Classification of Targets and Distractors in an Audiovisual Attention Task Based on Electroencephalography Article Swipe
Steven Mortier
,
Renata Turkeš
,
Jorg De Winne
,
Wannes Van Ransbeeck
,
Dick Botteldooren
,
Paul Devos
,
Steven Latré
,
Marc Leman
,
Tim Verdonck
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23239588
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23239588
Within the broader context of improving interactions between artificial intelligence and humans, the question has arisen regarding whether auditory and rhythmic support could increase attention for visual stimuli that do not stand out clearly from an information stream. To this end, we designed an experiment inspired by pip-and-pop but more appropriate for eliciting attention and P3a-event-related potentials (ERPs). In this study, the aim was to distinguish between targets and distractors based on the subject’s electroencephalography (EEG) data. We achieved this objective by employing different machine learning (ML) methods for both individual-subject (IS) and cross-subject (CS) models. Finally, we investigated which EEG channels and time points were used by the model to make its predictions using saliency maps. We were able to successfully perform the aforementioned classification task for both the IS and CS scenarios, reaching classification accuracies up to 76%. In accordance with the literature, the model primarily used the parietal–occipital electrodes between 200 ms and 300 ms after the stimulus to make its prediction. The findings from this research contribute to the development of more effective P300-based brain–computer interfaces. Furthermore, they validate the EEG data collected in our experiment.
Related Topics
Concepts
Electroencephalography
P3a
Computer science
Context (archaeology)
Stimulus (psychology)
Event-related potential
Task (project management)
Artificial intelligence
Oddball paradigm
Brain–computer interface
Speech recognition
Cognitive psychology
Psychology
Neuroscience
Management
Paleontology
Economics
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23239588
- https://www.mdpi.com/1424-8220/23/23/9588/pdf?version=1701592220
- OA Status
- gold
- Cited By
- 5
- References
- 79
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389286248
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389286248Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s23239588Digital Object Identifier
- Title
-
Classification of Targets and Distractors in an Audiovisual Attention Task Based on ElectroencephalographyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-03Full publication date if available
- Authors
-
Steven Mortier, Renata Turkeš, Jorg De Winne, Wannes Van Ransbeeck, Dick Botteldooren, Paul Devos, Steven Latré, Marc Leman, Tim VerdonckList of authors in order
- Landing page
-
https://doi.org/10.3390/s23239588Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/23/23/9588/pdf?version=1701592220Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/23/23/9588/pdf?version=1701592220Direct OA link when available
- Concepts
-
Electroencephalography, P3a, Computer science, Context (archaeology), Stimulus (psychology), Event-related potential, Task (project management), Artificial intelligence, Oddball paradigm, Brain–computer interface, Speech recognition, Cognitive psychology, Psychology, Neuroscience, Management, Paleontology, Economics, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
79Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.maps. | 116 |
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| abstract_inverted_index.study, | 60 |
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| abstract_inverted_index.(ERPs). | 57 |
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| abstract_inverted_index.methods | 87 |
| abstract_inverted_index.models. | 95 |
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| abstract_inverted_index.attention | 24, 53 |
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| abstract_inverted_index.electroencephalography | 74 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5082936710 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I149213910 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5600000023841858 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.77425405 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |