Deep learning-based EEG analysis: investigating P3 ERP components
The neural processing of incoming stimuli can be analysed from the electroencephalogram (EEG) through event-related potentials (ERPs). The P3 component is largely investigated as it represents an important psychophysiological marker of psychiatric disorders. This is composed by several subcompon...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IMR Press
2021-12-01
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| Series: | Journal of Integrative Neuroscience |
| Subjects: | |
| Online Access: | https://www.imrpress.com/journal/JIN/20/4/10.31083/j.jin2004083 |
| _version_ | 1818538681530580992 |
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| author | Davide Borra Elisa Magosso |
| author_facet | Davide Borra Elisa Magosso |
| author_sort | Davide Borra |
| collection | DOAJ |
| description | The neural processing of incoming stimuli can be analysed from the
electroencephalogram (EEG) through event-related potentials (ERPs). The P3
component is largely investigated as it represents an important
psychophysiological marker of psychiatric disorders. This is composed by several
subcomponents, such as P3a and P3b, reflecting distinct but interrelated sensory
and cognitive processes of incoming stimuli. Due to the low EEG
signal-to-noise-ratio, ERPs emerge only after an averaging procedure across
trials and subjects. Thus, this canonical ERP analysis lacks in the ability to
highlight EEG neural signatures at the level of single-subject and single-trial.
In this study, a deep learning-based workflow is investigated to enhance EEG
neural signatures related to P3 subcomponents already at single-subject and at
single-trial level. This was based on the combination of a convolutional neural
network (CNN) with an explanation technique (ET). The CNN was trained using two
different strategies to produce saliency representations enhancing signatures
shared across subjects or more specific for each subject and trial. Cross-subject
saliency representations matched the signatures already emerging from ERPs, i.e.,
P3a and P3b-related activity within 350–400 ms (frontal sites) and 400–650 ms
(parietal sites) post-stimulus, validating the CNN+ET respect to canonical ERP
analysis. Single-subject and single-trial saliency representations enhanced P3
signatures already at the single-trial scale, while EEG-derived representations
at single-subject and single-trial level provided no or only mildly evident
signatures. Empowering the analysis of P3 modulations at single-subject and at
single-trial level, CNN+ET could be useful to provide insights about neural
processes linking sensory stimulation, cognition and behaviour. |
| first_indexed | 2024-12-11T21:32:06Z |
| format | Article |
| id | doaj.art-5789d5a5674a4ca3863a9fdf341fff46 |
| institution | Directory Open Access Journal |
| issn | 1757-448X |
| language | English |
| last_indexed | 2024-12-11T21:32:06Z |
| publishDate | 2021-12-01 |
| publisher | IMR Press |
| record_format | Article |
| series | Journal of Integrative Neuroscience |
| spelling | doaj.art-5789d5a5674a4ca3863a9fdf341fff462022-12-22T00:50:08ZengIMR PressJournal of Integrative Neuroscience1757-448X2021-12-0120479181110.31083/j.jin2004083S0219-6352(21)00231-XDeep learning-based EEG analysis: investigating P3 ERP componentsDavide Borra0Elisa Magosso1Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi” (DEI), University of Bologna, Cesena Campus, 47522 Cesena, ItalyDepartment of Electrical, Electronic and Information Engineering “Guglielmo Marconi” (DEI), University of Bologna, Cesena Campus, 47522 Cesena, ItalyThe neural processing of incoming stimuli can be analysed from the electroencephalogram (EEG) through event-related potentials (ERPs). The P3 component is largely investigated as it represents an important psychophysiological marker of psychiatric disorders. This is composed by several subcomponents, such as P3a and P3b, reflecting distinct but interrelated sensory and cognitive processes of incoming stimuli. Due to the low EEG signal-to-noise-ratio, ERPs emerge only after an averaging procedure across trials and subjects. Thus, this canonical ERP analysis lacks in the ability to highlight EEG neural signatures at the level of single-subject and single-trial. In this study, a deep learning-based workflow is investigated to enhance EEG neural signatures related to P3 subcomponents already at single-subject and at single-trial level. This was based on the combination of a convolutional neural network (CNN) with an explanation technique (ET). The CNN was trained using two different strategies to produce saliency representations enhancing signatures shared across subjects or more specific for each subject and trial. Cross-subject saliency representations matched the signatures already emerging from ERPs, i.e., P3a and P3b-related activity within 350–400 ms (frontal sites) and 400–650 ms (parietal sites) post-stimulus, validating the CNN+ET respect to canonical ERP analysis. Single-subject and single-trial saliency representations enhanced P3 signatures already at the single-trial scale, while EEG-derived representations at single-subject and single-trial level provided no or only mildly evident signatures. Empowering the analysis of P3 modulations at single-subject and at single-trial level, CNN+ET could be useful to provide insights about neural processes linking sensory stimulation, cognition and behaviour.https://www.imrpress.com/journal/JIN/20/4/10.31083/j.jin2004083electroencephalographyp3ap3bconvolutional neural networksdecision explanation |
| spellingShingle | Davide Borra Elisa Magosso Deep learning-based EEG analysis: investigating P3 ERP components Journal of Integrative Neuroscience electroencephalography p3a p3b convolutional neural networks decision explanation |
| title | Deep learning-based EEG analysis: investigating P3 ERP components |
| title_full | Deep learning-based EEG analysis: investigating P3 ERP components |
| title_fullStr | Deep learning-based EEG analysis: investigating P3 ERP components |
| title_full_unstemmed | Deep learning-based EEG analysis: investigating P3 ERP components |
| title_short | Deep learning-based EEG analysis: investigating P3 ERP components |
| title_sort | deep learning based eeg analysis investigating p3 erp components |
| topic | electroencephalography p3a p3b convolutional neural networks decision explanation |
| url | https://www.imrpress.com/journal/JIN/20/4/10.31083/j.jin2004083 |
| work_keys_str_mv | AT davideborra deeplearningbasedeeganalysisinvestigatingp3erpcomponents AT elisamagosso deeplearningbasedeeganalysisinvestigatingp3erpcomponents |