{"id":"https://openalex.org/W7161283712","doi":"https://doi.org/10.48550/arxiv.2605.14883","title":"BCI-Based Assessment of Ocular Response Time Using Dynamic Time Warping Leveraging an RDWT-Driven Deep Neural Framework","display_name":"BCI-Based Assessment of Ocular Response Time Using Dynamic Time Warping Leveraging an RDWT-Driven Deep Neural Framework","publication_year":2026,"publication_date":"2026-05-14","ids":{"openalex":"https://openalex.org/W7161283712","doi":"https://doi.org/10.48550/arxiv.2605.14883"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.14883","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14883","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.14883","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136269594","display_name":"Shantanu Sarkar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sarkar, Shantanu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136257946","display_name":"Sai Shashank Gandavarapu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gandavarapu, Sai Shashank","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076213740","display_name":"Jeff Feng","orcid":"https://orcid.org/0000-0001-5777-1250"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Jeff","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037823063","display_name":"Saurabh Prasad","orcid":"https://orcid.org/0000-0003-3729-9360"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prasad, Saurabh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078302701","display_name":"Reza Khanbabaie","orcid":"https://orcid.org/0000-0001-6574-2097"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khanbabaie, Reza","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5028200774","display_name":"Jos\u00e9 L. Contreras-Vidal","orcid":"https://orcid.org/0000-0002-6499-1208"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Contreras-Vidal, Jose L.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.43860000371932983,"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":0.43860000371932983,"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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.34860000014305115,"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"}},{"id":"https://openalex.org/T10542","display_name":"Vestibular and auditory disorders","score":0.08550000190734863,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/dynamic-time-warping","display_name":"Dynamic time warping","score":0.6901000142097473},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6292999982833862},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5509999990463257},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.4415000081062317},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4390999972820282},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.39879998564720154},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.3865000009536743},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.35839998722076416}],"concepts":[{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.6901000142097473},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6833000183105469},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.63919997215271},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6292999982833862},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5509999990463257},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.4415000081062317},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4390999972820282},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.39879998564720154},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3702000081539154},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.35839998722076416},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.3416000008583069},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.33570000529289246},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3197000026702881},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31700000166893005},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C19012869","wikidata":"https://www.wikidata.org/wiki/Q578372","display_name":"Response time","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C2778681526","wikidata":"https://www.wikidata.org/wiki/Q420647","display_name":"Electrooculography","level":3,"score":0.28459998965263367},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.27410000562667847},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2703999876976013},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.25589999556541443},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.14883","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14883","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.14883","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14883","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.43515172600746155,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Mild":[0],"traumatic":[1],"brain":[2],"injury":[3],"(mTBI)":[4],"is":[5,20],"a":[6,21,79],"prevalent":[7],"condition":[8],"that":[9,32,48,125],"remains":[10],"difficult":[11],"to":[12,60,93,154],"diagnose":[13],"in":[14],"its":[15],"early":[16],"stages.":[17],"Oculomotor":[18],"dysfunction":[19],"well-established":[22],"marker":[23],"of":[24,29,208],"mTBI,":[25],"motivating":[26],"the":[27,101,192,206],"development":[28],"portable":[30],"tools":[31],"capture":[33],"both":[34],"eye-movement":[35],"behavior":[36],"and":[37,73,98,111,118,146,204],"underlying":[38],"neurophysiology.":[39],"In":[40],"this":[41],"work,":[42],"we":[43],"present":[44],"an":[45,130],"initial":[46],"framework":[47],"integrates":[49],"electroencephalogram":[50],"(EEG)":[51],"with":[52,213],"augmented-reality":[53],"(AR)-based":[54],"Vestibular/Ocular":[55],"Motor":[56],"Screening":[57],"(VOMS)":[58],"tasks":[59,182,196],"estimate":[61,155],"subject-specific":[62],"ocular":[63,156],"response":[64,157],"times.":[65,158],"Pre-processed":[66],"EEG":[67,210],"signals,":[68],"obtained":[69],"through":[70],"band-pass":[71],"filtering":[72,97,113,127],"average":[74],"referencing,":[75],"are":[76,91],"analyzed":[77],"using":[78,114,141],"Redundant":[80],"Discrete":[81],"Wavelet":[82],"Transform":[83],"(RDWT)-driven":[84],"deep":[85],"neural":[86],"framework.":[87],"The":[88],"RDWT":[89],"coefficients":[90],"subjected":[92],"trainable":[94],"zero-phase":[95],"convolutional":[96],"reconstructed":[99],"into":[100],"time":[102],"domain":[103],"via":[104],"inverse":[105],"RDWT,":[106],"followed":[107],"by":[108,170],"channel-wise":[109],"temporal":[110,179],"spatial":[112],"2D":[115],"convolution":[116],"layers":[117],"convolutional-LSTM-based":[119],"decoding.":[120],"An":[121],"ablation":[122],"study":[123],"demonstrates":[124],"wavelet-domain":[126],"serves":[128],"as":[129,197],"effective":[131],"denoising":[132],"strategy,":[133],"improving":[134],"prediction":[135],"performance.":[136],"Sliding-window":[137],"predictions":[138],"were":[139],"validated":[140],"Pearson":[142],"correlation":[143],"(&gt;=":[144],"0.5),":[145],"Dynamic":[147],"Time":[148],"Warping":[149],"(DTW)":[150],"was":[151],"subsequently":[152],"used":[153],"DTW-derived":[159],"metrics":[160,215],"revealed":[161,177],"significant":[162],"inter-subject":[163],"differences":[164,203],"across":[165],"all":[166],"VOM":[167],"tasks,":[168],"supported":[169],"Mann-Whitney":[171],"U":[172],"tests.":[173],"Cross-correlation":[174],"analysis":[175],"further":[176],"task-dependent":[178],"behaviors:":[180],"pursuit":[181,195],"exhibited":[183],"reactive":[184],"tracking,":[185],"whereas":[186],"saccades":[187],"showed":[188],"anticipatory":[189],"responses.":[190],"Overall,":[191],"results":[193],"highlight":[194],"particularly":[198],"informative":[199],"for":[200,216],"distinguishing":[201],"timing":[202],"demonstrate":[205],"potential":[207],"RDWT-based":[209],"features":[211],"combined":[212],"DTW":[214],"multimodal":[217],"mTBI":[218],"assessment.":[219]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-16T00:00:00"}
