Features extraction from human eye movements via echo state network
The paper develops a procedure for features extraction from eye movement’s time series aimed at age-related classification of humans. It exploits the properties of the echo state network (ESN) reservoir state achieved after its intrinsic plasticity tuning. A novel, recently proposed approach for ran...
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| Published in | Neural computing & applications Vol. 32; no. 9; pp. 4213 - 4226 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer London
01.05.2020
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0941-0643 1433-3058 |
| DOI | 10.1007/s00521-019-04329-z |
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| Abstract | The paper develops a procedure for features extraction from eye movement’s time series aimed at age-related classification of humans. It exploits the properties of the echo state network (ESN) reservoir state achieved after its intrinsic plasticity tuning. A novel, recently proposed approach for ranking of dynamic data series using as single feature the length of the reservoir state vector reached after consecutive feeding of each time series into the ESN was investigated in details using eye tracker recordings of human eye movements during visual stimulation and decision-making process. Inclusion of other features like variance of ESN extracted feature for multiple similar stimulations as well as decision correctness allowed for better classification of test subjects. The results support the view that the metrics and dynamics of the eye movements depend little on age, though they are strongly related to the visual stimulation characteristics. |
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| AbstractList | The paper develops a procedure for features extraction from eye movement’s time series aimed at age-related classification of humans. It exploits the properties of the echo state network (ESN) reservoir state achieved after its intrinsic plasticity tuning. A novel, recently proposed approach for ranking of dynamic data series using as single feature the length of the reservoir state vector reached after consecutive feeding of each time series into the ESN was investigated in details using eye tracker recordings of human eye movements during visual stimulation and decision-making process. Inclusion of other features like variance of ESN extracted feature for multiple similar stimulations as well as decision correctness allowed for better classification of test subjects. The results support the view that the metrics and dynamics of the eye movements depend little on age, though they are strongly related to the visual stimulation characteristics. |
| Author | Kraleva, Radoslava Koprinkova-Hristova, Petia Stefanova, Miroslava Genova, Bilyana Bocheva, Nadejda Kralev, Velin |
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| CitedBy_id | crossref_primary_10_3390_electronics12030485 crossref_primary_10_1016_j_eswa_2022_118789 crossref_primary_10_1007_s00521_020_04781_2 crossref_primary_10_1016_j_neucom_2022_06_008 |
| Cites_doi | 10.1167/iovs.05-1311 10.1016/j.neucom.2007.12.020 10.1007/978-3-319-32192-9_3 10.3991/ijoe.v14i02.7988 10.3389/fnhum.2015.00046 10.1016/j.artmed.2018.02.002 10.1016/j.neurobiolaging.2006.11.007 10.1016/j.ins.2018.09.057 10.1007/978-3-319-92007-8_29 10.1016/j.neunet.2017.04.008 10.1016/j.patrec.2014.10.015 10.1038/s41598-018-31577-1 10.1109/IJCNN.2015.7280703 10.1016/j.neucom.2016.03.108 10.1016/j.cosrev.2009.03.005 10.1016/j.neunet.2007.04.011 10.3200/JMBR.38.5.373-382 10.1007/978-3-642-40728-4_43 |
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| References_xml | – reference: Jaeger H (2002) Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the ”echo state network” approach. GMD Report 159, German National Research Center for Information Technology – reference: SunLJinBYangHTongJLiuCXiongHUnsupervised EEG feature extraction based on echo state networkInf. Sci.201947511710.1016/j.ins.2018.09.057 – reference: PraterASpatiotemporal signal classification via principal components of reservoir statesNeural Netw201791667510.1016/j.neunet.2017.04.008 – reference: Koprinkova-Hristova P (2015) On effects of IP improvement of ESN reservoirs for reflecting of data structure. In: Proceedings of the international joint conference on neural networks (IJCNN) 2015. IEEE, Killarney, Ireland. https://doi.org/10.1109/IJCNN.2015.7280703 – reference: SteilJJOnline reservoir adaptation by intrinsic plasticity for back-propagation–decorrelation and echo state learningNeural Netw20072035336410.1016/j.neunet.2007.04.011 – reference: Koprinkova-Hristova P, Alexiev K (2013) Echo state networks in dynamic data clustering. In: V. Mladenov et al. (eds.) International conferenece on artificial neural networks 2013, LNCS vol 8131. Springer, Heidelberg, pp 343–350 – reference: YangQKapoulaZAging does not affect the accuracy of vertical saccades nor the quality of their binocular coordination: a study of a special elderly groupNeurobiol Aging20082946223810.1016/j.neurobiolaging.2006.11.007 – reference: MarandiRMadeleinePOmlandØVuillermeNSamanAEye movement characteristics reflected fatigue development in both young and elderly individualsSci Rep201881314810.1038/s41598-018-31577-1 – reference: IrvingELSteinbachMJLillakasLBabuRJHutchingsNHorizontal saccade dynamics across the human life spanInvest Ophthalmol Vis Sci2006472478248410.1167/iovs.05-1311 – reference: BozhkovLKoprinkova-HristovaPGeorgievaPReservoir computing for emotion valence discrimination from EEG signalsNeurocomputing2017231284010.1016/j.neucom.2016.03.108 – reference: Koprinkova-HristovaPKountchevRNakamatsuKMultidimensional data clustering and visualization via Echo state networksNew approaches in intelligent image analysis, intelligent systems reference library2016ChamSpringer9312210.1007/978-3-319-32192-9_3 – reference: TrentinESchererSSchwenkerFEmotion recognition from speech signals via a probabilistic echo state networkPattern Recognit Lett20156641210.1016/j.patrec.2014.10.015 – reference: Koprinkova-Hristova P, Tontchev N (2012) Echo state networks for multidimensional data clustering. In: Villa AEP, Duch W, Érdi P, Masulli F, Palm G (eds) International conference on artificial neural networks 2012, LNCS vol 7552. Springer, Heidelberg, pp 571–578 – reference: SchrauwenBWandermannMVerstraetenDSteilJJStroobandtDImproving reservoirs using intrinsic plasticityNeurocomputing2008711159117110.1016/j.neucom.2007.12.020 – reference: LacySESmithSLLonesMAUsing echo state networks for classification: a case study in Parkinson’s disease diagnosisArtif Intell Med201886535910.1016/j.artmed.2018.02.002 – reference: DowiaschSMarxSEinhauserWBremmerFEffects of aging on eye movements in the real worldFront Hum Neurosci201594610.3389/fnhum.2015.00046 – reference: Koprinkova-Hristova P, Stefanova M, Genova B, Bocheva N (2018) Echo state network for classification of human eye movements during decision making. In: Iliadis L, Maglogiannis I, Plagianakos V (eds) Artificial intelligence applications and innovations. AIAI 2018. IFIP advances in information and communication technology, vol 519, pp 337–348 – reference: PrattJWelshTDoddMGrowing older does not always mean moving slower: examining aging and the Saccadic motor systemJ Motor Behav20063837338210.3200/JMBR.38.5.373-382 – reference: LukoseviciusMJaegerHReservoir computing approaches to recurrent neural network trainingComput. Sci. Rev.2009312714910.1016/j.cosrev.2009.03.005 – reference: KralevaRKralevVSinyaginaNKoprinkova-HristovaPBochevaNDesign and analysis of a relational database for behavioral experiments data processingInt J Online Eng201814211713210.3991/ijoe.v14i02.7988 – volume: 47 start-page: 2478 year: 2006 ident: 4329_CR15 publication-title: Invest Ophthalmol Vis Sci doi: 10.1167/iovs.05-1311 – ident: 4329_CR1 – volume: 71 start-page: 1159 year: 2008 ident: 4329_CR10 publication-title: Neurocomputing doi: 10.1016/j.neucom.2007.12.020 – start-page: 93 volume-title: New approaches in intelligent image analysis, intelligent systems reference library year: 2016 ident: 4329_CR11 doi: 10.1007/978-3-319-32192-9_3 – volume: 14 start-page: 117 issue: 2 year: 2018 ident: 4329_CR19 publication-title: Int J Online Eng doi: 10.3991/ijoe.v14i02.7988 – volume: 9 start-page: 46 year: 2015 ident: 4329_CR16 publication-title: Front Hum Neurosci doi: 10.3389/fnhum.2015.00046 – volume: 86 start-page: 53 year: 2018 ident: 4329_CR4 publication-title: Artif Intell Med doi: 10.1016/j.artmed.2018.02.002 – volume: 29 start-page: 622 issue: 4 year: 2008 ident: 4329_CR18 publication-title: Neurobiol Aging doi: 10.1016/j.neurobiolaging.2006.11.007 – volume: 475 start-page: 1 year: 2019 ident: 4329_CR3 publication-title: Inf. 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| Title | Features extraction from human eye movements via echo state network |
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