Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imagery
Abstract In cognitive neuroscience and brain-computer interface research, accurately predicting imagined stimuli is crucial. This study investigates the effectiveness of Domain Adaptation (DA) in enhancing imagery prediction using primarily visual data from fMRI scans of 18 subjects. Initially, we t...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-06-01
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| Series: | Brain Informatics |
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| Online Access: | https://doi.org/10.1186/s40708-025-00263-0 |
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| author | Alexander Olza David Soto Roberto Santana |
| author_facet | Alexander Olza David Soto Roberto Santana |
| author_sort | Alexander Olza |
| collection | DOAJ |
| description | Abstract In cognitive neuroscience and brain-computer interface research, accurately predicting imagined stimuli is crucial. This study investigates the effectiveness of Domain Adaptation (DA) in enhancing imagery prediction using primarily visual data from fMRI scans of 18 subjects. Initially, we train a baseline model on visual stimuli to predict imagined stimuli, utilizing data from 14 brain regions. We then develop several models to improve imagery prediction, comparing different DA methods. Our results demonstrate that DA significantly enhances imagery prediction in binary classification on our dataset, as well as in multiclass classification on a publicly available dataset. We then conduct a DA-enhanced searchlight analysis, followed by permutation-based statistical tests to identify brain regions where imagery decoding is consistently above chance across subjects. Our DA-enhanced searchlight predicts imagery contents in a highly distributed set of brain regions, including the visual cortex and the frontoparietal cortex, thereby outperforming standard cross-domain classification methods. The complete code and data for this paper have been made openly available for the use of the scientific community. |
| format | Article |
| id | doaj-art-9c81da13fa8442d5b41b3f18d75c92ae |
| institution | OA Journals |
| issn | 2198-4018 2198-4026 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Brain Informatics |
| spelling | doaj-art-9c81da13fa8442d5b41b3f18d75c92ae2025-08-20T02:37:57ZengSpringerOpenBrain Informatics2198-40182198-40262025-06-0112111710.1186/s40708-025-00263-0Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imageryAlexander Olza0David Soto1Roberto Santana2Intelligent Systems Group, University of the Basque Country (UPV/EHU)Consciousness Group, Basque Center for Cognition, Brain and Language (BCBL)Intelligent Systems Group, University of the Basque Country (UPV/EHU)Abstract In cognitive neuroscience and brain-computer interface research, accurately predicting imagined stimuli is crucial. This study investigates the effectiveness of Domain Adaptation (DA) in enhancing imagery prediction using primarily visual data from fMRI scans of 18 subjects. Initially, we train a baseline model on visual stimuli to predict imagined stimuli, utilizing data from 14 brain regions. We then develop several models to improve imagery prediction, comparing different DA methods. Our results demonstrate that DA significantly enhances imagery prediction in binary classification on our dataset, as well as in multiclass classification on a publicly available dataset. We then conduct a DA-enhanced searchlight analysis, followed by permutation-based statistical tests to identify brain regions where imagery decoding is consistently above chance across subjects. Our DA-enhanced searchlight predicts imagery contents in a highly distributed set of brain regions, including the visual cortex and the frontoparietal cortex, thereby outperforming standard cross-domain classification methods. The complete code and data for this paper have been made openly available for the use of the scientific community.https://doi.org/10.1186/s40708-025-00263-0Domain AdaptationBrain decodingFMRISearchlight |
| spellingShingle | Alexander Olza David Soto Roberto Santana Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imagery Brain Informatics Domain Adaptation Brain decoding FMRI Searchlight |
| title | Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imagery |
| title_full | Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imagery |
| title_fullStr | Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imagery |
| title_full_unstemmed | Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imagery |
| title_short | Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imagery |
| title_sort | domain adaptation enhanced searchlight enabling classification of brain states from visual perception to mental imagery |
| topic | Domain Adaptation Brain decoding FMRI Searchlight |
| url | https://doi.org/10.1186/s40708-025-00263-0 |
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