Exploring the feasibility of olfactory brain–computer interfaces
Abstract In this study, we explore the feasibility of single-trial predictions of odor registration in the brain using olfactory bio-signals. We focus on two main aspects: input data modality and the processing model. For the first time, we assess the predictability of odor registration from novel e...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-01488-z |
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| author | Nona Rajabi Irene Zanettin Antônio H. Ribeiro Miguel Vasco Mårten Björkman Johan N. Lundström Danica Kragic |
| author_facet | Nona Rajabi Irene Zanettin Antônio H. Ribeiro Miguel Vasco Mårten Björkman Johan N. Lundström Danica Kragic |
| author_sort | Nona Rajabi |
| collection | DOAJ |
| description | Abstract In this study, we explore the feasibility of single-trial predictions of odor registration in the brain using olfactory bio-signals. We focus on two main aspects: input data modality and the processing model. For the first time, we assess the predictability of odor registration from novel electrobulbogram (EBG) recordings, both in sensor and source space, and compare these with commonly used electroencephalogram (EEG) signals. Despite having fewer data channels, EBG shows comparable performance to EEG. We also examine whether breathing patterns contain relevant information for this task. By comparing a logistic regression classifier, which requires hand-crafted features, with an end-to-end convolutional deep neural network, we find that end-to-end approaches can be as effective as classic methods. However, due to the high dimensionality of the data, the current dataset is insufficient for either classifier to robustly differentiate odor and non-odor trials. Finally, we identify key challenges in olfactory BCIs and suggest future directions for improving odor detection systems. |
| format | Article |
| id | doaj-art-3744a1992ee744efabf063266c2156ff |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3744a1992ee744efabf063266c2156ff2025-08-20T03:16:31ZengNature PortfolioScientific Reports2045-23222025-05-0115111310.1038/s41598-025-01488-zExploring the feasibility of olfactory brain–computer interfacesNona Rajabi0Irene Zanettin1Antônio H. Ribeiro2Miguel Vasco3Mårten Björkman4Johan N. Lundström5Danica Kragic6Department of Intelligent Systems, KTH Royal Institute of TechnologyDepartment of Clinical Neuroscience, Karolinska InstituteDepartment of Information Technology, Uppsala UniversityDepartment of Intelligent Systems, KTH Royal Institute of TechnologyDepartment of Intelligent Systems, KTH Royal Institute of TechnologyDepartment of Clinical Neuroscience, Karolinska InstituteDepartment of Intelligent Systems, KTH Royal Institute of TechnologyAbstract In this study, we explore the feasibility of single-trial predictions of odor registration in the brain using olfactory bio-signals. We focus on two main aspects: input data modality and the processing model. For the first time, we assess the predictability of odor registration from novel electrobulbogram (EBG) recordings, both in sensor and source space, and compare these with commonly used electroencephalogram (EEG) signals. Despite having fewer data channels, EBG shows comparable performance to EEG. We also examine whether breathing patterns contain relevant information for this task. By comparing a logistic regression classifier, which requires hand-crafted features, with an end-to-end convolutional deep neural network, we find that end-to-end approaches can be as effective as classic methods. However, due to the high dimensionality of the data, the current dataset is insufficient for either classifier to robustly differentiate odor and non-odor trials. Finally, we identify key challenges in olfactory BCIs and suggest future directions for improving odor detection systems.https://doi.org/10.1038/s41598-025-01488-z |
| spellingShingle | Nona Rajabi Irene Zanettin Antônio H. Ribeiro Miguel Vasco Mårten Björkman Johan N. Lundström Danica Kragic Exploring the feasibility of olfactory brain–computer interfaces Scientific Reports |
| title | Exploring the feasibility of olfactory brain–computer interfaces |
| title_full | Exploring the feasibility of olfactory brain–computer interfaces |
| title_fullStr | Exploring the feasibility of olfactory brain–computer interfaces |
| title_full_unstemmed | Exploring the feasibility of olfactory brain–computer interfaces |
| title_short | Exploring the feasibility of olfactory brain–computer interfaces |
| title_sort | exploring the feasibility of olfactory brain computer interfaces |
| url | https://doi.org/10.1038/s41598-025-01488-z |
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