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Optimising the classification of feature-based attention in frequency-tagged electroencephalography data

The University of Queensland
Dr David Lloyd (Aggregated by) Dr David Painter (Aggregated by) Miss Angela Renton (Aggregated by) Miss Angela Renton (Aggregated by) Professor Jason Mattingley (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.14264/ed3c0c9&rft.title=Optimising the classification of feature-based attention in frequency-tagged electroencephalography data&rft.identifier=RDM ID: c9b17d20-9393-11eb-ad56-01a50e313e64&rft.publisher=The University of Queensland&rft.description=The data repository for the feature-based attention classification dataset contains four top level folders. Top level folders include “FeatAttnClassification\ExperimentalTask\”, which contains the MATLAB code used to run the experimental task, “FeatAttnClassification\Data\”, which contains all EEG and behavioural data, “FeatAttnClassification\AnalysisScripts\”, which contains the code used for technical validation, and “FeatAttnClassification\Results\”, which contains the files output by the analysis scripts. The data folder follows the BIDS specification for folder hierarchy. Critical information regarding the experimental task parameters, display settings, EEG recording settings and triggers is contained in the file “FeatAttnClassification\Data\helperdata.mat”.&rft.creator=Dr David Lloyd&rft.creator=Dr David Painter&rft.creator=Miss Angela Renton&rft.creator=Miss Angela Renton&rft.creator=Professor Jason Mattingley&rft.creator=Professor Jason Mattingley&rft.date=2021&rft_rights=The University of Queensland&rft_rights= https://guides.library.uq.edu.au/deposit-your-data/license-reuse-data-agreement&rft_subject=eng&rft_subject=electroencephalography&rft_subject=Biological Psychology (Neuropsychology, Psychopharmacology, Physiological Psychology)&rft_subject=PSYCHOLOGY AND COGNITIVE SCIENCES&rft_subject=PSYCHOLOGY&rft_subject=Knowledge Representation and Machine Learning&rft_subject=COGNITIVE SCIENCE&rft.type=dataset&rft.language=English Access the data
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a.renton@uq.edu.au
Queensland Brain Institute

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The data repository for the feature-based attention classification dataset contains four top level folders. Top level folders include “FeatAttnClassification\ExperimentalTask\”, which contains the MATLAB code used to run the experimental task, “FeatAttnClassification\Data\”, which contains all EEG and behavioural data, “FeatAttnClassification\AnalysisScripts\”, which contains the code used for technical validation, and “FeatAttnClassification\Results\”, which contains the files output by the analysis scripts. The data folder follows the BIDS specification for folder hierarchy. Critical information regarding the experimental task parameters, display settings, EEG recording settings and triggers is contained in the file “FeatAttnClassification\Data\helperdata.mat”.

Issued: 2021

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Subjects
Biological Psychology (Neuropsychology, Psychopharmacology, Physiological Psychology) | Cognitive Science | Knowledge Representation and Machine Learning | Psychology | Psychology and Cognitive Sciences | electroencephalography | eng |

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Optimising the classification of feature-based attention in frequency-tagged electroencephalography data

local : UQ:72841bd

Renton, Angela I., Painter, David R. and Mattingley, Jason B. (2022). Optimising the classification of feature-based attention in frequency-tagged electroencephalography data. Scientific Data, 9 (1) 296, 296. doi: 10.1038/s41597-022-01398-z

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local : UQ:289097

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