A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers

Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals signifi...

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Published inJournal of neural engineering Vol. 18; no. 3; p. 31002
Main Authors Zhang, Xiang, Yao, Lina, Wang, Xianzhi, Monaghan, Jessica, McAlpine, David, Zhang, Yu
Format Journal Article
LanguageEnglish
Published England 01.06.2021
Subjects
Algorithms
Brain
Brain-Computer Interfaces
Deep Learning
Electroencephalography - methods
Humans
brain signals
brain–computer interface
survey
deep learning algorithms
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Abstract Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.
AbstractList Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.
Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.
Author McAlpine, David
Wang, Xianzhi
Zhang, Yu
Zhang, Xiang
Yao, Lina
Monaghan, Jessica
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  orcidid: 0000-0001-5097-2113
  surname: Zhang
  fullname: Zhang, Xiang
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  givenname: Lina
  surname: Yao
  fullname: Yao, Lina
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  givenname: Xianzhi
  surname: Wang
  fullname: Wang, Xianzhi
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  givenname: Jessica
  orcidid: 0000-0003-1416-4164
  surname: Monaghan
  fullname: Monaghan, Jessica
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  givenname: David
  surname: McAlpine
  fullname: McAlpine, David
– sequence: 6
  givenname: Yu
  surname: Zhang
  fullname: Zhang, Yu
BackLink https://www.ncbi.nlm.nih.gov/pubmed/33171452$$D View this record in MEDLINE/PubMed
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Cites_doi 10.1109/mmsp.2017.8122291
10.1109/mlsp.2016.7738824
10.1109/cvprw.2018.00267
10.1016/j.procs.2016.07.422
10.1109/upcon.2017.8251115
10.1016/j.neuroimage.2018.07.043
10.1109/jiot.2018.2877786
10.1007/978-3-319-49685-6_3
10.1109/bibe.2014.26
10.1016/j.bspc.2014.12.007
10.1016/j.neunet.2014.09.003
10.1109/isbi.2018.8363678
10.23919/icact.2018.8323715
10.1109/spmb.2017.8257019
10.1016/j.eij.2015.06.002
10.1007/978-3-319-70096-0_76
10.1109/eusipco.2015.7362882
10.1142/S0129065716500398
10.3389/fnins.2017.00103
10.1109/smc.2017.8122608
10.1109/smc.2016.7844325
10.1109/TPAMI.2010.125
10.1109/icc.2016.7510831
10.1063/1.5005474
10.1109/isbi.2018.8363685
10.1016/j.neucom.2016.01.007
10.1007/978-3-319-46672-9_58
10.1155/2016/5480760
10.1109/icecds.2017.8390035
10.1109/TNSRE.2012.2190299
10.1109/iww-bci.2018.8311531
10.1109/JBHI.2017.2727218
10.1088/1741-2552/aaaf82
10.1007/978-3-319-58628-1_4
10.24963/ijcai.2018/432
10.1109/5.939829
10.1109/icme.2014.6890166
10.1109/TNSRE.2018.2884641
10.1016/j.ijpsycho.2015.05.004
10.1109/JBHI.2017.2650199
10.1016/j.neuroimage.2016.03.063
10.1109/icassp.2013.6639347
10.11591/ijra.v4i4.pp292-310
10.1145/3264959
10.1007/s12559-017-9533-x
10.1109/iecbes.2016.7843428
10.1109/embc.2017.8037251
10.1007/978-3-319-73600-6_8
10.1016/j.neuroimage.2011.04.040
10.1016/j.cmpb.2016.12.005
10.3389/fnins.2014.00229
10.1142/S0129065716500258
10.1155/2016/5642856
10.4108/eai.7-11-2017.2273696
10.1016/j.clinph.2006.10.019
10.1002/aic.690370209
10.4249/scholarpedia.5947
10.1109/embc.2015.7318929
10.3389/fninf.2018.00023
10.1109/ner.2015.7146583
10.1007/978-3-319-16628-5_25
10.1088/1741-2560/8/2/025022
10.1016/j.neucom.2014.08.092
10.1016/j.neuroimage.2016.01.005
10.1142/S0129065718500119
10.1007/978-3-319-70096-0_78
10.1109/rtsi.2016.7740576
10.1155/2018/1350692
10.1101/304774
10.1145/3123266.3127907
10.2172/1366924
10.1109/TNSRE.2017.2733220
10.1038/s41598-018-21495-7
10.1016/j.neucom.2017.08.039
10.5755/j01.itc.46.2.17528
10.1016/j.jneumeth.2016.11.002
10.1038/srep27755
10.1016/j.biopsycho.2013.04.010
10.1109/iww-bci.2018.8311491
10.1109/embc.2012.6346563
10.1109/icac.2017.41
10.1016/j.media.2017.07.005
10.1109/ijcnn.2014.6889383
10.1109/bibm.2017.8217737
10.1109/tpami.2013.50
10.1109/mmsp.2016.7813351
10.1109/socpar.2015.7492799
10.1109/embc.2016.7590835
10.1016/j.bspc.2016.11.013
10.1371/journal.pone.0077089
10.3389/fncom.2016.00130
10.1109/apwc-on-cse.2016.017
10.1109/ner.2017.8008295
10.1142/S0129065718500090
10.3389/fnins.2015.00438
10.1088/1741-2560/4/2/R03
10.1088/1741-2552/aaf12e
10.1056/NEJM198205203062004
10.1109/embc.2016.7590716
10.1109/chinasip.2014.6889297
10.1007/978-3-319-09330-7_25
10.1016/j.eswa.2018.04.021
10.1109/TNNLS.2018.2790388
10.1088/1741-2552/aab2f2
10.1088/1741-2560/14/1/016003
10.11591/ijece.v6i6.12967
10.1109/prni.2017.7981506
10.1109/dmiaf.2016.7574921
10.1109/bibm.2013.6732507
10.1007/978-3-319-44778-0_36
10.1109/ner.2017.8008425
10.1109/fg.2018.00060
10.1007/978-3-319-26561-2_5
10.1167/15.6.4
10.3115/v1/D14-1179
10.31031/rdms.2018.07.000662
10.1109/ijcnn.2016.7727656
10.1007/978-3-319-10443-0_39
10.26599/BDMA.2018.9020001
10.1007/978-981-10-2666-9
10.1109/bhi.2018.8333405
10.1109/MLSP.2017.8168133
10.1109/TNSRE.2018.2813138
10.1145/3292500.3330966
10.1109/cspa.2017.8064956
10.3389/fninf.2018.00029
10.1109/embc.2015.7318984
10.1088/1741-2552/aace8c
10.1117/12.2224172
10.1109/TNSRE.2015.2403270
10.1016/j.jneumeth.2016.10.008
10.1007/978-3-319-63312-1_20
10.1109/TNSRE.2017.2721116
10.1109/cidm.2014.7008672
10.1109/iccv.2017.369
10.3390/s19030551
10.1016/j.ijpsycho.2014.09.003
10.1016/j.ijpsycho.2016.07.500
10.1109/TNNLS.2014.2302898
10.3389/fnhum.2017.00290
10.1109/TAMD.2015.2431497
10.1371/journal.pone.0172578
10.1109/TBDATA.2017.2769670
10.1155/2018/6058065
10.1371/journal.pcbi.1006633
10.3389/fnins.2017.00302
10.1364/JOSA.67.001475
10.3389/fncom.2015.00146
10.1016/j.compbiomed.2017.09.017
10.1016/j.media.2016.05.004
10.1088/1741-2560/4/2/R01
10.1371/journal.pone.0131328
10.1109/cvpr.2017.479
10.1088/1741-2552/ab260c
1Emotion recognition based on EEG using LSTM recurrent neural network
10.1109/smc.2017.8122606
10.1109/ijcnn.2018.8489727
10.1109/icoin.2018.8343254
10.1007/978-3-319-18356-5_24
10.1109/camsap.2015.7383844
10.1177/0142331215587568
10.1088/1361-6579/aad57e
10.1109/chicc.2015.7260182
10.1109/ftc.2016.7821697
10.1109/spmb.2015.7405422
10.1016/j.nbd.2014.11.025
10.1126/science.1127647
10.1088/1741-2552/aa9817
10.1016/j.compbiomed.2016.10.019
10.1016/j.neuroimage.2009.02.028
10.1038/s41598-017-17562-0
10.1109/tcyb.2017.2788081
10.1016/j.neulet.2009.06.045
10.3389/fnhum.2015.00003
10.1007/978-3-319-12637-1_63
10.1109/72.159058
10.1007/978-3-319-59081-3_62
10.1101/070441
10.1007/s10827-009-0196-9
10.1007/978-3-319-59072-1_55
10.1371/journal.pone.0184713
10.1016/j.neubiorev.2017.01.002
10.1016/j.bspc.2017.12.001
10.3390/app7111197
10.1145/3038439.3038444
10.1017/atsip.2014.4
10.1016/j.jneumeth.2015.01.033
10.1016/j.ijleo.2016.10.117
10.1145/3393619
10.1109/embc.2015.7318980
10.1109/spmb.2017.8257015
10.1109/amc.2019.8371082
10.1007/978-3-319-59081-3_58
chinasip.2015.7230517
10.1109/TNSRE.2016.2601240
10.1109/iww-bci.2018.8311493
10.1016/j.cmpb.2018.04.012
10.1016/j.compbiomed.2018.05.019
10.1145/3230632
10.1016/S1388-2457(99)00141-8
10.1007/s10439-006-9170-0
10.1088/1741-2552/aae5d8
10.1016/S0165-0173(03)00193-0
10.1109/ACCESS.2018.2833746
10.22323/1.299.0002
10.1109/embc.2016.7590822
10.1145/2903150.2903159
10.1007/978-3-030-00928-1_21
10.1007/978-3-319-42297-8_74
10.1109/icassp.2016.7471776
10.1109/percom.2018.8444575
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Keywords brain signals
brain–computer interface
survey
deep learning algorithms
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PublicationTitle Journal of neural engineering
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References Zhang (jneabc902bib76) 2017
Soekadar (jneabc902bib20) 2015; 83
Yin (jneabc902bib107) 2017; 140
Manor (jneabc902bib176) 2015; 9
Ortiz (jneabc902bib212) 2016; 26
Fatourechi (jneabc902bib32) 2007; 118
Lawhern (jneabc902bib73) 2018; 15
Mioranda-Correa (jneabc902bib104) 2018
Attia (jneabc902bib196) 2018
Plis (jneabc902bib211) 2014; 8
Koyamada (jneabc902bib202) 2015
Wen (jneabc902bib14) 2018; 12
Lees (jneabc902bib249) 2018; 15
Gui (jneabc902bib26) 2019; 51
Cecotti (jneabc902bib28) 2017; 111
Zhao (jneabc902bib126) 2014
Hiroyasu (jneabc902bib201) 2014
Mason (jneabc902bib15) 2007; 35
Parasuraman (jneabc902bib231) 2012; 59
Singh (jneabc902bib39) 2018; 6
Li (jneabc902bib100) 2015
Cichy (jneabc902bib254) 2017; 153
Mao (jneabc902bib180) 2017
Fraiwan (jneabc902bib56) 2017
Kramer (jneabc902bib43) 1991; 37
Rutkove (jneabc902bib239) 2007
Nurse (jneabc902bib84) 2015; 10
Li (jneabc902bib89) 2016
Sainath (jneabc902bib42) 2013
Hosseini (jneabc902bib129) 2017
Nurse (jneabc902bib60) 2016
Kumar (jneabc902bib79) 2016
Ahn (jneabc902bib21) 2015; 243
Cecotti (jneabc902bib177) 2017
Lin (jneabc902bib182) 2017; 12
Hernández (jneabc902bib144) 2018; 12
Tabar (jneabc902bib4) 2016; 14
Abiri (jneabc902bib27) 2019; 16
Kulasingham (jneabc902bib195) 2016
Gordienko (jneabc902bib220) 2017
Vieira (jneabc902bib33) 2017; 74
Kawde (jneabc902bib105) 2017
Bashivan (jneabc902bib159) 2015
Shamwell (jneabc902bib185) 2016; 9836
Supratak (jneabc902bib57) 2017; 25
An (jneabc902bib3) 2014
Yin (jneabc902bib223) 2015; 23
Bengio (jneabc902bib257) 2013; 35
Jingwei (jneabc902bib63) 2015
St-Yves (jneabc902bib259) 2018
Stober (jneabc902bib142) 2014
Wang (jneabc902bib67) 2017
Ruiz (jneabc902bib22) 2014; 95
Behncke (jneabc902bib139) 2018
Zhang (jneabc902bib88) 2018; 99
Zhang (jneabc902bib10) 2018
Gao (jneabc902bib167) 2015; 18
Hung (jneabc902bib140) 2017
Dong (jneabc902bib58) 2018; 26
Zhang (jneabc902bib6) 2018; 2
Reddy (jneabc902bib136) 2016
Pfurtscheller (jneabc902bib35) 2001; 89
Chu (jneabc902bib149) 2017
Mao (jneabc902bib174) 2014
Maddula (jneabc902bib170) 2017
Schmidhuber (jneabc902bib30) 2015; 61
Regan (jneabc902bib37) 1977; 67
Cichy (jneabc902bib204) 2016; 6
Zheng (jneabc902bib102) 2014
Tang (jneabc902bib66) 2017; 130
Solon (jneabc902bib178) 2017
Page (jneabc902bib125) 2014
Vařeka (jneabc902bib186) 2017; 11
Dai (jneabc902bib81) 2019; 19
Li (jneabc902bib208) 2014
Pal (jneabc902bib40) 1992; 3
Movahedi (jneabc902bib19) 2018; 22
Xu (jneabc902bib101) 2016
Kingma (jneabc902bib46) 2013
Kwak (jneabc902bib191) 2017; 12
Zhang (jneabc902bib2) 2018; 6
Liu (jneabc902bib168) 2017
jneabc902bib246
Acharya (jneabc902bib113) 2018; 161
Suk (jneabc902bib210) 2015
Kavasidis (jneabc902bib134) 2017
Bashashati (jneabc902bib8) 2007; 4
Zheng (jneabc902bib97) 2015
Li (jneabc902bib155) 2016
Kalunga (jneabc902bib236) 2016; 191
Liu (jneabc902bib24) 2018; 1
Tsiouris (jneabc902bib53) 2018; 99
Haider (jneabc902bib23) 2017
Baltatzis (jneabc902bib141) 2017; 7
Zhang (jneabc902bib233) 2019
Fernández-Varela (jneabc902bib70)
Li (jneabc902bib92) 2017; 10
Ullah (jneabc902bib112) 2018; 107
jneabc902bib242
Roy (jneabc902bib17) 2019; 16
Lee (jneabc902bib64) 2018
Yuan (jneabc902bib109) 2018
Morabito (jneabc902bib123) 2017; 27
Wang (jneabc902bib91) 2018
Radford (jneabc902bib227) 2016
Tan (jneabc902bib54) 2015
Mikolov (jneabc902bib41) 2010
Biswal (jneabc902bib52) 2017
Bashivan (jneabc902bib172) 2016
Suk (jneabc902bib209) 2016; 129
Teo (jneabc902bib135) 2017; vol
Hochreiter (jneabc902bib255) 1997; 9
Litjens (jneabc902bib16) 2017; 42
Li (jneabc902bib77) 2014
Chiarelli (jneabc902bib71) 2018; 15
Talathi (jneabc902bib110) 2017
Naseer (jneabc902bib29) 2015; 9
Xu (jneabc902bib99) 2016
Stober (jneabc902bib188) 2014; 1
Redkar (jneabc902bib75) 2015; 4
Shanbhag (jneabc902bib173) 2017
Völker (jneabc902bib143) 2018
Jia (jneabc902bib98) 2014
Schirrmeister (jneabc902bib116) 2017
Samek (jneabc902bib9) 2012
Tsinalis (jneabc902bib25) 2016
Du (jneabc902bib151) 2017
Yuan (jneabc902bib121) 2017
Narejo (jneabc902bib152) 2016; 6
Zheng (jneabc902bib232) 2016
Almogbel (jneabc902bib145) 2018
Pérez-Benítez (jneabc902bib197) 2018
Shahin (jneabc902bib69) 2017; 21
Acharya (jneabc902bib114) 2018; 100
Morabito (jneabc902bib115) 2016
Tan (jneabc902bib82) 2017
Hosseini (jneabc902bib120) 2017; 3
Shen (jneabc902bib203) 2019; 15
Sturm (jneabc902bib68) 2016; 274
Seeliger (jneabc902bib213) 2018; 181
Gordon (jneabc902bib183) 2017
Li (jneabc902bib103) 2013
Min (jneabc902bib224) 2017; 18
Stober (jneabc902bib157) 2015
Bashivan (jneabc902bib171) 2016
Antoniades (jneabc902bib230) 2018; 28
Aliakbaryhosseinabadi (jneabc902bib235) 2019
Vilamala (jneabc902bib51) 2017
Al-kaysi (jneabc902bib131) 2015
Sarkar (jneabc902bib165) 2016; 7
Koike-Akino (jneabc902bib161) 2016
Hajinoroozi (jneabc902bib179) 2017
Hu (jneabc902bib216) 2016
Frydenlund (jneabc902bib87) 2015
Yoon (jneabc902bib184) 2018; 2018
Burle (jneabc902bib240) 2015; 97
Mao (jneabc902bib175) 2016
Mayya (jneabc902bib247) 2017
Marc Moreno (jneabc902bib226) 2017
Waytowich (jneabc902bib192) 2018
Hajinoroozi (jneabc902bib147) 2015
Ball (jneabc902bib1) 2009; 46
Cho (jneabc902bib256) 2014
Alhagry (jneabc902bib108) 2017; 8
Hajinoroozi (jneabc902bib153) 2015
Huve (jneabc902bib198) 2017
Lin (jneabc902bib122) 2016
Turner (jneabc902bib127) 2014
Hasasneh (jneabc902bib219) 2018; 2018
Sakhavi (jneabc902bib86) 2015
Pfurtscheller (jneabc902bib221) 1999; 110
Kawasaki (jneabc902bib162) 2015
Sors (jneabc902bib50) 2018; 42
Zheng (jneabc902bib96) 2015; 7
Wang (jneabc902bib93) 2016; 2016
Carabez (jneabc902bib187) 2017; 7
Guger (jneabc902bib251) 2009; 462
Salek-Haddadi (jneabc902bib244) 2003; 43
Chambon (jneabc902bib48) 2018; 26
Yepes (jneabc902bib137) 2017
Huve (jneabc902bib199) 2018
Abdulkader (jneabc902bib7) 2015; 16
Hinton (jneabc902bib45) 2009; 4
Zhang (jneabc902bib85) 2018
Tu (jneabc902bib194) 2018
Yang (jneabc902bib62) 2015
Johansen (jneabc902bib118) 2016
Antoniades (jneabc902bib229) 2016
Huang (jneabc902bib36) 2012; 20
Shreyas (jneabc902bib206) 2017
Koyama (jneabc902bib234) 2010; 29
Mahmud (jneabc902bib13) 2018; 29
Shang (jneabc902bib138) 2017
Norcia (jneabc902bib248) 2015; 15
Cecotti (jneabc902bib166) 2011; 33
Mikkelsen (jneabc902bib238) 2015; 9
San (jneabc902bib154) 2016
Chiappa (jneabc902bib250) 1997; 306
Sarraf (jneabc902bib207) 2016
Hartmann (jneabc902bib59) 2018
Hosseini (jneabc902bib117) 2017
Hachem (jneabc902bib189) 2014; 65
Ruffini (jneabc902bib111) 2016
Chai (jneabc902bib94) 2016; 79
Sarraf (jneabc902bib225) 2016
Tortella-Feliu (jneabc902bib243) 2014; 94
Malmivuo (jneabc902bib241) 1995
Thomas (jneabc902bib190) 2017
Lu (jneabc902bib80) 2017; 25
Glauner (jneabc902bib258) 2015
Hennrich (jneabc902bib200) 2015
Lotte (jneabc902bib11) 2007; 4
Welvaert (jneabc902bib253) 2013; 8
Zhang (jneabc902bib61) 2017; 11
Abdelfattah (jneabc902bib132) 2018
Naseer (jneabc902bib38) 2016; 2016
Belitski (jneabc902bib252) 2011; 8
Zhang (jneabc902bib160) 2018
Aznan (jneabc902bib193) 2018
Manzano (jneabc902bib55) 2017
Arjovsky (jneabc902bib228) 2017
Liu (jneabc902bib95) 2016
Shah (jneabc902bib128) 2017
Lotte (jneabc902bib5) 2018; 15
Ren (jneabc902bib78) 2014
Palazzo (jneabc902bib133) 2017
Chai (jneabc902bib158) 2017; 11
Zhang (jneabc902bib156) 2018; 27
Manor (jneabc902bib181) 2016; 10
Gao (jneabc902bib106) 2015
Sternin (jneabc902bib148) 2015
Uktveris (jneabc902bib72) 2017; 46
Putten (jneabc902bib146) 2018; 8
Ikeda (jneabc902bib245) 2019
Ansari (jneabc902bib119) 2018; 29
Wang (jneabc902bib18) 2016
Duan (jneabc902bib83) 2016; 88
Garg (jneabc902bib217) 2017
Shu (jneabc902bib218) 2013
Ma (jneabc902bib169) 2017; 275
Khurana (jneabc902bib222) 2016
Cecotti (jneabc902bib12) 2014; 25
Spampinato (jneabc902bib163) 2017
Zhang (jneabc902bib65) 2017
Deng (jneabc902bib31) 2014; 3
Han (jneabc902bib214) 2018
Yin (jneabc902bib150) 2017; 33
Golmohammadi (jneabc902bib130) 2017
Liu (jneabc902bib164) 2018; 275
Zhang (jneabc902bib215) 2018
Pacharra (jneabc902bib237) 2017; 11
Li (jneabc902bib74) 2015; 165
Wen (jneabc902bib124) 2018; 6
Havaei (jneabc902bib205) 2017; 35
Zhang (jneabc902bib49) 2016; 38
Liu (jneabc902bib90) 2017; 299
Hinton (jneabc902bib44) 2006; 313
Aricò (jneabc902bib34) 2018; 39
Goodfellow (jneabc902bib47) 2014; 2
References_xml – start-page: 1
  year: 2017
  ident: jneabc902bib206
  article-title: A deep learning architecture for brain tumor segmentation in MRI images
  doi: 10.1109/mmsp.2017.8122291
– start-page: 1
  year: 2016
  ident: jneabc902bib229
  article-title: Deep learning for epileptic intracranial EEG data
  doi: 10.1109/mlsp.2016.7738824
– start-page: 1985
  year: 2018
  ident: jneabc902bib194
  article-title: Relating deep neural network representations to EEG-fMRI spatiotemporal dynamics in a perceptual decision-making task
  doi: 10.1109/cvprw.2018.00267
– volume: 88
  start-page: 176
  year: 2016
  ident: jneabc902bib83
  article-title: Classification based on multilayer extreme learning machine for motor imagery task from EEG signals
  publication-title: Proc. Comput. Sci.
  doi: 10.1016/j.procs.2016.07.422
– start-page: 587
  year: 2017
  ident: jneabc902bib105
  article-title: Deep belief network based affect recognition from physiological signals
  doi: 10.1109/upcon.2017.8251115
– volume: 181
  start-page: 775
  year: 2018
  ident: jneabc902bib213
  article-title: Generative adversarial networks for reconstructing natural images from brain activity
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2018.07.043
– year: 1995
  ident: jneabc902bib241
– volume: 6
  start-page: 2084
  year: 2018
  ident: jneabc902bib2
  article-title: Internet of things meets brain-computer interface: a unified deep learning framework for enabling human-thing cognitive interactivity
  publication-title: IEEE Internet Things J.
  doi: 10.1109/jiot.2018.2877786
– start-page: 22
  year: 2016
  ident: jneabc902bib89
  article-title: Implementation of EEG emotion recognition system based on hierarchical convolutional neural networks
  doi: 10.1007/978-3-319-49685-6_3
– start-page: 30
  year: 2014
  ident: jneabc902bib98
  article-title: A novel semi-supervised deep learning framework for affective state recognition on EEG signals
  doi: 10.1109/bibe.2014.26
– volume: 18
  start-page: 127
  year: 2015
  ident: jneabc902bib167
  article-title: Multi-ganglion ANN based feature learning with application to p300-BCI signal classification
  publication-title: Biomed. Signal Process. Control
  doi: 10.1016/j.bspc.2014.12.007
– volume: 61
  start-page: 85
  year: 2015
  ident: jneabc902bib30
  article-title: Deep learning in neural networks: an overview
  publication-title: Neural Netw.
  doi: 10.1016/j.neunet.2014.09.003
– start-page: 734
  year: 2018
  ident: jneabc902bib214
  article-title: GAN-based synthetic brain MR image generation
  doi: 10.1109/isbi.2018.8363678
– start-page: 256
  year: 2018
  ident: jneabc902bib145
  article-title: EEG-signals based cognitive workload detection of vehicle driver using deep learning
  doi: 10.23919/icact.2018.8323715
– start-page: 1
  year: 2017
  ident: jneabc902bib128
  article-title: Optimizing channel selection for seizure detection
  doi: 10.1109/spmb.2017.8257019
– volume: 16
  start-page: 213
  year: 2015
  ident: jneabc902bib7
  article-title: Brain computer interfacing: applications and challenges
  publication-title: Egypt. Inf. J.
  doi: 10.1016/j.eij.2015.06.002
– start-page: 748
  year: 2017
  ident: jneabc902bib65
  article-title: Intent recognition in smart living through deep recurrent neural networks
  doi: 10.1007/978-3-319-70096-0_76
– start-page: 2736
  year: 2015
  ident: jneabc902bib86
  article-title: Parallel convolutional-linear neural network for motor imagery classification
  doi: 10.1109/eusipco.2015.7362882
– start-page: 214
  year: 2017
  ident: jneabc902bib228
  article-title: Wasserstein generative adversarial networks
– volume: 27
  year: 2017
  ident: jneabc902bib123
  article-title: Deep learning representation from electroencephalography of early-stage Creutzfeldt-Jakob disease and features for differentiation from rapidly progressive dementia
  publication-title: Int. J. Neural Syst.
  doi: 10.1142/S0129065716500398
– volume: 11
  start-page: 103
  year: 2017
  ident: jneabc902bib158
  article-title: Improving EEG-based driver fatigue classification using sparse-deep belief networks
  publication-title: Frontiers Neurosci.
  doi: 10.3389/fnins.2017.00103
– year: 2017
  ident: jneabc902bib23
– volume: 2
  start-page: 2672
  year: 2014
  ident: jneabc902bib47
  article-title: Generative adversarial nets
  publication-title: Adv. Neural Inf. Process. Syst.
– start-page: 234
  year: 2017
  ident: jneabc902bib190
  article-title: Deep learning-based classification for brain-computer interfaces
  doi: 10.1109/smc.2017.8122608
– start-page: 712
  year: 2016
  ident: jneabc902bib136
  article-title: Online eye state recognition from EEG data using deep architectures
  doi: 10.1109/smc.2016.7844325
– volume: 33
  start-page: 433
  year: 2011
  ident: jneabc902bib166
  article-title: Convolutional neural networks for p300 detection with application to brain-computer interfaces
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
  doi: 10.1109/TPAMI.2010.125
– start-page: 1
  year: 2016
  ident: jneabc902bib216
  article-title: Clinical decision support for Alzheimer’s disease based on deep learning and brain network
  doi: 10.1109/icc.2016.7510831
– volume: vol
  year: 2017
  ident: jneabc902bib135
  article-title: Deep learning for EEG-based preference classification
  doi: 10.1063/1.5005474
– start-page: 766
  year: 2018
  ident: jneabc902bib196
  article-title: A time domain classification of steady-state visual evoked potentials using deep recurrent-convolutional neural networks
  doi: 10.1109/isbi.2018.8363685
– volume: 191
  start-page: 55
  year: 2016
  ident: jneabc902bib236
  article-title: Online SSVEP-based BCI using Riemannian geometry
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2016.01.007
– start-page: 521
  year: 2016
  ident: jneabc902bib95
  article-title: Emotion recognition using multimodal deep learning
  doi: 10.1007/978-3-319-46672-9_58
– volume: 2016
  start-page: 1
  year: 2016
  ident: jneabc902bib38
  article-title: Analysis of different classification techniques for two-class functional near-infrared spectroscopy-based brain-computer interface
  publication-title: Comput. Intell. Neurosci.
  doi: 10.1155/2016/5480760
– year: 2018
  ident: jneabc902bib160
  article-title: Brain2Object: printing your mind from brain signals with spatial correlation embedding
– start-page: 3142
  year: 2017
  ident: jneabc902bib173
  article-title: P300 analysis using deep neural network
  doi: 10.1109/icecds.2017.8390035
– start-page: 1573
  year: 2017
  ident: jneabc902bib198
  article-title: Brain activity recognition with a wearable fNIRS using neural networks
– volume: 20
  start-page: 379
  year: 2012
  ident: jneabc902bib36
  article-title: Electroencephalography (EEG)-based brain–computer interface (BCI): a 2D virtual wheelchair control based on event-related desynchronization/synchronization and state control
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2012.2190299
– start-page: 1
  year: 2018
  ident: jneabc902bib139
  article-title: The signature of robot action success in EEG signals of a human observer: decoding and visualization using deep convolutional neural networks
  doi: 10.1109/iww-bci.2018.8311531
– volume: 22
  start-page: 642
  year: 2018
  ident: jneabc902bib19
  article-title: Deep belief networks for electroencephalography: a review of recent contributions and future outlooks
  publication-title: IEEE J. Biomed. Health Inform.
  doi: 10.1109/JBHI.2017.2727218
– volume: 15
  year: 2018
  ident: jneabc902bib71
  article-title: Deep learning for hybrid EEG-fNIRs brain–computer interface: application to motor imagery classification
  publication-title: J. Neural Eng.
  doi: 10.1088/1741-2552/aaaf82
– year: 2015
  ident: jneabc902bib148
  article-title: Tempo estimation from the EEG signal during perception and imagination of music
– year: 2015
  ident: jneabc902bib100
  article-title: EEG based emotion identification using unsupervised deep feature learning
– start-page: 45
  year: 2017
  ident: jneabc902bib179
  article-title: Deep transfer learning for cross-subject and cross-experiment prediction of image rapid serial visual presentation events from EEG data
  doi: 10.1007/978-3-319-58628-1_4
– year: 2018
  ident: jneabc902bib85
  article-title: Multi-modality sensor data classification with selective attention
  doi: 10.24963/ijcai.2018/432
– year: 2017
  ident: jneabc902bib130
  article-title: Deep architectures for automated seizure detection in scalp EEGs
– volume: 89
  start-page: 1123
  year: 2001
  ident: jneabc902bib35
  article-title: Motor imagery and direct brain-computer communication
  publication-title: Proc. IEEE
  doi: 10.1109/5.939829
– start-page: 1
  year: 2014
  ident: jneabc902bib102
  article-title: EEG-based emotion classification using deep belief networks
  doi: 10.1109/icme.2014.6890166
– year: 2017
  ident: jneabc902bib129
  article-title: Cloud-based deep learning of big EEG data for epileptic seizure prediction
– volume: 27
  start-page: 31
  year: 2018
  ident: jneabc902bib156
  article-title: Learning spatial–spectral–temporal EEG features with recurrent 3D convolutional neural networks for cross-task mental workload assessment
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2018.2884641
– volume: 97
  start-page: 210
  year: 2015
  ident: jneabc902bib240
  article-title: Spatial and temporal resolutions of EEG: is it really black and white? A scalp current density view
  publication-title: Int. J. Psychophysiol.
  doi: 10.1016/j.ijpsycho.2015.05.004
– volume: 21
  start-page: 1546
  year: 2017
  ident: jneabc902bib69
  article-title: Deep learning and insomnia: assisting clinicians with their diagnosis
  publication-title: IEEE J. Biomed. Health Inform.
  doi: 10.1109/JBHI.2017.2650199
– volume: 153
  start-page: 346
  year: 2017
  ident: jneabc902bib254
  article-title: Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks
  publication-title: Neuroimage
  doi: 10.1016/j.neuroimage.2016.03.063
– start-page: 8614
  year: 2013
  ident: jneabc902bib42
  article-title: Deep convolutional neural networks for LVCSR
  doi: 10.1109/icassp.2013.6639347
– year: 2016
  ident: jneabc902bib171
  article-title: Learning representations from EEG with deep recurrent-convolutional neural networks
– volume: 4
  start-page: 4
  year: 2015
  ident: jneabc902bib75
  article-title: Using deep learning for human computer interface via electroencephalography
  publication-title: IAES Int. J. Robot. Autom.
  doi: 10.11591/ijra.v4i4.pp292-310
– volume: 2
  start-page: 149
  year: 2018
  ident: jneabc902bib6
  article-title: Mindid: person identification from brain waves through attention-based recurrent neural network
  publication-title: Proc. ACM Interact. Mobile Wearable Ubiquitous Technol.
  doi: 10.1145/3264959
– volume: 10
  start-page: 368
  year: 2017
  ident: jneabc902bib92
  article-title: Hierarchical convolutional neural networks for EEG-based emotion recognition
  publication-title: Cogn. Comput.
  doi: 10.1007/s12559-017-9533-x
– start-page: 127
  year: 2016
  ident: jneabc902bib195
  article-title: Deep belief networks and stacked autoencoders for the p300 guilty knowledge test
  doi: 10.1109/iecbes.2016.7843428
– start-page: 2031
  year: 2017
  ident: jneabc902bib177
  article-title: Convolutional neural networks for event-related potential detection: impact of the architecture
  doi: 10.1109/embc.2017.8037251
– start-page: 82
  year: 2018
  ident: jneabc902bib91
  article-title: Data augmentation for EEG-based emotion recognition with deep convolutional neural networks
  doi: 10.1007/978-3-319-73600-6_8
– volume: 59
  start-page: 70
  year: 2012
  ident: jneabc902bib231
  article-title: Individual differences in cognition, affect and performance: behavioral, neuroimaging and molecular genetic approaches
  publication-title: Neuroimage
  doi: 10.1016/j.neuroimage.2011.04.040
– year: 2016
  ident: jneabc902bib25
– volume: 140
  start-page: 93
  year: 2017
  ident: jneabc902bib107
  article-title: Recognition of emotions using multimodal physiological signals and an ensemble deep learning model
  publication-title: Comput. Methods Programs Biomed.
  doi: 10.1016/j.cmpb.2016.12.005
– volume: 8
  start-page: 229
  year: 2014
  ident: jneabc902bib211
  article-title: Deep learning for neuroimaging: a validation study
  publication-title: Frontiers Neurosci.
  doi: 10.3389/fnins.2014.00229
– volume: 26
  year: 2016
  ident: jneabc902bib212
  article-title: Ensembles of deep learning architectures for the early diagnosis of the Alzheimer’s disease
  publication-title: Int. J. Neural Syst.
  doi: 10.1142/S0129065716500258
– volume: 2016
  start-page: 1
  year: 2016
  ident: jneabc902bib93
  article-title: Research on healthy anomaly detection model based on deep learning from multiple time-series physiological signals
  publication-title: Sci. Program.
  doi: 10.1155/2016/5642856
– start-page: 31
  year: 2019
  ident: jneabc902bib235
– start-page: 28
  year: 2017
  ident: jneabc902bib76
  article-title: Multi-person brain activity recognition via comprehensive EEG signal analysis
  doi: 10.4108/eai.7-11-2017.2273696
– volume: 118
  start-page: 480
  year: 2007
  ident: jneabc902bib32
  article-title: EMG and EOG artifacts in brain computer interface systems: a survey
  publication-title: Clin. Neurophysiol.
  doi: 10.1016/j.clinph.2006.10.019
– volume: 1
  start-page: 1449
  year: 2014
  ident: jneabc902bib188
  article-title: Using convolutional neural networks to recognize rhythm stimuli from electroencephalography recordings
  publication-title: Adv. Neural Inf. Process. Syst. (NIPS)
– volume: 37
  start-page: 233
  year: 1991
  ident: jneabc902bib43
  article-title: Nonlinear principal component analysis using autoassociative neural networks
  publication-title: AIChE J.
  doi: 10.1002/aic.690370209
– volume: 4
  start-page: 5947
  year: 2009
  ident: jneabc902bib45
  article-title: Deep belief networks
  publication-title: Scholarpedia
  doi: 10.4249/scholarpedia.5947
– start-page: 2620
  year: 2015
  ident: jneabc902bib62
  article-title: On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification
  doi: 10.1109/embc.2015.7318929
– volume: 12
  start-page: 23
  year: 2018
  ident: jneabc902bib14
  article-title: Deep learning methods to process fMRI data and their application in the diagnosis of cognitive impairment: a brief overview and our opinion
  publication-title: Frontiers Neuroinform.
  doi: 10.3389/fninf.2018.00023
– start-page: 154
  year: 2015
  ident: jneabc902bib97
  article-title: Revealing critical channels and frequency bands for emotion recognition from EEG with deep belief network
  doi: 10.1109/ner.2015.7146583
– start-page: 340
  year: 2014
  ident: jneabc902bib126
  article-title: Deep learning in the EEG diagnosis of Alzheimer’s disease
  doi: 10.1007/978-3-319-16628-5_25
– volume: 8
  year: 2011
  ident: jneabc902bib252
  article-title: P300 audio-visual speller
  publication-title: J. Neural Eng.
  doi: 10.1088/1741-2560/8/2/025022
– volume: 165
  start-page: 23
  year: 2015
  ident: jneabc902bib74
  article-title: Feature learning from incomplete EEG with denoising autoencoder
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2014.08.092
– volume: 129
  start-page: 292
  year: 2016
  ident: jneabc902bib209
  article-title: State-space model with deep learning for functional dynamics estimation in resting-state fMRI
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2016.01.005
– volume: 29
  year: 2018
  ident: jneabc902bib119
  article-title: Neonatal seizure detection using deep convolutional neural networks
  publication-title: Int. J. Neural Syst.
  doi: 10.1142/S0129065718500119
– start-page: 767
  year: 2017
  ident: jneabc902bib82
  article-title: Multimodal classification with deep convolutional-recurrent neural networks for electroencephalography
  doi: 10.1007/978-3-319-70096-0_78
– start-page: 1
  year: 2016
  ident: jneabc902bib115
  article-title: Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer’s disease patients from scalp EEG recordings
  doi: 10.1109/rtsi.2016.7740576
– volume: 2018
  year: 2018
  ident: jneabc902bib219
  article-title: Deep learning approach for automatic classification of ocular and cardiac artifacts in meg data
  publication-title: J. Eng.
  doi: 10.1155/2018/1350692
– start-page: 1054
  year: 2018
  ident: jneabc902bib259
  article-title: Generative adversarial networks conditioned on brain activity reconstruct seen images
  doi: 10.1101/304774
– start-page: 1809
  year: 2017
  ident: jneabc902bib134
  article-title: Brain2Image: converting brain signals into images
  doi: 10.1145/3123266.3127907
– year: 2017
  ident: jneabc902bib110
  article-title: Deep recurrent neural networks for seizure detection and early seizure detection systems
  doi: 10.2172/1366924
– year: 2017
  ident: jneabc902bib247
  article-title: Information-theoretic analysis of refractory effects in the p300 speller
– volume: 26
  start-page: 324
  year: 2018
  ident: jneabc902bib58
  article-title: Mixed neural network approach for temporal sleep stage classification
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2017.2733220
– volume: 8
  start-page: 3069
  year: 2018
  ident: jneabc902bib146
  article-title: Predicting sex from brain rhythms with deep learning
  publication-title: Sci. Rep.
  doi: 10.1038/s41598-018-21495-7
– volume: 275
  start-page: 288
  year: 2018
  ident: jneabc902bib164
  article-title: Deep learning based on batch normalization for p300 signal detection
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2017.08.039
– year: 2016
  ident: jneabc902bib172
  article-title: Mental state recognition via wearable EEG
– volume: 46
  start-page: 260
  year: 2017
  ident: jneabc902bib72
  article-title: Application of convolutional neural networks to four-class motor imagery classification problem
  publication-title: Inf. Technol. Control
  doi: 10.5755/j01.itc.46.2.17528
– volume: 275
  start-page: 80
  year: 2017
  ident: jneabc902bib169
  article-title: The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing
  publication-title: J. Neurosci. Methods
  doi: 10.1016/j.jneumeth.2016.11.002
– volume: 6
  year: 2016
  ident: jneabc902bib204
  article-title: Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence
  publication-title: Sci. Rep.
  doi: 10.1038/srep27755
– volume: 95
  start-page: 4
  year: 2014
  ident: jneabc902bib22
  article-title: Real-time fMRI brain computer interfaces: self-regulation of single brain regions to networks
  publication-title: Biol. Psychol.
  doi: 10.1016/j.biopsycho.2013.04.010
– start-page: 18
  year: 2018
  ident: jneabc902bib197
  article-title: Development of a brain computer interface interface using multi-frequency visual stimulation and deep neural networks
  doi: 10.1109/isbi.2018.8363685
– start-page: 1
  year: 2018
  ident: jneabc902bib143
  article-title: Deep transfer learning for error decoding from non-invasive EEG
  doi: 10.1109/iww-bci.2018.8311491
– start-page: 2873
  year: 2012
  ident: jneabc902bib9
  article-title: Brain-computer interfacing in discriminative and stationary subspaces
  doi: 10.1109/embc.2012.6346563
– start-page: 83
  year: 2017
  ident: jneabc902bib117
  article-title: Deep learning with edge computing for localization of epileptogenicity using multimodal rs-fMRI and EEG big data
  doi: 10.1109/icac.2017.41
– volume: 42
  start-page: 60
  year: 2017
  ident: jneabc902bib16
  article-title: A survey on deep learning in medical image analysis
  publication-title: Med. Image Anal.
  doi: 10.1016/j.media.2017.07.005
– start-page: 2850
  year: 2014
  ident: jneabc902bib78
  article-title: Convolutional deep belief networks for feature extraction of EEG signal
  doi: 10.1109/ijcnn.2014.6889383
– start-page: 694
  year: 2017
  ident: jneabc902bib121
  article-title: A novel wavelet-based model for EEG epileptic seizure detection using multi-context learning
  doi: 10.1109/bibm.2017.8217737
– year: 2014
  ident: jneabc902bib125
  article-title: Comparing raw data and feature extraction for seizure detection with deep learning methods
– volume: 9
  start-page: 1735
  year: 1997
  ident: jneabc902bib255
  article-title: Long short-term memory
  publication-title: Neural Comput.
  doi: 10.1109/tpami.2013.50
– start-page: 1
  year: 2016
  ident: jneabc902bib99
  article-title: Affective states classification using EEG and semi-supervised deep learning approaches
  doi: 10.1109/mmsp.2016.7813351
– start-page: 149
  year: 2015
  ident: jneabc902bib162
  article-title: Visualizing extracted feature by deep learning in p300 discrimination task
  doi: 10.1109/socpar.2015.7492799
– start-page: 854
  year: 2016
  ident: jneabc902bib161
  article-title: High-accuracy user identification using EEG biometrics
  doi: 10.1109/embc.2016.7590835
– volume: 33
  start-page: 30
  year: 2017
  ident: jneabc902bib150
  article-title: Cross-session classification of mental workload levels using EEG and an adaptive deep learning model
  publication-title: Biomed. Signal Process. Control
  doi: 10.1016/j.bspc.2016.11.013
– volume: 8
  start-page: 11
  year: 2013
  ident: jneabc902bib253
  article-title: On the definition of signal-to-noise ratio and contrast-to-noise ratio for fMRI data
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0077089
– start-page: 1045
  year: 2010
  ident: jneabc902bib41
  article-title: Recurrent neural network based language model
– volume: 10
  start-page: 130
  year: 2016
  ident: jneabc902bib181
  article-title: Multimodal neural network for rapid serial visual presentation brain computer interface
  publication-title: Frontiers Comput. Neurosci.
  doi: 10.3389/fncom.2016.00130
– start-page: 34
  year: 2016
  ident: jneabc902bib79
  article-title: A deep learning approach for motor imagery EEG signal classification
  doi: 10.1109/apwc-on-cse.2016.017
– start-page: 74
  year: 2017
  ident: jneabc902bib151
  article-title: Detecting driving fatigue with multimodal deep learning
  doi: 10.1109/ner.2017.8008295
– volume: 28
  year: 2018
  ident: jneabc902bib230
  article-title: Deep neural architectures for mapping scalp to intracranial EEG
  publication-title: Int. J. Neural Syst.
  doi: 10.1142/S0129065718500090
– volume: 9
  start-page: 438
  year: 2015
  ident: jneabc902bib238
  article-title: EEG recorded from the ear: characterizing the ear-EEG method
  publication-title: Frontiers Neurosci.
  doi: 10.3389/fnins.2015.00438
– volume: 4
  start-page: R32
  year: 2007
  ident: jneabc902bib8
  article-title: A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals
  publication-title: J. Neural Eng.
  doi: 10.1088/1741-2560/4/2/R03
– ident: jneabc902bib246
– year: 2015
  ident: jneabc902bib157
  article-title: Deep feature learning for EEG recordings
– volume: 16
  year: 2019
  ident: jneabc902bib27
  article-title: A comprehensive review of EEG-based brain–computer interface paradigms
  publication-title: J. Neural Eng.
  doi: 10.1088/1741-2552/aaf12e
– volume: 306
  start-page: 1140–50
  year: 1997
  ident: jneabc902bib250
  article-title: Evoked potentials in clinical medicine
  publication-title: New England Journal of Medicine
  doi: 10.1056/NEJM198205203062004
– start-page: 367
  year: 2016
  ident: jneabc902bib155
  article-title: Single-channel EEG-based mental fatigue detection based on deep belief network
  doi: 10.1109/embc.2016.7590716
– start-page: 520
  year: 2014
  ident: jneabc902bib174
  article-title: Classification of non-time-locked rapid serial visual presentation events for brain-computer interaction using deep learning
  doi: 10.1109/chinasip.2014.6889297
– start-page: 203
  year: 2014
  ident: jneabc902bib3
  article-title: A deep learning method for classification of EEG data based on motor imagery
  doi: 10.1007/978-3-319-09330-7_25
– volume: 107
  start-page: 61
  year: 2018
  ident: jneabc902bib112
  article-title: An automated system for epilepsy detection using EEG brain signals based on deep learning approach
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2018.04.021
– volume: 7
  start-page: 1
  year: 2016
  ident: jneabc902bib165
  article-title: Wearable EEG-based activity recognition in PHM-related service environment via deep learning
  publication-title: Int. J. Progn. Health Manag.
– volume: 29
  start-page: 2063
  year: 2018
  ident: jneabc902bib13
  article-title: Applications of deep learning and reinforcement learning to biological data
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
  doi: 10.1109/TNNLS.2018.2790388
– volume: 15
  year: 2018
  ident: jneabc902bib5
  article-title: A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update
  publication-title: J. Neural Eng.
  doi: 10.1088/1741-2552/aab2f2
– volume: 14
  year: 2016
  ident: jneabc902bib4
  article-title: A novel deep learning approach for classification of EEG motor imagery signals
  publication-title: J. Neural Eng.
  doi: 10.1088/1741-2560/14/1/016003
– start-page: 203
  year: 2015
  ident: jneabc902bib210
– volume: 6
  start-page: 3131
  year: 2016
  ident: jneabc902bib152
  article-title: EEG based eye state classification using deep belief network and stacked autoencoder
  publication-title: Int. J. Electr. Computer Eng.
  doi: 10.11591/ijece.v6i6.12967
– start-page: 1
  year: 2017
  ident: jneabc902bib217
  article-title: Automatic 1D convolutional neural network-based detection of artifacts in MEG acquired without electrooculography or electrocardiography
  doi: 10.1109/prni.2017.7981506
– start-page: 148
  year: 2016
  ident: jneabc902bib101
  article-title: EEG-based affect states classification using deep belief networks
  doi: 10.1109/dmiaf.2016.7574921
– start-page: 305
  year: 2013
  ident: jneabc902bib103
  article-title: Affective state recognition from EEG with deep belief networks
  doi: 10.1109/bibm.2013.6732507
– start-page: 306
  year: 2016
  ident: jneabc902bib111
  article-title: EEG-driven RNN classification for prognosis of neurodegeneration in at-risk patients
  doi: 10.1007/978-3-319-44778-0_36
– start-page: 609
  year: 2017
  ident: jneabc902bib180
  article-title: EEG-based biometric identification with deep learning
  doi: 10.1109/ner.2017.8008425
– start-page: 373
  year: 2018
  ident: jneabc902bib104
  article-title: A multi-task cascaded network for prediction of affect, personality, mood and social context using EEG signals
  doi: 10.1109/fg.2018.00060
– start-page: 38
  year: 2015
  ident: jneabc902bib131
  article-title: A multichannel deep belief network for the classification of EEG data
  doi: 10.1007/978-3-319-26561-2_5
– volume: 18
  start-page: 851
  year: 2017
  ident: jneabc902bib224
  article-title: Deep learning in bioinformatics
  publication-title: Briefings Bioinform.
– start-page: 649
  year: 2014
  ident: jneabc902bib142
  article-title: Classifying EEG recordings of rhythm perception
– volume: 15
  start-page: 4
  year: 2015
  ident: jneabc902bib248
  article-title: The steady-state visual evoked potential in vision research: a review
  publication-title: J. Vision
  doi: 10.1167/15.6.4
– year: 2014
  ident: jneabc902bib256
  article-title: Learning phrase representations using RNN encoder-decoder for statistical machine translation
  doi: 10.3115/v1/D14-1179
– volume: 6
  start-page: 175
  year: 2018
  ident: jneabc902bib39
  article-title: Study for reduction of pollution level in diesel engines, petrol engines and generator sets by bio signal ring
  publication-title: Int. J. Adv. Res. Innov.
  doi: 10.31031/rdms.2018.07.000662
– start-page: 3556
  year: 2016
  ident: jneabc902bib222
  article-title: Class-wise deep dictionaries for EEG classification
  doi: 10.1109/ijcnn.2016.7727656
– start-page: 305
  year: 2014
  ident: jneabc902bib208
  article-title: Deep learning based imaging data completion for improved brain disease diagnosis
  doi: 10.1007/978-3-319-10443-0_39
– volume: 1
  start-page: 1
  year: 2018
  ident: jneabc902bib24
  article-title: Applications of deep learning to MRI images: a survey
  publication-title: Big Data Min. Anal.
  doi: 10.26599/BDMA.2018.9020001
– start-page: 102
  year: 2016
  ident: jneabc902bib18
  doi: 10.1007/978-981-10-2666-9
– start-page: 206
  year: 2018
  ident: jneabc902bib109
  article-title: A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning
  doi: 10.1109/bhi.2018.8333405
– start-page: 1
  year: 2017
  ident: jneabc902bib51
  article-title: Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring
  doi: 10.1109/MLSP.2017.8168133
– volume: 26
  start-page: 758
  year: 2018
  ident: jneabc902bib48
  article-title: A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2018.2813138
– start-page: 139
  year: 2019
  ident: jneabc902bib233
  article-title: Adversarial variational embedding for robust semi-supervised learning
  doi: 10.1145/3292500.3330966
– start-page: 228
  year: 2017
  ident: jneabc902bib56
  article-title: Neonatal sleep state identification using deep learning autoencoders
  doi: 10.1109/cspa.2017.8064956
– year: 2016
  ident: jneabc902bib175
  article-title: Deep learning for rapid serial visual presentation event from electroencephalography signal
– volume: 12
  start-page: 29
  year: 2018
  ident: jneabc902bib144
  article-title: EEG-based detection of braking intention under different car driving conditions
  publication-title: Frontiers Neuroinform.
  doi: 10.3389/fninf.2018.00029
– start-page: 2844
  year: 2015
  ident: jneabc902bib200
  article-title: Investigating deep learning for fNIRs based BCI
  doi: 10.1109/embc.2015.7318984
– volume: 15
  year: 2018
  ident: jneabc902bib73
  article-title: EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces
  publication-title: J. Neural Eng.
  doi: 10.1088/1741-2552/aace8c
– volume: 9836
  year: 2016
  ident: jneabc902bib185
  article-title: Single-trial EEG rsvp classification using convolutional neural networks
  publication-title: Micro- Nanotechnol. Sensors Syst. Appl. VIII
  doi: 10.1117/12.2224172
– volume: 23
  start-page: 693
  year: 2015
  ident: jneabc902bib223
  article-title: A hybrid brain–computer interface based on the fusion of p300 and SSVEP scores
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2015.2403270
– volume: 274
  start-page: 141
  year: 2016
  ident: jneabc902bib68
  article-title: Interpretable deep neural networks for single-trial EEG classification
  publication-title: J. Neurosci. Methods
  doi: 10.1016/j.jneumeth.2016.10.008
– start-page: 219
  year: 2017
  ident: jneabc902bib55
  article-title: Combination of EEG data time and frequency representations in deep networks for sleep stage classification
  doi: 10.1007/978-3-319-63312-1_20
– volume: 25
  start-page: 1998
  year: 2017
  ident: jneabc902bib57
  article-title: Deepsleepnet: a model for automatic sleep stage scoring based on raw single-channel EEG
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2017.2721116
– start-page: 229
  year: 2014
  ident: jneabc902bib201
  article-title: Gender classification of subjects from cerebral blood flow changes using deep learning
  doi: 10.1109/cidm.2014.7008672
– start-page: 3410
  year: 2017
  ident: jneabc902bib133
  article-title: Generative adversarial networks conditioned by brain signals
  doi: 10.1109/iccv.2017.369
– year: 2017
  ident: jneabc902bib137
  article-title: Improving classification accuracy of feedforward neural networks for spiking neuromorphic chips
– volume: 19
  start-page: 551
  year: 2019
  ident: jneabc902bib81
  article-title: EEG classification of motor imagery using a novel deep learning framework
  publication-title: Sensors
  doi: 10.3390/s19030551
– start-page: 43
  year: 2007
  ident: jneabc902bib239
– volume: 94
  start-page: 365
  year: 2014
  ident: jneabc902bib243
  article-title: Spontaneous EEG activity and spontaneous emotion regulation
  publication-title: Int. J. Psychophysiol.
  doi: 10.1016/j.ijpsycho.2014.09.003
– volume: 111
  start-page: 156
  year: 2017
  ident: jneabc902bib28
  article-title: Best practice for single-trial detection of event-related potentials: application to brain-computer interfaces
  publication-title: Int. J. Psychophysiol.
  doi: 10.1016/j.ijpsycho.2016.07.500
– volume: 25
  start-page: 2030
  year: 2014
  ident: jneabc902bib12
  article-title: Single-trial classification of event-related potentials in rapid serial visual presentation tasks using supervised spatial filtering
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
  doi: 10.1109/TNNLS.2014.2302898
– volume: 11
  start-page: 290
  year: 2017
  ident: jneabc902bib237
  article-title: Concealed around-the-ear EEG captures cognitive processing in a visual simon task
  publication-title: Front. Hum. Neurosci.
  doi: 10.3389/fnhum.2017.00290
– volume: 7
  start-page: 162
  year: 2015
  ident: jneabc902bib96
  article-title: Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks
  publication-title: IEEE Trans. Auton. Mental Dev.
  doi: 10.1109/TAMD.2015.2431497
– volume: 12
  year: 2017
  ident: jneabc902bib191
  article-title: A convolutional neural network for steady state visual evoked potential classification under ambulatory environment
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0172578
– ident: jneabc902bib242
– volume: 3
  start-page: 392
  year: 2017
  ident: jneabc902bib120
  article-title: Optimized deep learning for EEG big data and seizure prediction BCI via internet of things
  publication-title: IEEE Trans. Big Data
  doi: 10.1109/TBDATA.2017.2769670
– year: 2014
  ident: jneabc902bib127
  article-title: Deep belief networks used on high resolution multichannel electroencephalography data for seizure detection
– volume: 2018
  year: 2018
  ident: jneabc902bib184
  article-title: Spatial and time domain feature of ERP speller system extracted via convolutional neural network
  publication-title: Computat. Intell. Neurosci.
  doi: 10.1155/2018/6058065
– volume: 15
  year: 2019
  ident: jneabc902bib203
  article-title: Deep image reconstruction from human brain activity
  publication-title: PLoS Comput. Biol.
  doi: 10.1371/journal.pcbi.1006633
– volume: 11
  start-page: 302
  year: 2017
  ident: jneabc902bib186
  article-title: Stacked autoencoders for the p300 component detection
  publication-title: Frontiers Neurosci.
  doi: 10.3389/fnins.2017.00302
– volume: 67
  start-page: 1475
  year: 1977
  ident: jneabc902bib37
  article-title: Steady-state evoked potentials
  publication-title: JOSA
  doi: 10.1364/JOSA.67.001475
– volume: 9
  start-page: 146
  year: 2015
  ident: jneabc902bib176
  article-title: Convolutional neural network for multi-category rapid serial visual presentation BCI
  publication-title: Frontiers Comput. Neurosci.
  doi: 10.3389/fncom.2015.00146
– volume: 100
  start-page: 270
  year: 2018
  ident: jneabc902bib114
  article-title: Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
  publication-title: Comput. Biol. Med.
  doi: 10.1016/j.compbiomed.2017.09.017
– volume: 35
  start-page: 18
  year: 2017
  ident: jneabc902bib205
  article-title: Brain tumor segmentation with deep neural networks
  publication-title: Med. Image Anal.
  doi: 10.1016/j.media.2016.05.004
– year: 2017
  ident: jneabc902bib226
  article-title: Deep learning for brain tumor segmentation
– volume: 4
  start-page: R1
  year: 2007
  ident: jneabc902bib11
  article-title: A review of classification algorithms for EEG-based brain–computer interfaces
  publication-title: J. Neural Eng.
  doi: 10.1088/1741-2560/4/2/R01
– volume: 10
  year: 2015
  ident: jneabc902bib84
  article-title: A generalizable brain-computer interface (BCI) using machine learning for feature discovery
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0131328
– start-page: 6809
  year: 2017
  ident: jneabc902bib163
  article-title: Deep learning human mind for automated visual classification
  doi: 10.1109/cvpr.2017.479
– start-page: 495
  year: 2019
  ident: jneabc902bib245
  article-title: Spontaneous EEG classification using complex valued neural network
– volume: 16
  year: 2019
  ident: jneabc902bib17
  article-title: Deep learning-based electroencephalography analysis: a systematic review
  publication-title: J. Neural Eng.
  doi: 10.1088/1741-2552/ab260c
– volume: 8
  start-page: 10
  year: 2017
  ident: jneabc902bib108
  article-title: Emotion recognition based on EEG using LSTM recurrent neural network
  publication-title: Emotion
  doi: 1Emotion recognition based on EEG using LSTM recurrent neural network
– start-page: 222
  year: 2017
  ident: jneabc902bib140
  article-title: Brain dynamic states analysis based on 3D convolutional neural network
  doi: 10.1109/smc.2017.8122606
– start-page: 1
  year: 2018
  ident: jneabc902bib132
  article-title: Augmenting the size of EEG datasets using generative adversarial networks
  doi: 10.1109/ijcnn.2018.8489727
– start-page: 2732
  year: 2016
  ident: jneabc902bib232
  article-title: Personalizing EEG-based affective models with transfer learning
– start-page: 906
  year: 2018
  ident: jneabc902bib64
  article-title: A convolution neural networks scheme for classification of motor imagery EEG based on wavelet time-frequency image
  doi: 10.1109/icoin.2018.8343254
– start-page: 273
  year: 2015
  ident: jneabc902bib87
  article-title: Emotional affect estimation using video and EEG data in deep neural networks
  doi: 10.1007/978-3-319-18356-5_24
– volume: 35
  start-page: 1798
  year: 2013
  ident: jneabc902bib257
  article-title: Representation learning: a review and new perspectives
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
  doi: 10.1109/tpami.2013.50
– start-page: 493
  year: 2015
  ident: jneabc902bib147
  article-title: Prediction of driver’s drowsy and alert states from EEG signals with deep learning
  doi: 10.1109/camsap.2015.7383844
– volume: 38
  start-page: 435
  year: 2016
  ident: jneabc902bib49
  article-title: Automatic sleep stage classification based on sparse deep belief net and combination of multiple classifiers
  publication-title: Trans. Inst. Meas. Control
  doi: 10.1177/0142331215587568
– volume: 39
  start-page: 08TR02
  year: 2018
  ident: jneabc902bib34
  article-title: Passive BCI beyond the lab: current trends and future directions
  publication-title: Physiol. Meas.
  doi: 10.1088/1361-6579/aad57e
– start-page: 3518
  year: 2015
  ident: jneabc902bib63
  article-title: Deep learning EEG response representation for brain computer interface
  doi: 10.1109/chicc.2015.7260182
– start-page: 816
  year: 2016
  ident: jneabc902bib207
  article-title: Deep learning-based pipeline to recognize Alzheimer’s disease using fMRI data
  doi: 10.1109/ftc.2016.7821697
– start-page: 1
  year: 2015
  ident: jneabc902bib159
  article-title: Single trial prediction of normal and excessive cognitive load through EEG feature fusion
  doi: 10.1109/spmb.2015.7405422
– volume: 83
  start-page: 172
  year: 2015
  ident: jneabc902bib20
  article-title: Brain–machine interfaces in neurorehabilitation of stroke
  publication-title: Neurobiol. Dis.
  doi: 10.1016/j.nbd.2014.11.025
– volume: 313
  start-page: 504
  year: 2006
  ident: jneabc902bib44
  article-title: Reducing the dimensionality of data with neural networks
  publication-title: Science
  doi: 10.1126/science.1127647
– volume: 15
  year: 2018
  ident: jneabc902bib249
  article-title: A review of rapid serial visual presentation-based brain–computer interfaces
  publication-title: J. Neural Eng.
  doi: 10.1088/1741-2552/aa9817
– volume: 79
  start-page: 205
  year: 2016
  ident: jneabc902bib94
  article-title: Unsupervised domain adaptation techniques based on auto-encoder for non-stationary EEG-based emotion recognition
  publication-title: Comput. Biol. Med.
  doi: 10.1016/j.compbiomed.2016.10.019
– volume: 46
  start-page: 708
  year: 2009
  ident: jneabc902bib1
  article-title: Signal quality of simultaneously recorded invasive and non-invasive EEG
  publication-title: Neuroimage
  doi: 10.1016/j.neuroimage.2009.02.028
– volume: 7
  year: 2017
  ident: jneabc902bib141
  article-title: Bullying incidences identification within an immersive environment using hd EEG-based analysis: a swarm decomposition and deep learning approach
  publication-title: Sci. Rep.
  doi: 10.1038/s41598-017-17562-0
– year: 2016
  ident: jneabc902bib227
  article-title: Unsupervised representation learning with deep convolutional generative adversarial networks
– volume: 99
  start-page: 1
  year: 2018
  ident: jneabc902bib88
  article-title: Spatial-temporal recurrent neural network for emotion recognition
  publication-title: IEEE Trans. Cybern.
  doi: 10.1109/tcyb.2017.2788081
– volume: 462
  start-page: 94
  year: 2009
  ident: jneabc902bib251
  article-title: How many people are able to control a p300-based brain–computer interface (BCI)?
  publication-title: Neurosci. Lett.
  doi: 10.1016/j.neulet.2009.06.045
– volume: 9
  start-page: 3
  year: 2015
  ident: jneabc902bib29
  article-title: fNIRS-based brain-computer interfaces: a review
  publication-title: Front. Hum. Neurosci.
  doi: 10.3389/fnhum.2015.00003
– start-page: 1
  year: 2015
  ident: jneabc902bib106
  article-title: Deep learning of EEG signals for emotion recognition
– start-page: 503
  year: 2014
  ident: jneabc902bib77
  article-title: Deep learning of multifractal attributes from motor imagery induced EEG
  doi: 10.1007/978-3-319-12637-1_63
– volume: 3
  start-page: 683
  year: 1992
  ident: jneabc902bib40
  article-title: Multilayer perceptron, fuzzy sets and classification
  publication-title: IEEE Trans. Neural Netw.
  doi: 10.1109/72.159058
– start-page: 533
  year: 2017
  ident: jneabc902bib67
  article-title: Multi-channel EEG classification based on fast convolutional feature extraction
  doi: 10.1007/978-3-319-59081-3_62
– year: 2013
  ident: jneabc902bib46
  article-title: Auto-encoding variational Bayes
– year: 2016
  ident: jneabc902bib225
  article-title: DeepAD: Alzheimer’s disease classification via deep convolutional neural networks using MRI and fMRI
  doi: 10.1101/070441
– year: 2015
  ident: jneabc902bib202
– year: 2017
  ident: jneabc902bib52
– volume: 29
  start-page: 73
  year: 2010
  ident: jneabc902bib234
  article-title: Comparison of brain–computer interface decoding algorithms in open-loop and closed-loop control
  publication-title: J. Comput. Neurosci.
  doi: 10.1007/s10827-009-0196-9
– start-page: 466
  year: 2017
  ident: jneabc902bib138
  article-title: Cognitive load recognition using multi-channel complex network method
  doi: 10.1007/978-3-319-59072-1_55
– year: 2015
  ident: jneabc902bib258
  article-title: Comparison of training methods for deep neural networks
– volume: 12
  year: 2017
  ident: jneabc902bib182
  article-title: Method for enhancing single-trial p300 detection by introducing the complexity degree of image information in rapid serial visual presentation tasks
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0184713
– volume: 74
  start-page: 58
  year: 2017
  ident: jneabc902bib33
  article-title: Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications
  publication-title: Neurosci. Biobehav. Rev.
  doi: 10.1016/j.neubiorev.2017.01.002
– year: 2018
  ident: jneabc902bib193
– volume: 42
  start-page: 107
  year: 2018
  ident: jneabc902bib50
  article-title: A convolutional neural network for sleep stage scoring from raw single-channel EEG
  publication-title: Biomed. Signal Process. Control
  doi: 10.1016/j.bspc.2017.12.001
– volume: 7
  start-page: 1197
  year: 2017
  ident: jneabc902bib187
  article-title: Identifying single trial event-related potentials in an earphone-based auditory brain-computer interface
  publication-title: Appl. Sci.
  doi: 10.3390/app7111197
– start-page: 25
  year: 2017
  ident: jneabc902bib183
  article-title: Real world BCI: cross-domain learning and practical applications
  doi: 10.1145/3038439.3038444
– volume: 3
  start-page: e2
  year: 2014
  ident: jneabc902bib31
  article-title: A tutorial survey of architectures, algorithms and applications for deep learning
  publication-title: APSIPA Trans. Signal Inf. Process.
  doi: 10.1017/atsip.2014.4
– volume: 243
  start-page: 103
  year: 2015
  ident: jneabc902bib21
  article-title: Performance variation in motor imagery brain–computer interface: a brief review
  publication-title: J. Neurosci. Methods
  doi: 10.1016/j.jneumeth.2015.01.033
– volume: 130
  start-page: 11
  year: 2017
  ident: jneabc902bib66
  article-title: Single-trial EEG classification of motor imagery using deep convolutional neural networks
  publication-title: Optik-Int. J. Light Electron Opt.
  doi: 10.1016/j.ijleo.2016.10.117
– volume: 11
  start-page: 4
  year: 2017
  ident: jneabc902bib61
  article-title: DeepKey: A Multimodal Biometric Authentication System via Deep Decoding Gaits and Brainwaves
  publication-title: ACM Trans. on Intelligent Systems and Technology (TIST)
  doi: 10.1145/3393619
– start-page: 5
  year: 2017
  ident: jneabc902bib178
  article-title: Deep learning approaches for p300 classification in image triage: applications to the nails task
– start-page: 2828
  year: 2015
  ident: jneabc902bib54
  article-title: Sleep spindle detection using deep learning: a validation study based on crowdsourcing
  doi: 10.1109/embc.2015.7318980
– year: 2013
  ident: jneabc902bib218
  article-title: Sparse autoencoders for word decoding from magnetoencephalography
– year: 2017
  ident: jneabc902bib220
  article-title: Deep learning for fatigue estimation on the basis of multimodal human-machine interactions
– start-page: 1
  year: 2017
  ident: jneabc902bib116
  article-title: Deep learning with convolutional neural networks for decoding and visualization of EEG pathology
  doi: 10.1109/spmb.2017.8257015
– start-page: 18
  year: 2017
  ident: jneabc902bib170
  article-title: Deep recurrent convolutional neural networks for classifying p300 BCI signals
– start-page: 169
  year: 2018
  ident: jneabc902bib199
  article-title: Brain-computer interface using deep neural network and its application to mobile robot control
  doi: 10.1109/amc.2019.8371082
– start-page: 498
  year: 2017
  ident: jneabc902bib168
  article-title: Deep belief networks for EEG-based concealed information test
  doi: 10.1007/978-3-319-59081-3_58
– start-page: 812
  year: 2015
  ident: jneabc902bib153
  article-title: Feature extraction with deep belief networks for driver’s cognitive states prediction from EEG data
  doi: chinasip.2015.7230517
– volume: 25
  start-page: 566
  year: 2017
  ident: jneabc902bib80
  article-title: A deep learning scheme for motor imagery classification based on restricted Boltzmann machines
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2016.2601240
– start-page: 1
  year: 2018
  ident: jneabc902bib59
  article-title: Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding
  doi: 10.1109/iww-bci.2018.8311493
– volume: 161
  start-page: 103
  year: 2018
  ident: jneabc902bib113
  article-title: Automated EEG-based screening of depression using deep convolutional neural network
  publication-title: Comput. Methods Programs Biomed.
  doi: 10.1016/j.cmpb.2018.04.012
– ident: jneabc902bib70
  article-title: Sleep staging with deep learning: a convolutional model
– volume: 99
  start-page: 24
  year: 2018
  ident: jneabc902bib53
  article-title: A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals
  publication-title: Comput. Biol. Med.
  doi: 10.1016/j.compbiomed.2018.05.019
– volume: 51
  start-page: 112
  year: 2019
  ident: jneabc902bib26
  article-title: A survey on brain biometrics
  publication-title: ACM Comput. Surv.
  doi: 10.1145/3230632
– volume: 110
  start-page: 1842
  year: 1999
  ident: jneabc902bib221
  article-title: Event-related EEG/MEG synchronization and desynchronization: basic principles
  publication-title: Clin. Neurophysiol.
  doi: 10.1016/S1388-2457(99)00141-8
– volume: 35
  start-page: 137
  year: 2007
  ident: jneabc902bib15
  article-title: A comprehensive survey of brain interface technology designs
  publication-title: Ann. Biomed. Eng.
  doi: 10.1007/s10439-006-9170-0
– year: 2018
  ident: jneabc902bib192
  article-title: Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials
  doi: 10.1088/1741-2552/aae5d8
– volume: 43
  start-page: 110
  year: 2003
  ident: jneabc902bib244
  article-title: Studying spontaneous EEG activity with fMRI
  publication-title: Brain Res. Rev.
  doi: 10.1016/S0165-0173(03)00193-0
– volume: 6
  start-page: 25399
  year: 2018
  ident: jneabc902bib124
  article-title: Deep convolution neural network and autoencoders-based unsupervised feature learning of EEG signals
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2018.2833746
– volume: 299
  start-page: 002
  year: 2017
  ident: jneabc902bib90
  article-title: Analyze EEG signals with convolutional neural network based on power spectrum feature selection
  publication-title: Proc. Sci.
  doi: 10.22323/1.299.0002
– year: 2017
  ident: jneabc902bib149
  article-title: Individual recognition in schizophrenia using deep learning methods with random forest and voting classifiers: Insights from resting state EEG streams
– volume: 65
  start-page: 1
  year: 2014
  ident: jneabc902bib189
  article-title: Effect of fatigue on SSVEP during virtual wheelchair navigation
  publication-title: J. Theor. Appl. Inf. Technol.
– start-page: 800
  year: 2016
  ident: jneabc902bib154
  article-title: EEG-based driver fatigue detection using hybrid deep generic model
  doi: 10.1109/embc.2016.7590822
– start-page: 259
  year: 2016
  ident: jneabc902bib60
  article-title: Decoding EEG and LFP signals using deep learning: heading TrueNorth
  doi: 10.1145/2903150.2903159
– start-page: 180
  year: 2018
  ident: jneabc902bib215
  article-title: Multi-channel generative adversarial network for parallel magnetic resonance image reconstruction in k-space
  doi: 10.1007/978-3-030-00928-1_21
– start-page: 802
  year: 2016
  ident: jneabc902bib122
  article-title: Classification of epileptic EEG signals with stacked sparse autoencoder based on deep learning
  doi: 10.1007/978-3-319-42297-8_74
– start-page: 754
  year: 2016
  ident: jneabc902bib118
  article-title: Epileptiform spike detection via convolutional neural networks
  doi: 10.1109/icassp.2016.7471776
– start-page: 1
  year: 2018
  ident: jneabc902bib10
  article-title: Converting your thoughts to texts: enabling brain typing via deep feature learning of EEG signals
  doi: 10.1109/percom.2018.8444575
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Snippet Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or...
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SubjectTerms Algorithms
Brain
Brain-Computer Interfaces
Deep Learning
Electroencephalography - methods
Humans
Title A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers
URI https://www.ncbi.nlm.nih.gov/pubmed/33171452
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Volume 18
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