{"id":"https://openalex.org/W4388937686","doi":"https://doi.org/10.1109/icccnt56998.2023.10306915","title":"EEG and EMG fusion-based hand 3D Trajectory Estimation using deep learning model: A preliminary study","display_name":"EEG and EMG fusion-based hand 3D Trajectory Estimation using deep learning model: A preliminary study","publication_year":2023,"publication_date":"2023-07-06","ids":{"openalex":"https://openalex.org/W4388937686","doi":"https://doi.org/10.1109/icccnt56998.2023.10306915"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt56998.2023.10306915","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt56998.2023.10306915","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101721924","display_name":"Rohit Gupta","orcid":"https://orcid.org/0000-0002-6940-6899"},"institutions":[{"id":"https://openalex.org/I145286018","display_name":"SRM Institute of Science and Technology","ror":"https://ror.org/050113w36","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Rohit Gupta","raw_affiliation_strings":["SRM IST,Dept. of Biomedical Engineering,Kattankulathur,Tamil Nadu,India"],"affiliations":[{"raw_affiliation_string":"SRM IST,Dept. of Biomedical Engineering,Kattankulathur,Tamil Nadu,India","institution_ids":["https://openalex.org/I145286018"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048431560","display_name":"Amit Bhongade","orcid":"https://orcid.org/0000-0001-7375-9438"},"institutions":[{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Amit Bhongade","raw_affiliation_strings":["Indian Institute of Technology Delhi,Department of Electrical Engineering,New Delhi,India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology Delhi,Department of Electrical Engineering,New Delhi,India","institution_ids":["https://openalex.org/I68891433"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052347493","display_name":"Tapan Kumar Gandhi","orcid":"https://orcid.org/0000-0002-3532-9389"},"institutions":[{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Tapan Kumar Gandhi","raw_affiliation_strings":["Indian Institute of Technology Delhi,Department of Electrical Engineering,New Delhi,India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology Delhi,Department of Electrical Engineering,New Delhi,India","institution_ids":["https://openalex.org/I68891433"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101721924"],"corresponding_institution_ids":["https://openalex.org/I145286018"],"apc_list":null,"apc_paid":null,"fwci":0.1813,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.5150188,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"12","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7644879817962646},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7636893391609192},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7571024894714355},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.6787641644477844},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.628665030002594},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5744423270225525},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5143619775772095},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.5088034272193909},{"id":"https://openalex.org/keywords/robotics","display_name":"Robotics","score":0.5000615119934082},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49363505840301514},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4656409025192261},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.4148615896701813},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3639466464519501},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.21108010411262512},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08761760592460632},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.07363271713256836}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7644879817962646},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7636893391609192},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7571024894714355},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.6787641644477844},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.628665030002594},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5744423270225525},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5143619775772095},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.5088034272193909},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.5000615119934082},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49363505840301514},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4656409025192261},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.4148615896701813},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3639466464519501},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.21108010411262512},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08761760592460632},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.07363271713256836},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt56998.2023.10306915","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt56998.2023.10306915","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334771","display_name":"Science and Engineering Research Board","ror":"https://ror.org/03ffdsr55"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1783344409","https://openalex.org/W2012300725","https://openalex.org/W2024855129","https://openalex.org/W2111692383","https://openalex.org/W2126013367","https://openalex.org/W2327450850","https://openalex.org/W2354225344","https://openalex.org/W2369439413","https://openalex.org/W2790727571","https://openalex.org/W2910825437","https://openalex.org/W2960585436","https://openalex.org/W2965188267","https://openalex.org/W2983598339","https://openalex.org/W2991004743","https://openalex.org/W3012841673","https://openalex.org/W3022003141","https://openalex.org/W3022387615","https://openalex.org/W3037514691","https://openalex.org/W3080395176","https://openalex.org/W3092622470","https://openalex.org/W3105175154","https://openalex.org/W3115777743","https://openalex.org/W3207696354","https://openalex.org/W3210979994","https://openalex.org/W4229460043","https://openalex.org/W4380894500"],"related_works":["https://openalex.org/W2922348724","https://openalex.org/W200322357","https://openalex.org/W2130428257","https://openalex.org/W4308951944","https://openalex.org/W4295532600","https://openalex.org/W2063823869","https://openalex.org/W2047973478","https://openalex.org/W1941703695","https://openalex.org/W2057366091","https://openalex.org/W3131574667"],"abstract_inverted_index":{"Hand":[0],"trajectory":[1,32,64,140,212,227],"estimation":[2,65,228],"plays":[3],"an":[4,68],"important":[5],"part":[6],"in":[7,30,62,225],"various":[8],"fields,":[9],"such":[10,238],"as":[11,239],"human-computer":[12],"interaction":[13],"(HCI),":[14],"rehabilitation":[15,241],"robotics,":[16,242],"and":[17,26,40,51,95,114,128,137,184,196,207,221,229,243],"prosthetics.":[18,244],"This":[19],"study":[20],"investigates":[21],"the":[22,76,104,143,147,152,167,170,193,205,210,217,234],"use":[23],"of":[24,37,71,78,85,93,108,146,160,174,182,192,209,219,236],"EEG":[25,86,94,112,175,183,220],"EMG":[27,89,96,119,178,185,222],"data":[28,97,186,223],"fusion":[29,92,181,224],"hand":[31,63,109,139,149,201,211,226],"estimation,":[33],"employing":[34],"a":[35,41,59,157,188],"dataset":[36,168],"five":[38,79],"subjects":[39,80],"deep":[42,123],"learning":[43,124],"algorithm":[44,125],"based":[45],"on":[46,164],"Convolutional":[47],"Neural":[48],"Networks":[49],"(CNN)":[50],"Long":[52],"Short-Term":[53],"Memory":[54],"(LSTM).":[55],"The":[56,91,121,180],"results":[57],"demonstrate":[58],"significant":[60],"enhancement":[61],"accuracy,":[66],"with":[67,88,151,177,200],"average":[69,165],"accuracy":[70,206],"0.608":[72],"\u00b1":[73,162],"0.031":[74],"across":[75,166],"database":[77],"for":[81,99,169],"entire":[82,171],"frequency":[83,172],"range":[84,173],"signal":[87,176],"signal.":[90,179],"allows":[98],"improved":[100],"spatial":[101],"resolution,":[102],"capturing":[103],"intention":[105],"or":[106],"planning":[107],"movements":[110],"from":[111,118],"signals":[113],"precise":[115],"muscle":[116,197],"activations":[117,198],"signals.":[120],"CNN-LSTM":[122],"effectively":[126],"integrates":[127],"processes":[129,195],"these":[130],"fused":[131],"signals,":[132],"leading":[133],"to":[134,204,232],"more":[135,189],"accurate":[136],"reliable":[138],"estimations.":[141],"Furthermore,":[142],"correlation":[144,159],"coefficient":[145],"estimated":[148],"trajectories":[150,155],"ground":[153],"truth":[154],"exhibits":[156],"strong":[158],"0.6354":[161],"0.030":[163],"enables":[187],"comprehensive":[190],"understanding":[191],"neural":[194],"associated":[199],"movements,":[202],"contributing":[203],"reliability":[208],"estimation.":[213],"These":[214],"findings":[215],"highlight":[216],"significance":[218],"its":[230],"potential":[231],"improve":[233],"performance":[235],"applications":[237],"HCI,":[240]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}