A multi-source classification framework with invariant representation reconstruction for dual-target RSVP-BCI tasks in cross-subject scenario
The Rapid Serial Visual Presentation (RSVP) is a widely used paradigm for target detection tasks in Brain-Computer Interface (BCI) by decoding Electroencephalogram (EEG) signals. One major issue concerns the time-consuming calibration in cross-subject scenarios, which worsens in dual-target RSVP-BCI...
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| Published in | Neurocomputing (Amsterdam) Vol. 620; p. 129239 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.03.2025
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| Abstract | The Rapid Serial Visual Presentation (RSVP) is a widely used paradigm for target detection tasks in Brain-Computer Interface (BCI) by decoding Electroencephalogram (EEG) signals. One major issue concerns the time-consuming calibration in cross-subject scenarios, which worsens in dual-target RSVP-BCI tasks. A new method is desperately needed to detect two targets further with less calibration time. This paper proposed a novel framework named Cross-subject Invariant Representation Extraction-Targeted Stacked Convolutional Autoencoder (CS-IRE-TSCAE) based on reconstructing the invariant representation. After filtering the source subjects, the CS-TSCAE alleviates the subject-dependent effect by reconstructing the invariant representation generated by CS-IRE. It was validated on the ERP datasets from the BCI Controlled Robot Contest 2022. The experimental result showed that CS-IRE-TSCAE obtained the highest Recall, F1 and Average ACC with significant differences both in subject-dependent and inter-subject experiments. It demonstrated that CS-IRE-TSCAE achieved a higher classification performance for dual-target RSVP with less calibration time. Our framework drives the application development of target detection in RSVP-BCI by facilitating multiple target detection in cross-subject scenarios, which has practical significance, especially in fast-deployment scenarios. |
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| AbstractList | The Rapid Serial Visual Presentation (RSVP) is a widely used paradigm for target detection tasks in Brain-Computer Interface (BCI) by decoding Electroencephalogram (EEG) signals. One major issue concerns the time-consuming calibration in cross-subject scenarios, which worsens in dual-target RSVP-BCI tasks. A new method is desperately needed to detect two targets further with less calibration time. This paper proposed a novel framework named Cross-subject Invariant Representation Extraction-Targeted Stacked Convolutional Autoencoder (CS-IRE-TSCAE) based on reconstructing the invariant representation. After filtering the source subjects, the CS-TSCAE alleviates the subject-dependent effect by reconstructing the invariant representation generated by CS-IRE. It was validated on the ERP datasets from the BCI Controlled Robot Contest 2022. The experimental result showed that CS-IRE-TSCAE obtained the highest Recall, F1 and Average ACC with significant differences both in subject-dependent and inter-subject experiments. It demonstrated that CS-IRE-TSCAE achieved a higher classification performance for dual-target RSVP with less calibration time. Our framework drives the application development of target detection in RSVP-BCI by facilitating multiple target detection in cross-subject scenarios, which has practical significance, especially in fast-deployment scenarios. |
| ArticleNumber | 129239 |
| Author | Zhang, Yueqi Chen, Hongying Chen, Yuanfang Wang, Dan Chen, Jiaming Xu, Meng |
| Author_xml | – sequence: 1 givenname: Hongying orcidid: 0000-0001-8367-6791 surname: Chen fullname: Chen, Hongying organization: the Beijing University of Technology, China – sequence: 2 givenname: Dan surname: Wang fullname: Wang, Dan email: wangdan@bjut.edu.cn organization: the Beijing University of Technology, China – sequence: 3 givenname: Meng surname: Xu fullname: Xu, Meng organization: the Beijing University of Technology, China – sequence: 4 givenname: Jiaming surname: Chen fullname: Chen, Jiaming organization: the Beijing University of Technology, China – sequence: 5 givenname: Yueqi surname: Zhang fullname: Zhang, Yueqi organization: the Beijing University of Technology, China – sequence: 6 givenname: Yuanfang surname: Chen fullname: Chen, Yuanfang organization: the Beijing Machine and Equipment Institute, China |
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| Cites_doi | 10.1109/TNSRE.2019.2956488 10.1109/TBME.2021.3137184 10.1109/TNSRE.2020.2980299 10.1088/1741-2552/aa9817 10.1109/TBME.2018.2889705 10.1109/TBCAS.2019.2929053 10.26599/BSA.2022.9050007 10.1109/TCDS.2020.3007453 10.1037/0096-1523.7.5.937 10.1002/hbm.23730 10.1007/978-3-319-58628-1_4 10.1109/TNSRE.2020.3023761 10.1109/TNSRE.2022.3145515 10.1109/EMBC.2019.8857082 10.1109/TPAMI.2009.57 10.1088/1741-2552/aace8c 10.1109/TSP.2019.2894801 10.1109/TBME.2019.2913914 10.1109/TBME.2020.2965178 10.1109/MSP.2008.4408447 10.1109/TCYB.2018.2821764 10.1016/j.asoc.2019.105689 10.1088/1741-2552/ab598f 10.1109/CBS.2018.8612222 10.1109/TBME.2017.2742541 10.1109/TMRB.2019.2959559 10.3390/s22093331 10.1109/NER.2017.8008395 10.1109/NER.2015.7146596 10.1038/s41928-022-00913-9 10.1109/TPAMI.2018.2868685 10.1109/PRNI.2018.8423951 10.1109/ACCESS.2019.2930958 10.1109/IWW-BCI.2018.8311491 10.1109/CISP-BMEI53629.2021.9624450 10.3389/fnhum.2019.00210 10.1109/CVPR.2010.5539857 10.1016/j.neucom.2020.09.017 10.1109/ICOT.2017.8336126 10.1186/s12911-018-0693-8 |
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| References | Wei (bib38) Dec. 2018; 18 M. Völker et al., Deep transfer learning for error decoding from non-invasive EEG,2018 6th International Conference on Brain-Computer Interface (BCI). vol., no., pp. 1-6, 15-17, Jan. 2018. Xu (bib18) 2022; 30 Lees (bib10) Apr. 2018; 15 Schirrmeister (bib41) Dec. 2017; 38 Tang (bib1) Feb. 2023; 6 Xie (bib15) 2019; 49 Värbu (bib3) 2022; 22 Ono, Fau - Weber, Weber (bib7) 1995; 7 Rodrigues (bib28) 2019; 66 Y. Tan et al., A Convolution Network of Multi-Windows Spatial-Temporal Feature Analysis For Single-trial EEG Classification in RSVP Task,2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). vol., no., pp. 1-6, 23-25 Oct. 2021. J. He et al., Boosting Transfer Learning Improves Performance of Driving Drowsiness Classification Using EEG,2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI). pp. 1-4, 12-14 June. 2018. E. Tzeng et al., Deep Domain Confusion: Maximizing for Domain Invariance,ArXiv. vol. abs/1412.3474, 2014. Zanini (bib27) 2018; 65 Zhang (bib11) Jun. 2022; 8 Y. Wang et al., Epileptic Signal Classification with Deep Transfer Learning Feature on Mean Amplitude Spectrum,2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). vol., no., pp. 2392-2395, 23-27 July. 2019. M. Xu et al., Inter-subject information contributes to the ERP classification in the P300 speller,2015 7th International IEEE/EMBS Conference on Neural Engineering (NER). pp. 206-209, 22-24 April. 2015. Yair (bib29) 2019; 67 Y. Liu et al., The Application of Transfer Learning in P300 Detection,2018 IEEE International Conference on Cyborg and Bionic Systems (CBS). vol., no., pp. 412-417, 25-27 Oct. 2018. He, Wu (bib31) 2020; 28 Ming (bib17) Dec. 2019; 84 Y.M. Jin et al., EEG-based emotion recognition using domain adaptation network,2017 International Conference on Orange Technologies (ICOT). vol., no., pp. 222-225, 8-10 Dec. 2017. J. Hou et al., Improving the P300-based brain-computer interface with transfer learning,2017 8th International IEEE/EMBS Conference on Neural Engineering (NER). vol., no., pp. 485-488, 25-28 May. 2017. He, Fau - Wu, Wu (bib30) 2020; 67 Jin, Fau - Chen (bib5) 2020; 67 C. a S. Tan, Fuchun and Zhang, Wenchang, Deep Transfer Learning for EEG-based Brain Computer Interface,ICASSP, 2018. Y. Yao and G. Doretto, Boosting for transfer learning with multiple sources,2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. vol., no., pp. 1855-1862, 13-18 June. 2010. Bruzzone, Marconcini (bib24) 2010; 32 Lawhern (bib40) Jul. 2018; 15 Autthasan (bib4) Jun. 2022; 69 Wei (bib12) 2020; 28 Hajinoroozi (bib43) 2017 Parra (bib6) 2008; 25 Wang (bib9) 2020; 17 A. Barachant and M. Congedo, A Plug&Play P300 BCI Using Information Geometry,ArXiv. vol. abs/1409.0107, no., pp., 2014. Yang (bib23) 2020; 1601 Xu (bib33) 2019; 7 Kundu, Ari (bib37) 2020; 2 Daoud, Bayoumi (bib35) 2019; 13 Wu (bib26) 2022; 14 Vourvopoulos (bib2) 2019; 13 Long (bib13) 2019; 41 Jin (bib8) 2020; 28 Wan (bib32) Jan. 2021; 421 Rodrigues (10.1016/j.neucom.2024.129239_bib28) 2019; 66 Xu (10.1016/j.neucom.2024.129239_bib33) 2019; 7 Xie (10.1016/j.neucom.2024.129239_bib15) 2019; 49 Long (10.1016/j.neucom.2024.129239_bib13) 2019; 41 Schirrmeister (10.1016/j.neucom.2024.129239_bib41) 2017; 38 Xu (10.1016/j.neucom.2024.129239_bib18) 2022; 30 10.1016/j.neucom.2024.129239_bib25 Ono (10.1016/j.neucom.2024.129239_bib7) 1995; 7 Bruzzone (10.1016/j.neucom.2024.129239_bib24) 2010; 32 Wan (10.1016/j.neucom.2024.129239_bib32) 2021; 421 Lees (10.1016/j.neucom.2024.129239_bib10) 2018; 15 Jin (10.1016/j.neucom.2024.129239_bib8) 2020; 28 Zanini (10.1016/j.neucom.2024.129239_bib27) 2018; 65 Lawhern (10.1016/j.neucom.2024.129239_bib40) 2018; 15 Yair (10.1016/j.neucom.2024.129239_bib29) 2019; 67 Jin (10.1016/j.neucom.2024.129239_bib5) 2020; 67 Zhang (10.1016/j.neucom.2024.129239_bib11) 2022; 8 Tang (10.1016/j.neucom.2024.129239_bib1) 2023; 6 Wang (10.1016/j.neucom.2024.129239_bib9) 2020; 17 10.1016/j.neucom.2024.129239_bib19 Hajinoroozi (10.1016/j.neucom.2024.129239_bib43) 2017 He (10.1016/j.neucom.2024.129239_bib31) 2020; 28 Wei (10.1016/j.neucom.2024.129239_bib38) 2018; 18 Wei (10.1016/j.neucom.2024.129239_bib12) 2020; 28 10.1016/j.neucom.2024.129239_bib39 10.1016/j.neucom.2024.129239_bib16 Ming (10.1016/j.neucom.2024.129239_bib17) 2019; 84 Wu (10.1016/j.neucom.2024.129239_bib26) 2022; 14 10.1016/j.neucom.2024.129239_bib14 10.1016/j.neucom.2024.129239_bib36 10.1016/j.neucom.2024.129239_bib34 10.1016/j.neucom.2024.129239_bib22 10.1016/j.neucom.2024.129239_bib44 10.1016/j.neucom.2024.129239_bib21 Yang (10.1016/j.neucom.2024.129239_bib23) 2020; 1601 10.1016/j.neucom.2024.129239_bib20 10.1016/j.neucom.2024.129239_bib42 Värbu (10.1016/j.neucom.2024.129239_bib3) 2022; 22 Parra (10.1016/j.neucom.2024.129239_bib6) 2008; 25 Autthasan (10.1016/j.neucom.2024.129239_bib4) 2022; 69 Kundu (10.1016/j.neucom.2024.129239_bib37) 2020; 2 He (10.1016/j.neucom.2024.129239_bib30) 2020; 67 Daoud (10.1016/j.neucom.2024.129239_bib35) 2019; 13 Vourvopoulos (10.1016/j.neucom.2024.129239_bib2) 2019; 13 |
| References_xml | – volume: 15 year: Jul. 2018 ident: bib40 article-title: EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces publication-title: J. Neural Eng. – reference: C. a S. Tan, Fuchun and Zhang, Wenchang, Deep Transfer Learning for EEG-based Brain Computer Interface,ICASSP, 2018. – start-page: 45 year: 2017 end-page: 55 ident: bib43 article-title: Deep Transfer Learning for Cross-subject and Cross-experiment Prediction of Image Rapid Serial Visual Presentation Events from EEG Data publication-title: Augment. Cogn. Neurocognition Mach. Learn. – volume: 15 year: Apr. 2018 ident: bib10 article-title: A review of rapid serial visual presentation-based brain-computer interfaces publication-title: J. Neural Eng. – volume: 2 start-page: 86 year: 2020 end-page: 93 ident: bib37 article-title: MsCNN: A Deep Learning Framework for P300-Based Brain–Computer Interface Speller publication-title: IEEE Trans. Med. Robot. Bionics – reference: Y. Liu et al., The Application of Transfer Learning in P300 Detection,2018 IEEE International Conference on Cyborg and Bionic Systems (CBS). vol., no., pp. 412-417, 25-27 Oct. 2018. – reference: M. Xu et al., Inter-subject information contributes to the ERP classification in the P300 speller,2015 7th International IEEE/EMBS Conference on Neural Engineering (NER). pp. 206-209, 22-24 April. 2015. – reference: A. Barachant and M. Congedo, A Plug&Play P300 BCI Using Information Geometry,ArXiv. vol. abs/1409.0107, no., pp., 2014. – volume: 65 start-page: 1107 year: 2018 end-page: 1116 ident: bib27 article-title: Transfer Learning: A Riemannian Geometry Framework With Applications to Brain–Computer Interfaces publication-title: IEEE Trans. Biomed. Eng. – volume: 38 start-page: 5391 year: Dec. 2017 end-page: 5420 ident: bib41 article-title: Deep learning with convolutional neural networks for EEG decoding and visualization publication-title: Hum. Brain Mapp. – volume: 69 start-page: 2105 year: Jun. 2022 end-page: 2118 ident: bib4 article-title: MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification publication-title: IEEE Trans. Biomed. Eng. – volume: 28 start-page: 1091 year: 2020 end-page: 1108 ident: bib31 article-title: Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 67 start-page: 2585 year: 2020 end-page: 2593 ident: bib5 article-title: Developing a Novel Tactile P300 Brain-Computer Interface With a Cheeks-Stim Paradigm publication-title: IEEE Trans. Biomed. Eng. – volume: 25 start-page: 107 year: 2008 end-page: 115 ident: bib6 article-title: Spatiotemporal Linear Decoding of Brain State publication-title: IEEE Signal Process. Mag. – volume: 421 start-page: 1 year: Jan. 2021 end-page: 14 ident: bib32 article-title: A review on transfer learning in EEG signal analysis publication-title: Neurocomputing – volume: 13 year: 2019 ident: bib2 article-title: Effects of a brain-computer interface with virtual reality (VR) neurofeedback: a pilot study in chronic stroke patients publication-title: Front. Hum. Neurosci. – volume: 17 year: 2020 ident: bib9 article-title: Enhance decoding of pre-movement EEG patterns for brain-computer interfaces publication-title: J. Neural Eng. – volume: 18 start-page: 111 year: Dec. 2018 ident: bib38 article-title: Automatic seizure detection using three-dimensional CNN based on multi-channel EEG publication-title: BMC Med. Inform. Decis. Mak. – volume: 32 start-page: 770 year: 2010 end-page: 787 ident: bib24 article-title: Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: E. Tzeng et al., Deep Domain Confusion: Maximizing for Domain Invariance,ArXiv. vol. abs/1412.3474, 2014. – volume: 22 start-page: 3331 year: 2022 ident: bib3 article-title: past, present, and future of EEG-based BCI applications publication-title: Sensors – volume: 28 start-page: 3 year: 2020 end-page: 12 ident: bib8 article-title: The Study of Generic Model Set for Reducing Calibration Time in P300-Based Brain–Computer Interface publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – reference: J. Hou et al., Improving the P300-based brain-computer interface with transfer learning,2017 8th International IEEE/EMBS Conference on Neural Engineering (NER). vol., no., pp. 485-488, 25-28 May. 2017. – volume: 67 start-page: 1797 year: 2019 end-page: 1811 ident: bib29 article-title: Parallel Transport on the Cone Manifold of SPD Matrices for Domain Adaptation publication-title: IEEE Trans. Signal Process. – reference: Y. Wang et al., Epileptic Signal Classification with Deep Transfer Learning Feature on Mean Amplitude Spectrum,2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). vol., no., pp. 2392-2395, 23-27 July. 2019. – volume: 7 start-page: 112767 year: 2019 end-page: 112776 ident: bib33 article-title: A Deep Transfer Convolutional Neural Network Framework for EEG Signal Classification publication-title: IEEE Access – volume: 7 start-page: 937 year: 1995 end-page: 947 ident: bib7 article-title: Journal of experimental psychology: human perception and performance publication-title: J. Exp. Psychol. Hum. Percept. Perform. – reference: J. He et al., Boosting Transfer Learning Improves Performance of Driving Drowsiness Classification Using EEG,2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI). pp. 1-4, 12-14 June. 2018. – volume: 67 start-page: 399 year: 2020 end-page: 410 ident: bib30 article-title: Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach publication-title: IEEE Trans. Biomed. Eng. – volume: 8 start-page: 111 year: Jun. 2022 end-page: 126 ident: bib11 article-title: An improved EEGNet for single-trial EEG classification in rapid serial visual presentation task publication-title: Brain Sci. Adv. – volume: 49 start-page: 2200 year: 2019 end-page: 2214 ident: bib15 article-title: Generalized Hidden-Mapping Transductive Transfer Learning for Recognition of Epileptic Electroencephalogram Signals publication-title: IEEE Trans. Cybern. – volume: 1601 year: 2020 ident: bib23 article-title: Improving Session-to-session Transfer Performance of Emotion Recognition Using Adaptive Support Vector Machine publication-title: J. Phys.: Conf. Ser. – reference: M. Völker et al., Deep transfer learning for error decoding from non-invasive EEG,2018 6th International Conference on Brain-Computer Interface (BCI). vol., no., pp. 1-6, 15-17, Jan. 2018. – volume: 28 start-page: 2344 year: 2020 end-page: 2355 ident: bib12 article-title: Reducing Calibration Efforts in RSVP Tasks With Multi-Source Adversarial Domain Adaptation publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 6 start-page: 109 year: Feb. 2023 end-page: 118 ident: bib1 article-title: Flexible brain–computer interfaces publication-title: Nat. Electron. – volume: 30 start-page: 251 year: 2022 end-page: 263 ident: bib18 article-title: BWGAN-GP: An EEG Data Generation Method for Class Imbalance Problem in RSVP Tasks publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 41 start-page: 3071 year: 2019 end-page: 3085 ident: bib13 article-title: Transferable Representation Learning with Deep Adaptation Networks publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 84 year: Dec. 2019 ident: bib17 article-title: Subject adaptation network for EEG data analysis publication-title: Appl. Soft Comput. – volume: 14 start-page: 4 year: 2022 end-page: 19 ident: bib26 article-title: Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 publication-title: IEEE Trans. Cogn. Dev. Syst. – reference: Y.M. Jin et al., EEG-based emotion recognition using domain adaptation network,2017 International Conference on Orange Technologies (ICOT). vol., no., pp. 222-225, 8-10 Dec. 2017. – volume: 13 start-page: 804 year: 2019 end-page: 813 ident: bib35 article-title: Efficient Epileptic Seizure Prediction Based on Deep Learning publication-title: IEEE Trans. Biomed. Circuits Syst. – reference: Y. Tan et al., A Convolution Network of Multi-Windows Spatial-Temporal Feature Analysis For Single-trial EEG Classification in RSVP Task,2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). vol., no., pp. 1-6, 23-25 Oct. 2021. – reference: Y. Yao and G. Doretto, Boosting for transfer learning with multiple sources,2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. vol., no., pp. 1855-1862, 13-18 June. 2010. – volume: 66 start-page: 2390 year: 2019 end-page: 2401 ident: bib28 article-title: Riemannian Procrustes Analysis: Transfer Learning for Brain–Computer Interfaces publication-title: IEEE Trans. Biomed. Eng. – volume: 28 start-page: 3 issue: 1 year: 2020 ident: 10.1016/j.neucom.2024.129239_bib8 article-title: The Study of Generic Model Set for Reducing Calibration Time in P300-Based Brain–Computer Interface publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2019.2956488 – volume: 69 start-page: 2105 issue: 6 year: 2022 ident: 10.1016/j.neucom.2024.129239_bib4 article-title: MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2021.3137184 – volume: 1601 year: 2020 ident: 10.1016/j.neucom.2024.129239_bib23 article-title: Improving Session-to-session Transfer Performance of Emotion Recognition Using Adaptive Support Vector Machine publication-title: J. Phys.: Conf. Ser. – volume: 28 start-page: 1091 issue: 5 year: 2020 ident: 10.1016/j.neucom.2024.129239_bib31 article-title: Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2020.2980299 – volume: 15 issue: 2 year: 2018 ident: 10.1016/j.neucom.2024.129239_bib10 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: 66 start-page: 2390 issue: 8 year: 2019 ident: 10.1016/j.neucom.2024.129239_bib28 article-title: Riemannian Procrustes Analysis: Transfer Learning for Brain–Computer Interfaces publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2018.2889705 – volume: 13 start-page: 804 issue: 5 year: 2019 ident: 10.1016/j.neucom.2024.129239_bib35 article-title: Efficient Epileptic Seizure Prediction Based on Deep Learning publication-title: IEEE Trans. Biomed. Circuits Syst. doi: 10.1109/TBCAS.2019.2929053 – volume: 8 start-page: 111 issue: 2 year: 2022 ident: 10.1016/j.neucom.2024.129239_bib11 article-title: An improved EEGNet for single-trial EEG classification in rapid serial visual presentation task publication-title: Brain Sci. Adv. doi: 10.26599/BSA.2022.9050007 – volume: 14 start-page: 4 issue: 1 year: 2022 ident: 10.1016/j.neucom.2024.129239_bib26 article-title: Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 publication-title: IEEE Trans. Cogn. Dev. Syst. doi: 10.1109/TCDS.2020.3007453 – volume: 7 start-page: 937 issue: 5 year: 1995 ident: 10.1016/j.neucom.2024.129239_bib7 article-title: Journal of experimental psychology: human perception and performance publication-title: J. Exp. Psychol. Hum. Percept. Perform. doi: 10.1037/0096-1523.7.5.937 – volume: 38 start-page: 5391 issue: 11 year: 2017 ident: 10.1016/j.neucom.2024.129239_bib41 article-title: Deep learning with convolutional neural networks for EEG decoding and visualization publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.23730 – start-page: 45 year: 2017 ident: 10.1016/j.neucom.2024.129239_bib43 article-title: Deep Transfer Learning for Cross-subject and Cross-experiment Prediction of Image Rapid Serial Visual Presentation Events from EEG Data publication-title: Augment. Cogn. Neurocognition Mach. Learn. doi: 10.1007/978-3-319-58628-1_4 – volume: 28 start-page: 2344 issue: 11 year: 2020 ident: 10.1016/j.neucom.2024.129239_bib12 article-title: Reducing Calibration Efforts in RSVP Tasks With Multi-Source Adversarial Domain Adaptation publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2020.3023761 – volume: 30 start-page: 251 year: 2022 ident: 10.1016/j.neucom.2024.129239_bib18 article-title: BWGAN-GP: An EEG Data Generation Method for Class Imbalance Problem in RSVP Tasks publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2022.3145515 – ident: 10.1016/j.neucom.2024.129239_bib36 doi: 10.1109/EMBC.2019.8857082 – volume: 32 start-page: 770 issue: 5 year: 2010 ident: 10.1016/j.neucom.2024.129239_bib24 article-title: Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2009.57 – volume: 15 issue: 5 year: 2018 ident: 10.1016/j.neucom.2024.129239_bib40 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: 67 start-page: 1797 issue: 7 year: 2019 ident: 10.1016/j.neucom.2024.129239_bib29 article-title: Parallel Transport on the Cone Manifold of SPD Matrices for Domain Adaptation publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2019.2894801 – volume: 67 start-page: 399 issue: 2 year: 2020 ident: 10.1016/j.neucom.2024.129239_bib30 article-title: Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2019.2913914 – volume: 67 start-page: 2585 issue: 9 year: 2020 ident: 10.1016/j.neucom.2024.129239_bib5 article-title: Developing a Novel Tactile P300 Brain-Computer Interface With a Cheeks-Stim Paradigm publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2020.2965178 – volume: 25 start-page: 107 issue: 1 year: 2008 ident: 10.1016/j.neucom.2024.129239_bib6 article-title: Spatiotemporal Linear Decoding of Brain State publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2008.4408447 – ident: 10.1016/j.neucom.2024.129239_bib44 – volume: 49 start-page: 2200 issue: 6 year: 2019 ident: 10.1016/j.neucom.2024.129239_bib15 article-title: Generalized Hidden-Mapping Transductive Transfer Learning for Recognition of Epileptic Electroencephalogram Signals publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2018.2821764 – volume: 84 year: 2019 ident: 10.1016/j.neucom.2024.129239_bib17 article-title: Subject adaptation network for EEG data analysis publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2019.105689 – volume: 17 issue: 1 year: 2020 ident: 10.1016/j.neucom.2024.129239_bib9 article-title: Enhance decoding of pre-movement EEG patterns for brain-computer interfaces publication-title: J. Neural Eng. doi: 10.1088/1741-2552/ab598f – ident: 10.1016/j.neucom.2024.129239_bib16 – ident: 10.1016/j.neucom.2024.129239_bib20 doi: 10.1109/CBS.2018.8612222 – volume: 65 start-page: 1107 issue: 5 year: 2018 ident: 10.1016/j.neucom.2024.129239_bib27 article-title: Transfer Learning: A Riemannian Geometry Framework With Applications to Brain–Computer Interfaces publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2017.2742541 – volume: 2 start-page: 86 issue: 1 year: 2020 ident: 10.1016/j.neucom.2024.129239_bib37 article-title: MsCNN: A Deep Learning Framework for P300-Based Brain–Computer Interface Speller publication-title: IEEE Trans. Med. Robot. Bionics doi: 10.1109/TMRB.2019.2959559 – volume: 22 start-page: 3331 issue: 9 year: 2022 ident: 10.1016/j.neucom.2024.129239_bib3 article-title: past, present, and future of EEG-based BCI applications publication-title: Sensors doi: 10.3390/s22093331 – ident: 10.1016/j.neucom.2024.129239_bib19 doi: 10.1109/NER.2017.8008395 – ident: 10.1016/j.neucom.2024.129239_bib25 doi: 10.1109/NER.2015.7146596 – ident: 10.1016/j.neucom.2024.129239_bib39 – volume: 6 start-page: 109 issue: 2 year: 2023 ident: 10.1016/j.neucom.2024.129239_bib1 article-title: Flexible brain–computer interfaces publication-title: Nat. Electron. doi: 10.1038/s41928-022-00913-9 – volume: 41 start-page: 3071 issue: 12 year: 2019 ident: 10.1016/j.neucom.2024.129239_bib13 article-title: Transferable Representation Learning with Deep Adaptation Networks publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2018.2868685 – ident: 10.1016/j.neucom.2024.129239_bib22 doi: 10.1109/PRNI.2018.8423951 – volume: 7 start-page: 112767 year: 2019 ident: 10.1016/j.neucom.2024.129239_bib33 article-title: A Deep Transfer Convolutional Neural Network Framework for EEG Signal Classification publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2930958 – ident: 10.1016/j.neucom.2024.129239_bib34 doi: 10.1109/IWW-BCI.2018.8311491 – ident: 10.1016/j.neucom.2024.129239_bib42 doi: 10.1109/CISP-BMEI53629.2021.9624450 – volume: 13 year: 2019 ident: 10.1016/j.neucom.2024.129239_bib2 article-title: Effects of a brain-computer interface with virtual reality (VR) neurofeedback: a pilot study in chronic stroke patients publication-title: Front. Hum. Neurosci. doi: 10.3389/fnhum.2019.00210 – ident: 10.1016/j.neucom.2024.129239_bib21 doi: 10.1109/CVPR.2010.5539857 – volume: 421 start-page: 1 year: 2021 ident: 10.1016/j.neucom.2024.129239_bib32 article-title: A review on transfer learning in EEG signal analysis publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.09.017 – ident: 10.1016/j.neucom.2024.129239_bib14 doi: 10.1109/ICOT.2017.8336126 – volume: 18 start-page: 111 issue: 5 year: 2018 ident: 10.1016/j.neucom.2024.129239_bib38 article-title: Automatic seizure detection using three-dimensional CNN based on multi-channel EEG publication-title: BMC Med. Inform. Decis. Mak. doi: 10.1186/s12911-018-0693-8 |
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