Multimodal Neural Network for Rapid Serial Visual Presentation Brain Computer Interface
Brain computer interfaces allow users to preform various tasks using only the electrical activity of the brain. BCI applications often present the user a set of stimuli and record the corresponding electrical response. The BCI algorithm will then have to decode the acquired brain response and perfor...
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| Published in | Frontiers in computational neuroscience Vol. 10; p. 130 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
Frontiers Research Foundation
20.12.2016
Frontiers Media S.A |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1662-5188 1662-5188 |
| DOI | 10.3389/fncom.2016.00130 |
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| Abstract | Brain computer interfaces allow users to preform various tasks using only the electrical activity of the brain. BCI applications often present the user a set of stimuli and record the corresponding electrical response. The BCI algorithm will then have to decode the acquired brain response and perform the desired task. In rapid serial visual presentation (RSVP) tasks, the subject is presented with a continuous stream of images containing rare target images among standard images, while the algorithm has to detect brain activity associated with target images. In this work, we suggest a multimodal neural network for RSVP tasks. The network operates on the brain response and on the initiating stimulus simultaneously, providing more information for the BCI application. We present two variants of the multimodal network, a supervised model, for the case when the targets are known in advanced, and a semi-supervised model for when the targets are unknown. We test the neural networks with a RSVP experiment on satellite imagery carried out with two subjects. The multimodal networks achieve a significant performance improvement in classification metrics. We visualize what the networks has learned and discuss the advantages of using neural network models for BCI applications. |
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| AbstractList | Brain computer interfaces allow users to preform various tasks using only the electrical activity of the brain. BCI applications often present the user a set of stimuli and record the corresponding electrical response. The BCI algorithm will then have to decode the acquired brain response and perform the desired task. In rapid serial visual presentation (RSVP) tasks, the subject is presented with a continuous stream of images containing rare target images among standard images, while the algorithm has to detect brain activity associated with target images. In this work, we suggest a multimodal neural network for RSVP tasks. The network operates on the brain response and on the initiating stimulus simultaneously, providing more information for the BCI application. We present two variants of the multimodal network, a supervised model, for the case when the targets are known in advanced, and a semi-supervised model for when the targets are unknown. We test the neural networks with a RSVP experiment on satellite imagery carried out with two subjects. The multimodal networks achieve a significant performance improvement in classification metrics. We visualize what the networks has learned and discuss the advantages of using neural network models for BCI applications. Brain computer interfaces allow users to preform various tasks using only the electrical activity of the brain. BCI applications often present the user a set of stimuli and record the corresponding electrical response. The BCI algorithm will then have to decode the acquired brain response and perform the desired task. In rapid serial visual presentation (RSVP) tasks, the subject is presented with a continuous stream of images containing rare target images among standard images, while the algorithm has to detect brain activity associated with target images. In this work, we suggest a multimodal neural network for RSVP tasks. The network operates on the brain response and on the initiating stimulus simultaneously, providing more information for the BCI application. We present two variants of the multimodal network, a supervised model, for the case when the targets are known in advanced, and a semi-supervised model for when the targets are unknown. We test the neural networks with a RSVP experiment on satellite imagery carried out with two subjects. The multimodal networks achieve a significant performance improvement in classification metrics. We visualize what the networks has learned and discuss the advantages of using neural network models for BCI applications.Brain computer interfaces allow users to preform various tasks using only the electrical activity of the brain. BCI applications often present the user a set of stimuli and record the corresponding electrical response. The BCI algorithm will then have to decode the acquired brain response and perform the desired task. In rapid serial visual presentation (RSVP) tasks, the subject is presented with a continuous stream of images containing rare target images among standard images, while the algorithm has to detect brain activity associated with target images. In this work, we suggest a multimodal neural network for RSVP tasks. The network operates on the brain response and on the initiating stimulus simultaneously, providing more information for the BCI application. We present two variants of the multimodal network, a supervised model, for the case when the targets are known in advanced, and a semi-supervised model for when the targets are unknown. We test the neural networks with a RSVP experiment on satellite imagery carried out with two subjects. The multimodal networks achieve a significant performance improvement in classification metrics. We visualize what the networks has learned and discuss the advantages of using neural network models for BCI applications. |
| Author | Manor, Ran Mishali, Liran Geva, Amir B. |
| AuthorAffiliation | 2 Department of Electrical and Computer Engineering, Tel Aviv University Tel-Aviv, Israel 1 Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev Beer-Sheva, Israel |
| AuthorAffiliation_xml | – name: 2 Department of Electrical and Computer Engineering, Tel Aviv University Tel-Aviv, Israel – name: 1 Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev Beer-Sheva, Israel |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28066220$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1145/2449396.2449415 10.1109/CVPR.2008.4587618 10.1109/TPAMI.2012.59 10.1016/0041-5553(64)90137-5 10.1109/TNSRE.2008.2003381 10.3389/fncom.2015.00146 10.1109/TNSRE.2003.819785 10.1109/TPAMI.2010.125 10.1016/j.jneumeth.2003.10.009 10.1109/ISCAS.2010.5537907 10.2307/3001968 10.1088/1741-2560/8/2/025005 10.1109/MSP.2008.4408441 10.1080/01621459.1952.10483441 10.1109/86.712230 10.1007/s00530-010-0182-0 10.1088/1741-2560/8/3/036025 10.1016/S1388-2457(98)00038-8 10.1109/TNSRE.2006.875550 10.1109/JPROC.2009.2038406 10.1109/tbme.2004.826698 10.1109/MSP.2012.2205597 10.1016/j.clinph.2007.04.019 10.1109/86.847808 10.1109/MSP.2008.4408447 10.1109/TBME.2013.2289898 10.1109/TNSRE.2003.814484 10.1080/01621459.1961.10482090 10.1088/1741-2560/4/2/R01 10.5281/zenodo.27878 10.1088/1741-2560/3/1/L01 |
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| Copyright | 2016. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2016 Manor, Mishali and Geva. 2016 Manor, Mishali and Geva |
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| Keywords | deep learning EEG computer vision single trial BCI RSVP neural network |
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| Title | Multimodal Neural Network for Rapid Serial Visual Presentation Brain Computer Interface |
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