Loading…
Fusion with EEG signals and Images for closed or open eyes detection using deep learning
EEG technology has been used in many areas and systems such as games and electric wheelchairs. Despite their maturity and the availability of low-cost easy-to-use EEG sensors, the risk of error command still remains. To tackle this problem, merging EEG sensor data with another sensor can be an inter...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Conference Proceeding |
| Language: | English |
| Subjects: | |
| Online Access: | Request full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | |
|---|---|
| cites | |
| container_end_page | 5 |
| container_issue | |
| container_start_page | 1 |
| container_title | |
| container_volume | |
| creator | Zaway, Lassaad Ben Amor, Nader Ktari, Jalel Jallouli, Mohamed Chrifi-Alaoui, Larbi Delahoche, Laurent |
| description | EEG technology has been used in many areas and systems such as games and electric wheelchairs. Despite their maturity and the availability of low-cost easy-to-use EEG sensors, the risk of error command still remains. To tackle this problem, merging EEG sensor data with another sensor can be an interesting solution. In this work, we address this hypothesis by merging EEG-based blink detection with facial images captured by cameras using a deep learning algorithm, enabling the simultaneous analysis of both neural and visual aspects of blinks. This fusion not only enhances detection accuracy but also allows for assessing blink-related cognitive workload and emotional responses in real time. |
| doi_str_mv | 10.1109/DTTIS59576.2023.10348220 |
| format | conference_proceeding |
| fullrecord | <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_10348220</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10348220</ieee_id><sourcerecordid>10348220</sourcerecordid><originalsourceid>FETCH-LOGICAL-h153t-31c4743ffd3d2dcba48086a3a8752446f02be02cdbd22099f202a23f3c2d1a333</originalsourceid><addsrcrecordid>eNo1kN1Kw0AQhVdBsNS-gRf7AqmzM_nZvZTa1kDBCyv0rmyys2mkTUo2In17V6xXM-cc5oMzQkgFc6XAPL1st-V7ZrIinyMgzRVQqhHhRsxMYTRlQCqP8a2YoCZMNNLuXsxC-ASIkQFAMxG71Vdo-05-t-NBLpdrGdqms8cgbedkebINB-n7QdbHPrCTcevP3Em-RN_xyPX4ex0ZXRM1n-WR7dBF9SDufOTw7Dqn4mO13C5ek83bulw8b5KDymhMSNVpkZL3jhy6urKpBp1bsrrIME1zD1gxYO0qF7sZ42NXi-SpRqcsEU3F4x-3Zeb9eWhPdrjs_59BP9jQU_o</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Fusion with EEG signals and Images for closed or open eyes detection using deep learning</title><source>IEEE Xplore All Conference Series</source><creator>Zaway, Lassaad ; Ben Amor, Nader ; Ktari, Jalel ; Jallouli, Mohamed ; Chrifi-Alaoui, Larbi ; Delahoche, Laurent</creator><creatorcontrib>Zaway, Lassaad ; Ben Amor, Nader ; Ktari, Jalel ; Jallouli, Mohamed ; Chrifi-Alaoui, Larbi ; Delahoche, Laurent</creatorcontrib><description>EEG technology has been used in many areas and systems such as games and electric wheelchairs. Despite their maturity and the availability of low-cost easy-to-use EEG sensors, the risk of error command still remains. To tackle this problem, merging EEG sensor data with another sensor can be an interesting solution. In this work, we address this hypothesis by merging EEG-based blink detection with facial images captured by cameras using a deep learning algorithm, enabling the simultaneous analysis of both neural and visual aspects of blinks. This fusion not only enhances detection accuracy but also allows for assessing blink-related cognitive workload and emotional responses in real time.</description><identifier>EISSN: 2832-823X</identifier><identifier>EISBN: 9798350316957</identifier><identifier>DOI: 10.1109/DTTIS59576.2023.10348220</identifier><language>eng</language><publisher>IEEE</publisher><subject>Blink detection ; Convolutional Neural Network (CNN) ; Deep learning ; Electroencephalogram (EEG) ; Electroencephalography ; Games ; Long-Short Term Memory (LSTM) ; Merging ; Neural networks ; Visualization ; Wheelchairs</subject><ispartof>2023 IEEE International Conference on Design, Test and Technology of Integrated Systems (DTTIS), 2023, p.1-5</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10348220$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,27902,54530,54907</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10348220$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zaway, Lassaad</creatorcontrib><creatorcontrib>Ben Amor, Nader</creatorcontrib><creatorcontrib>Ktari, Jalel</creatorcontrib><creatorcontrib>Jallouli, Mohamed</creatorcontrib><creatorcontrib>Chrifi-Alaoui, Larbi</creatorcontrib><creatorcontrib>Delahoche, Laurent</creatorcontrib><title>Fusion with EEG signals and Images for closed or open eyes detection using deep learning</title><title>2023 IEEE International Conference on Design, Test and Technology of Integrated Systems (DTTIS)</title><addtitle>DTTIS</addtitle><description>EEG technology has been used in many areas and systems such as games and electric wheelchairs. Despite their maturity and the availability of low-cost easy-to-use EEG sensors, the risk of error command still remains. To tackle this problem, merging EEG sensor data with another sensor can be an interesting solution. In this work, we address this hypothesis by merging EEG-based blink detection with facial images captured by cameras using a deep learning algorithm, enabling the simultaneous analysis of both neural and visual aspects of blinks. This fusion not only enhances detection accuracy but also allows for assessing blink-related cognitive workload and emotional responses in real time.</description><subject>Blink detection</subject><subject>Convolutional Neural Network (CNN)</subject><subject>Deep learning</subject><subject>Electroencephalogram (EEG)</subject><subject>Electroencephalography</subject><subject>Games</subject><subject>Long-Short Term Memory (LSTM)</subject><subject>Merging</subject><subject>Neural networks</subject><subject>Visualization</subject><subject>Wheelchairs</subject><issn>2832-823X</issn><isbn>9798350316957</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2023</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1kN1Kw0AQhVdBsNS-gRf7AqmzM_nZvZTa1kDBCyv0rmyys2mkTUo2In17V6xXM-cc5oMzQkgFc6XAPL1st-V7ZrIinyMgzRVQqhHhRsxMYTRlQCqP8a2YoCZMNNLuXsxC-ASIkQFAMxG71Vdo-05-t-NBLpdrGdqms8cgbedkebINB-n7QdbHPrCTcevP3Em-RN_xyPX4ex0ZXRM1n-WR7dBF9SDufOTw7Dqn4mO13C5ek83bulw8b5KDymhMSNVpkZL3jhy6urKpBp1bsrrIME1zD1gxYO0qF7sZ42NXi-SpRqcsEU3F4x-3Zeb9eWhPdrjs_59BP9jQU_o</recordid><startdate>20231101</startdate><enddate>20231101</enddate><creator>Zaway, Lassaad</creator><creator>Ben Amor, Nader</creator><creator>Ktari, Jalel</creator><creator>Jallouli, Mohamed</creator><creator>Chrifi-Alaoui, Larbi</creator><creator>Delahoche, Laurent</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20231101</creationdate><title>Fusion with EEG signals and Images for closed or open eyes detection using deep learning</title><author>Zaway, Lassaad ; Ben Amor, Nader ; Ktari, Jalel ; Jallouli, Mohamed ; Chrifi-Alaoui, Larbi ; Delahoche, Laurent</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-h153t-31c4743ffd3d2dcba48086a3a8752446f02be02cdbd22099f202a23f3c2d1a333</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Blink detection</topic><topic>Convolutional Neural Network (CNN)</topic><topic>Deep learning</topic><topic>Electroencephalogram (EEG)</topic><topic>Electroencephalography</topic><topic>Games</topic><topic>Long-Short Term Memory (LSTM)</topic><topic>Merging</topic><topic>Neural networks</topic><topic>Visualization</topic><topic>Wheelchairs</topic><toplevel>online_resources</toplevel><creatorcontrib>Zaway, Lassaad</creatorcontrib><creatorcontrib>Ben Amor, Nader</creatorcontrib><creatorcontrib>Ktari, Jalel</creatorcontrib><creatorcontrib>Jallouli, Mohamed</creatorcontrib><creatorcontrib>Chrifi-Alaoui, Larbi</creatorcontrib><creatorcontrib>Delahoche, Laurent</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zaway, Lassaad</au><au>Ben Amor, Nader</au><au>Ktari, Jalel</au><au>Jallouli, Mohamed</au><au>Chrifi-Alaoui, Larbi</au><au>Delahoche, Laurent</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Fusion with EEG signals and Images for closed or open eyes detection using deep learning</atitle><btitle>2023 IEEE International Conference on Design, Test and Technology of Integrated Systems (DTTIS)</btitle><stitle>DTTIS</stitle><date>2023-11-01</date><risdate>2023</risdate><spage>1</spage><epage>5</epage><pages>1-5</pages><eissn>2832-823X</eissn><eisbn>9798350316957</eisbn><abstract>EEG technology has been used in many areas and systems such as games and electric wheelchairs. Despite their maturity and the availability of low-cost easy-to-use EEG sensors, the risk of error command still remains. To tackle this problem, merging EEG sensor data with another sensor can be an interesting solution. In this work, we address this hypothesis by merging EEG-based blink detection with facial images captured by cameras using a deep learning algorithm, enabling the simultaneous analysis of both neural and visual aspects of blinks. This fusion not only enhances detection accuracy but also allows for assessing blink-related cognitive workload and emotional responses in real time.</abstract><pub>IEEE</pub><doi>10.1109/DTTIS59576.2023.10348220</doi><tpages>5</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | EISSN: 2832-823X |
| ispartof | 2023 IEEE International Conference on Design, Test and Technology of Integrated Systems (DTTIS), 2023, p.1-5 |
| issn | 2832-823X |
| language | eng |
| recordid | cdi_ieee_primary_10348220 |
| source | IEEE Xplore All Conference Series |
| subjects | Blink detection Convolutional Neural Network (CNN) Deep learning Electroencephalogram (EEG) Electroencephalography Games Long-Short Term Memory (LSTM) Merging Neural networks Visualization Wheelchairs |
| title | Fusion with EEG signals and Images for closed or open eyes detection using deep learning |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-23T20%3A06%3A15IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Fusion%20with%20EEG%20signals%20and%20Images%20for%20closed%20or%20open%20eyes%20detection%20using%20deep%20learning&rft.btitle=2023%20IEEE%20International%20Conference%20on%20Design,%20Test%20and%20Technology%20of%20Integrated%20Systems%20(DTTIS)&rft.au=Zaway,%20Lassaad&rft.date=2023-11-01&rft.spage=1&rft.epage=5&rft.pages=1-5&rft.eissn=2832-823X&rft_id=info:doi/10.1109/DTTIS59576.2023.10348220&rft.eisbn=9798350316957&rft_dat=%3Cieee_CHZPO%3E10348220%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-h153t-31c4743ffd3d2dcba48086a3a8752446f02be02cdbd22099f202a23f3c2d1a333%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=10348220&rfr_iscdi=true |