 69% correctly classified trials (25% chance level) without the need for lengthy and burdensome subject training. Low classification performance in some subjects was explained by a low alpha signal. These findings support the case that modulation of alpha activity by means of covert spatial attention is promising as a control signal for a two-dimensional BCI. |
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AbstractList | Research on brain-computer interfaces (BCIs) is gaining strong interest. This is motivated by BCIs being applicable for helping disabled, for gaming, and as a tool in cognitive neuroscience. Often, motor imagery is used to produce (binary) control signals. However, finding other types of control signals that allow the discrimination of multiple classes would help to increase the applicability of BCIs. We have investigated if modulation of posterior alpha activity by means of covert spatial attention in two dimensions can be reliably classified in single trials. Magnetoencephalography (MEG) data were collected for 15 subjects who were engaged in a task where they covertly had to visually attend left, right, up or down during a period of 2500 ms. We then classified the trials using support vector machines. The four orientations of covert attention could be reliably classified up to a maximum of 69% correctly classified trials (25% chance level) without the need for lengthy and burdensome subject training. Low classification performance in some subjects was explained by a low alpha signal. These findings support the case that modulation of alpha activity by means of covert spatial attention is promising as a control signal for a two-dimensional BCI. Research on brain-computer interfaces (BCIs) is gaining strong interest. This is motivated by BCIs being applicable for helping disabled, for gaming, and as a tool in cognitive neuroscience. Often, motor imagery is used to produce (binary) control signals. However, finding other types of control signals that allow the discrimination of multiple classes would help to increase the applicability of BCIs. We have investigated if modulation of posterior alpha activity by means of covert spatial attention in two dimensions can be reliably classified in single trials. Magnetoencephalography (MEG) data were collected for 15 subjects who were engaged in a task where they covertly had to visually attend left, right, up or down during a period of 2500 ms. We then classified the trials using support vector machines. The four orientations of covert attention could be reliably classified up to a maximum of 69% correctly classified trials (25% chance level) without the need for lengthy and burdensome subject training. Low classification performance in some subjects was explained by a low alpha signal. These findings support the case that modulation of alpha activity by means of covert spatial attention is promising as a control signal for a two-dimensional BCI.Research on brain-computer interfaces (BCIs) is gaining strong interest. This is motivated by BCIs being applicable for helping disabled, for gaming, and as a tool in cognitive neuroscience. Often, motor imagery is used to produce (binary) control signals. However, finding other types of control signals that allow the discrimination of multiple classes would help to increase the applicability of BCIs. We have investigated if modulation of posterior alpha activity by means of covert spatial attention in two dimensions can be reliably classified in single trials. Magnetoencephalography (MEG) data were collected for 15 subjects who were engaged in a task where they covertly had to visually attend left, right, up or down during a period of 2500 ms. We then classified the trials using support vector machines. The four orientations of covert attention could be reliably classified up to a maximum of 69% correctly classified trials (25% chance level) without the need for lengthy and burdensome subject training. Low classification performance in some subjects was explained by a low alpha signal. These findings support the case that modulation of alpha activity by means of covert spatial attention is promising as a control signal for a two-dimensional BCI. |
Author | Jensen, Ole van Gerven, Marcel |
Author_xml | – sequence: 1 givenname: Marcel surname: van Gerven fullname: van Gerven, Marcel email: marcel.van.gerven@gmail.com, marcelge@cs.ru.nl organization: Institute for Computing and Information Sciences, Intelligent Systems Group, Radboud University Nijmegen, Nijmegen, The Netherlands – sequence: 2 givenname: Ole surname: Jensen fullname: Jensen, Ole organization: Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University Nijmegen, Nijmegen, The Netherlands |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19428515$$D View this record in MEDLINE/PubMed |
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Snippet | Research on brain–computer interfaces (BCIs) is gaining strong interest. This is motivated by BCIs being applicable for helping disabled, for gaming, and as a... Research on brain-computer interfaces (BCIs) is gaining strong interest. This is motivated by BCIs being applicable for helping disabled, for gaming, and as a... |
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SubjectTerms | Adult Alpha Rhythm Attention - physiology Brain - physiology Brain–computer interface Covert spatial attention Female Humans Magnetoencephalography Male Man-Machine Systems Space Perception - physiology Spatial Behavior - physiology Support vector machine User-Computer Interface |
Title | Attention modulations of posterior alpha as a control signal for two-dimensional brain–computer interfaces |
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