{"id":1062,"date":"2020-07-13T11:48:26","date_gmt":"2020-07-13T03:48:26","guid":{"rendered":"https:\/\/www.fst.um.edu.mo\/personal\/?page_id=1062"},"modified":"2026-03-02T22:29:07","modified_gmt":"2026-03-02T14:29:07","slug":"fwan","status":"publish","type":"page","link":"https:\/\/www.fst.um.edu.mo\/personal\/fwan\/","title":{"rendered":"Feng WAN"},"content":{"rendered":"\t\t
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<\/td>\nFeng WAN\u842c \u5cf0
Associate Professor<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

Academic Qualification<\/h3>\n
    \n
  • Ph.D in Electrical and Electronics Engineering, The Hong Kong University of Science and Technology, Hong Kong<\/li>\n
  • M.Eng. in Control Theory and Engineering, Institute of Industrial Process Control, Zhejiang University, China<\/li>\n
  • B.Eng. in Material Science and Engineering & B.Eng. in Electronics Engineering, Tianjin University, China<\/li>\n<\/ul>\n
    \n

    Teaching<\/h3>\n

    B.Sc. Courses<\/h4>\n
      \n
    1. Quality Control (ELEC200) <\/span><\/li>\n
    2. Project on Social Awareness (HONR2004)
      <\/span><\/li>\n
    3. Probability and Statistics (ECEN2007) <\/span><\/li>\n
    4. Control Systems (ECEN3000) <\/span><\/li>\n
    5. Computer and Microprocessor Control Systems (ELEC409) <\/span><\/li>\n
    6. Graduation Project I (ECEN4000)<\/li>\n
    7. Graduation Project II (ECEN4001)<\/li>\n
    8. Honours Project (HONR4000)<\/span><\/li>\n<\/ol>\n

      M.Sc. and PhD Courses<\/span><\/h4>\n
        \n
      1. Introduction to Research (ECEN7001)<\/span><\/li>\n
      2. Advanced Topics in Control Systems (ECEN7006)<\/span><\/li>\n
      3. Expert Systems (ECEN7008)<\/span><\/li>\n
      4. Advanced Topics in Applied Probability and Statistics (ECEN7102)
        <\/span><\/li>\n
      5. Advanced Signal Processing and Analysis (ELCE801)<\/span><\/li>\n
      6. Computational Intelligence and Intelligent Control (ELCE802)<\/span><\/li>\n
      7. Advanced Topics in Electrical and Computer Engineering (ECEN8001)<\/span><\/li>\n
      8. Thesis (ECEN7999)<\/span><\/li>\n
      9. Thesis (ECEN8999)<\/span><\/li>\n<\/ol>\n
        \n

        Research<\/h3>\n

        Research Interests<\/h4>\n
          \n
        • Biomedical Engineering: Brain-Computer Interfaces, Biomedical Signal Processing, Brain Imaging and Neurofeedback Training<\/li>\n
        • Computational Intelligence and Machine Learning: Fuzzy Systems, Neural Networks, Deep and Transfer Learning<\/li>\n
        • Control: Intelligent Control, Nonlinear and Adaptive Control<\/li>\n<\/ul>\n

          Recent Research Projects<\/h4>\n
            \n
          • Research on Key Technologies of Practical Brain-Computer Interface for Clinical Applications, FDCT-MOST Joint Research Project, Principal Investigator, 2025-2027.<\/li>\n
          • Fast Brain-Computer Interfaces with Deep Learning Techniques, funded by the University of Macau Development Fund, Principal Investigator, 2025-2026.<\/li>\n
          • Mitigating Inattentional Blindness Using AR+BCI+AI, International Collaborative Research Project funded by the University of Macau, Principal Investigator, 2025-2026.<\/li>\n
          • Spatio-Temporal Connection Mode on Dynamic Brain Cognitive Activities, funded by Guangdong Basic and Applied Basic Research Foundation, Principal Investigator, 2023-2025.<\/li>\n
          • Plug-and-Play High Performance SSVEP-based BCIs, funded by the University of Macau, Principal Investigator, 2023-2024.<\/li>\n
          • Decision-Making: EEG-Based Brain Network, Prediction and Intervention, FDCT-NSFC Joint Scientific Research Project, Principal Investigator, 2019-2022.<\/li>\n<\/ul>\n
            \n

            Awards<\/h3>\n
              \n
            • 2020 World Robot Conference Brain-Computer Interfaces Contest \/ The Fourth China Brain-Computer Interfaces Contest (advised by NSFC, organized by NSFC-DIFS, Chinese Institute of Electronics, and Tsinghua University):\n
                \n
              • Technical Competitions (in total 4 paradigms, for real-time performance)
                (1) Grand Champion\/Grand Prize (tied with Tsinghua University – Beijing Post and Telecommunication University Joint Team)
                (2) Event-Related Potentials-Based Brain-Computer Interfaces Competition: Champion\/First Prize<\/li>\n
              • Technical Championships (in total 7 paradigms, for best performance)
                (1) SSVEP-Based Brain-Computer Interfaces (with calibration) Competition: Champion
                (2) SSVEP-Based Brain-Computer Interfaces (without calibration) Competition: Champion
                (3) Event-Related Potentials-Based Brain-Computer Interfaces (with calibration) Competition: Champion
                (4) Event-Related Potentials-Based Brain-Computer Interfaces (without calibration) Competition: Champion<\/li>\n<\/ul>\n

                2020\u4e16\u754c\u6a5f\u5668\u4eba\u5927\u6703\u8166\u63a7\u6a5f\u5668\u4eba\u5927\u8cfd\u66a8\u7b2c\u56db\u5c46\u4e2d\u570b\u8166\u6a5f\u63a5\u53e3\u6bd4\u8cfd\uff08\u570b\u5bb6\u81ea\u7136\u79d1\u5b78\u57fa\u91d1\u59d4\u54e1\u6703\u6307\u5c0e\u3001\u570b\u5bb6\u81ea\u7136\u79d1\u5b78\u57fa\u91d1\u59d4\u54e1\u6703\u4fe1\u606f\u79d1\u5b78\u90e8\u3001\u4e2d\u570b\u96fb\u5b50\u5b78\u6703\u53ca\u6e05\u83ef\u5927\u5b78\u4e3b\u8fa6\uff09\uff0c\u7372\uff1a<\/p>\n

                  \n
                • \u6280\u8853\u8cfd\uff08\u5171\u56db\u500b\u55ae\u9805\u8cfd\uff0c\u8003\u5bdf\u5be6\u6642\u6027\u80fd\uff09
                  (1) \u7e3d\u51a0\u8ecd\/\u7279\u7b49\u734e\uff08\u8207\u6e05\u83ef\u5927\u5b78-\u5317\u4eac\u90f5\u96fb\u5927\u5b78\u806f\u968a\u4e26\u5217\uff09
                  (2) \u9802\u8449\u8166\u6a5f\u7d44\/\u57fa\u65bc\u4e8b\u4ef6\u76f8\u95dc\u96fb\u4f4d\u7684\u8166\u6a5f\u63a5\u53e3\u7cfb\u7d71\u8cfd\u51a0\u8ecd\/\u4e00\u7b49\u734e<\/li>\n
                • \u6280\u8853\u9326\u6a19\u8cfd\uff08\u5171\u4e03\u500b\u55ae\u9805\u8cfd\uff0c\u8003\u5bdf\u6700\u4f73\u4fe1\u606f\u50b3\u8f38\u7387\uff09
                  (1) \u6795\u8449\u8166\u6a5f\u7d44\/\u57fa\u65bc\u7a69\u614b\u8996\u89ba\u8a98\u767c\u96fb\u4f4d\u7684\u8166\u6a5f\u63a5\u53e3\u7cfb\u7d71\u8cfd\uff08\u6709\u8a13\u7df4\u96c6\uff09\u7b2c\u4e00\u540d\/\u512a\u52dd\u734e
                  (2) \u6795\u8449\u8166\u6a5f\u7d44\/\u57fa\u65bc\u7a69\u614b\u8996\u89ba\u8a98\u767c\u96fb\u4f4d\u7684\u8166\u6a5f\u63a5\u53e3\u7cfb\u7d71\u8cfd\uff08\u7121\u8a13\u7df4\u96c6\uff09\u7b2c\u4e00\u540d\/\u512a\u52dd\u734e
                  (3) \u9802\u8449\u8166\u6a5f\u7d44\/\u57fa\u65bc\u4e8b\u4ef6\u76f8\u95dc\u96fb\u4f4d\u7684\u8166\u6a5f\u63a5\u53e3\u7cfb\u7d71\u8cfd\uff08\u6709\u8a13\u7df4\u96c6\uff09\u7b2c\u4e00\u540d\/\u512a\u52dd\u734e
                  (4) \u9802\u8449\u8166\u6a5f\u7d44\/\u57fa\u65bc\u4e8b\u4ef6\u76f8\u95dc\u96fb\u4f4d\u7684\u8166\u6a5f\u63a5\u53e3\u7cfb\u7d71\u8cfd\uff08\u7121\u8a13\u7df4\u96c6\uff09\u7b2c\u4e00\u540d\/\u512a\u52dd\u734e<\/li>\n<\/ul>\n<\/li>\n
                • 2019 World Robot Conference Brain-Computer Interfaces Contest \/ The Third China Brain-Computer Interfaces Contest (advised by NSFC, organized by NSFC-DIFS, Chinese Institute of Electronics, and Tsinghua University, more than 400 teams participated in 3 technical competitions):
                  (1) Grand Champion\/Grand Prize, (2) Brain-Computer Interfaces Tying Record, (3) SSVEP-Based Brain-Computer Interfaces Competition: Champion\/First Prize, (4) Motor Imagery-Based Brain-Computer Interfaces Competition: Champion\/First Prize.
                  2019\u4e16\u754c\u6a5f\u5668\u4eba\u5927\u6703\u8166\u63a7\u6a5f\u5668\u4eba\u5927\u8cfd\u66a8\u7b2c\u4e09\u5c46\u4e2d\u570b\u8166\u6a5f\u63a5\u53e3\u6bd4\u8cfd\uff08\u570b\u5bb6\u81ea\u7136\u79d1\u5b78\u57fa\u91d1\u59d4\u54e1\u6703\u6307\u5c0e\u3001\u570b\u5bb6\u81ea\u7136\u79d1\u5b78\u57fa\u91d1\u59d4\u54e1\u6703\u4fe1\u606f\u79d1\u5b78\u90e8\u3001\u4e2d\u570b\u96fb\u5b50\u5b78\u6703\u53ca\u6e05\u83ef\u5927\u5b78\u4e3b\u8fa6\uff0c400\u4f59\u652f\u53c3\u8cfd\u968a\u53c3\u52a0\u5171\u4e09\u9805\u55ae\u9805\u6280\u8853\u8cfd\uff09\uff0c\u7372\uff1a
                  \u6280\u8853\u8cfd\u7e3d\u51a0\u8ecd\/\u7279\u7b49\u734e\u3001\u5275\u8166\u63a7\u6253\u5b57\u8a18\u9304\u3001\u6795\u8449\u8166\u6a5f\u7d44\/\u57fa\u65bc\u7a69\u614b\u8996\u89ba\u8a98\u767c\u96fb\u4f4d\u7684\u8166\u6a5f\u63a5\u53e3\u7cfb\u7d71\u6280\u8853\u8cfd\u51a0\u8ecd\/\u4e00\u7b49\u734e\u3001\u9873\u8449\u8166\u6a5f\u7d44\/\u57fa\u65bc\u904b\u52d5\u60f3\u50cf\u7684\u8166\u6a5f\u63a5\u53e3\u7cfb\u7d71\u6280\u8853\u8cfd\u51a0\u8ecd\/\u4e00\u7b49\u734e<\/li>\n<\/ul>\n
                  \n

                  Selected Publications<\/h3>\n

                  Selected Journal Papers<\/h4>\n
                    \n
                  1. \u201cPrototypical contrastive learning with temporal dynamic graph convolutional network for EEG-based emotion recognition,\u201d IEEE Transactions on Affective Computing<\/i>, accepted.<\/li>\n
                  2. \u201cDeepFingerNet: Enhancing finger trajectory prediction from ECoG signals using nested U-nets,\u201d IEEE Transactions on Instrumentation & Measurement<\/i>, Early Access, DOI: 10.1109\/TIM.2025.3644562, 2025.<\/li>\n
                  3. \u201cNeurofeedback system over frontal alpha asymmetry modulates fairness-related social decision-making,\u201d IEEE Transactions on Computational Social Systems<\/i>, Early Access, DOI: 10.1109\/TCSS.2025.3617658, 2025.<\/li>\n
                  4. “Dual-branch attention-based frequency domain network for cross-subject SSVEP-BCIs,\u201d IEEE Journal of Biomedical and Health Informatic<\/i>, Early Access, DOI: 10.1109\/JBHI.2025.3630249, 2025.<\/li>\n
                  5. \u201cDecoding decision-making and feedback interactions: Insights from EEG activation networks,\u201d IEEE Journal of Biomedical and Health Informatic<\/i>, Early Access, DOI: 10.1109\/JBHI.2025.3602838, 2025.<\/li>\n
                  6. \u201cEEG-based emotion monitoring and regulation system by learning the discriminative brain network manifold,\u201d IEEE Transactions on Neural Networks and Learning Systems<\/i>, 36(10), 17751-17765, 2025.<\/li>\n
                  7. \u201cExploiting the intrinsic neighborhood semantic structure for domain adaptation in EEG-based emotion recognition,\u201d IEEE Transactions on Affective Computing<\/i>, 16(3), 2466-2478, 2025.<\/li>\n
                  8. \u201cSpectral-spatial attention alignment for multi-source domain adaptation in EEG-based emotion recognition,\u201d IEEE Transactions on Affective Computing<\/i>, 15(4), 2012-2024, 2024.<\/li>\n
                  9. \u201cBrain network manifold learned by cognition-inspired graph embedding model for emotion recognition,\u201d IEEE Transactions on Systems, Man and Cybernetics: Systems<\/i>, 54(12), 7794-7808, 2024.<\/li>\n
                  10. \u201cA least-square unified framework for spatial filtering in SSVEP-based BCIs,\u201d IEEE Transactions on <\/i>Neural Systems and Rehabilitation Engineering<\/i>, 32, 2470-2481, 2024.<\/li>\n
                  11. \u201cEffect of excessive internet gaming on inhibitory control based on resting EEG and ERP,\u201d iScience<\/i>, 27(8), 2024.<\/li>\n
                  12. \u201cNeural adaptive optimal control of inequality constrained nonlinear system with partial uncertain time delay,\u201d IEEE Transactions on Systems, Man and Cybernetics: Systems<\/i>, 54(7), 4066-4076, 2024.<\/li>\n
                  13. “ADFCNN: Attention-based dual-scale fusion convolutional neural network for motor imagery brain-computer interface,” IEEE Transactions on Neural Systems and Rehabilitation Engineering<\/i>, 32(1), 154-165, 2024.<\/li>\n
                  14. \u201cActivation network improves spatiotemporal modelling of human brain communication processes,\u201d NeuroImage<\/i>, 285, 120472, 2024.<\/li>\n
                  15. “Relationship between decision-making and resting-state EEG in adolescents with different emotional stabilities,\u201d IEEE Transactions on Cognitive and Developmental Systems<\/i>, 16(1), 243-250, 2024.<\/li>\n
                  16. \u201cMulti-view contrastive learning for unsupervised domain adaptation in brain-computer interfaces,\u201d IEEE Transactions on Instrumentation and Measurement<\/i>, 73, 2509410, 2024.<\/li>\n
                  17. “Compact artificial neural network based on task attention for individual SSVEP recognition with less calibration,\u201d <\/span>IEEE Transactions on Neural Systems and Rehabilitation Engineering<\/i>, 31, 2525-2534, 2023.<\/span><\/li>\n
                  18. \u201cDriving fatigue effects on cross-frequency phase synchrony embedding in multi-layer brain network,\u201d IEEE Transactions on Instrumentation and Measurement<\/i>, 72, 1-14, 2023.<\/li>\n
                  19. \u201cEnhancing detection of multi-frequency-modulated SSVEP using phase difference constrained canonical correlation analysis,\u201d <\/span>IEEE <\/i>Transactions on Neural Systems and Rehabilitation Engineering<\/i>, 31, 1343-1352, 2023.<\/span><\/li>\n
                  20. \u201cE-Key: an EEG-based biometric authentication and driving fatigue detection system,\u201d IEEE Transactions on Affective Computing<\/i>, 14(2), 864-877, 2023.<\/li>\n
                  21. “Stimulus-stimulus transfer based on time-frequency-joint representation in SSVEP-based BCIs,” IEEE Transactions on Biomedical Engineering<\/i>, 70(2), 603-615, 2023.<\/li>\n
                  22. \u201cST-CapsNet: Linking sspatial and temporal attention with capsule network for P300 detection improvement,\u201d IEEE Transactions on Neural Systems and Rehabilitation Engineering<\/i>, 31, 991-1000, 2023.<\/li>\n
                  23. \u201cOn the benefits of two dimensional metric learning,\u201d IEEE Transactions on Knowledge and Data Engineering<\/i>, 35(2), 1909-1921, 2023.<\/li>\n
                  24. \u201cEEG-based emotion recognition via channel-wise attention and self attention,\u201d IEEE Transactions on Affective Computing<\/i>, 14(1), 382-393, 2023.<\/li>\n
                  25. \u201cLifelong online learning from accumulated knowledge,\u201d ACM Transactions on Knowledge Discovery from Data<\/i>, 17(4), Article No.: 52, 1-23, 2023.<\/li>\n
                  26. \u201cClassification of attention deficit\/hyperactivity disorder based on EEG signals using EEG-transformer model,\u201d Journal of Neural Engineering<\/i>, 20(5), 056013, 2023.<\/li>\n
                  27. \u201cResting-state network predicts the decision-making behaviors of the proposer during the ultimatum game,\u201d Journal of Neural Engineering<\/i>, 20(5), 056003, 2023.<\/li>\n
                  28. \u201cEmpirical validation of task-related component analysis reformulation for computational complexity reduction,\u201d Biomedical Signal Processing and Control<\/i>, 86, 105220, 2023.<\/li>\n
                  29. “SSVEP-based brain computer interface controlled soft robotic glove for post-stroke hand function rehabilitation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, <\/i>30: 1737-1744, 2022.<\/li>\n
                  30. “Adaptive Fourier decomposition for multi-channel signal analysis,” IEEE Transactions on Signal Processing<\/i>, 70(1): 903-918, 2022.<\/li>\n
                  31. \u201cOnline adaptation boosts SSVEP-based BCI performance,\u201d IEEE Transactions on Biomedical Engineering<\/i>, 69(6): 2018-2028, 2022. <\/li>\n
                  32. “Fusing frequency-domain features and brain connectivity features for cross-subject emotion recognition,”  IEEE Transactions on Instrumentation and Measurement, <\/i>71, 2508215, 2022. <\/li>\n
                  33. “The masking impact of intra-artifacts in EEG on deep learning-based sleep staging systems: A comparative study,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, <\/i>30: 1452-1463, 2022.<\/li>\n
                  34. “Learning curve and dynamic brain network based on phase locking value during short time neurofeedback training,\u201d IEEE Transactions on Cognitive and Developmental Systems<\/i>, 14(3): 1282-1295, 2022.<\/li>\n
                  35. “Epileptic seizure detection by cascading isolation forest-based anomaly screening and EasyEnsemble,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, <\/i>30: 915-924, 2022.<\/li>\n
                  36. ” Development of a clinical risk score prediction tool for 5-, 9-, and 13-year risk of dementia: a 13-year longitudinal population-based study,” JAMA Network Open<\/i>, 5(11): e2242596, 2022.<\/li>\n
                  37. \u201cTransferring subject-specific knowledge across stimulus frequencies in SSVEP-Based BCIs,\u201d <\/span>IEEE <\/em>Transactions <\/em>on Automation Science and Engineering<\/em>, 18(2): 552-563, 2021.<\/span><\/li>\n
                  38. \u201cCommon spatial pattern reformulated for regularizations in brain-computer interfaces,\u201d IEEE Transactions on Cybernetics<\/em>, 51(10): 5008-5020, 2021.<\/li>\n
                  39. \u201cDriving fatigue recognition with functional connectivity based on phase synchronization,\u201d IEEE <\/em>Transactions<\/em> on Cognitive and Developmental Systems<\/em>, 13(3): 668-678, 2021.<\/li>\n
                  40. \u201cBrain network excitatory\/inhibitory imbalance is a biomarker for drug-na\u00efve Rolandic epilepsy: A radiomics strategy,\u201d Epilepsia<\/i>, 62(10): 2426-2438, 2021.<\/li>\n
                  41. \u201cEffect of excessive use of internet games on inhibitory control and resting-state EEG,\u201d International Journal of Psychophysiology<\/i>, 168, S101, 2021.<\/li>\n
                  42. \u201cDecision-feedback stages revealed by hidden Markov modelling of EEG,\u201d International Journal of Neural Systems<\/i>, 31(7): 2150031, 2021.<\/li>\n
                  43. \u201cThe decision strategies of adolescents with different emotional stabilities in the unfair situations,\u201d Neuroscience Bulletin<\/i>, 37: 1481-1486, 2021<\/li>\n
                  44. \u201cEffect of brain alpha oscillation on the performance in laparoscopic skills simulator training,\u201d Surgical Endoscopy<\/em>, 35: 584-592, 2021.<\/li>\n
                  45. \u201cFractional delay filter based repetitive control for precision tracking: design and application to a piezoelectric nanopositioning stage,\u201d Mechanical Systems and Signal Processing<\/i>, 164, 108249, 2021.<\/li>\n
                  46. \u201cSpatial filtering in SSVEP-based BCIs: Unified framework and new improvements,\u201d <\/span>IEEE Transactions on Biomedical Engineering<\/em>, 67(11): 3057-3072, 2020.<\/span><\/li>\n
                  47. \u201cInter- and intra-subject transfer reduces calibration effort for high-speed SSVEP-based BCIs,\u201d IEEE Transactions on Neural Systems and Rehabilitation Engineering<\/em>, 28(10): 2123-2135, 2020.<\/li>\n
                  48. \u201cDynamic reorganization of functional connectivity unmasks fatigue related performance declines in simulated driving,\u201d IEEE Transactions on Neural Systems and Rehabilitation Engineering<\/em>, 28(8): 1790-1799, 2020.<\/li>\n
                  49. \u201cIndividual variation in alpha neurofeedback training efficacy predicts pain modulation,\u201d NeuroImage: Clinical<\/em>, 28,102454, 2020.<\/li>\n
                  50. \u201cConsensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist),\u201d Brain<\/em>, DOI: 10.1093\/brain\/awaa009, 2020.<\/li>\n
                  51. \u201cMulti-channel EEG-based emotion recognition via a multi-level features guided capsule network,\u201d Computer in Biology and Medicine<\/em>, 123,103927, 2020.<\/li>\n
                  52. \u201cChanges of EEG phase synchronization and EOG signals along the use of SSVEP-based BCI,\u201d Journal of Neural Engineering<\/em>, 17(4): 045006, 2020.<\/li>\n
                  53. \u201cAlpha down-regulation neurofeedback training facilitates implicit motor learning and consolidation,\u201d Journal of Neural Engineering<\/em>, 17(2): 026014, 2020.<\/li>\n
                  54. \u201cLearning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs,\u201d Journal of Neural Engineering<\/em>, 17(1): 016026, 2020.<\/li>\n
                  55. \u201cPredicting individual decision-making responses based on the functional connectivity of resting-state EEG,\u201d Journal of Neural Engineering<\/em>, 16(6): 066025, 2019.<\/li>\n
                  56. \u201cCompeting with multinational enterprise\u2019 entry: Search strategy, environmental complexity and survival of local firms,\u201d International Business Review<\/em>, 28(4): 727-738, 2019.<\/li>\n
                  57. \u201cSparse EEG source localization using LAPPS: Least absolute lp<\/sub><\/em> (0<p<1) penalized solution,\u201d IEEE Transactions on Biomedical Engineering<\/em>, 66(7): 1927-1939, 2018.<\/li>\n
                  58. \u201cEyes-closed resting EEG predicts the learning of alpha down-regulation in neurofeedback training,\u201d Frontiers in Psychology<\/em>, 9, 1067, 2018.<\/li>\n
                  59. \u201cReliable detection of implicit waveform-specific learning in continuous tracking task paradigm,\u201d Scientific Reports<\/em>, 7(1): 12333, Sep. 26, 2017.<\/li>\n
                  60. \u201cAdaptive Fourier decomposition based ECG denoising,\u201d <\/span>Computers in Biology and Medicine<\/em>, 77, 195-205, 2016.<\/span><\/li>\n
                  61. \u201cAlpha neurofeedback training improves SSVEP-based BCI performance,\u201d Journal of Neural Engineering<\/em>, 13(3): 036019, 2016.<\/li>\n
                  62. \u201cAdaptive time-window length based on online performance measurement in SSVEP-based BCIs,\u201d <\/span>Neurocomputing<\/em>, 149(A), 93-99, 2015.<\/span><\/li>\n
                  63. \u201cObjective evaluation of fatigue by EEG spectral analysis in steady-state visual evoked potential-based brain-computer interfaces,\u201d Biomedical Engineering Online<\/em>, 13(28), 2014.<\/li>\n
                  64. \u201cResting alpha activity predicts learning ability in alpha neurofeedback,\u201d Frontiers in Human Neuroscience<\/em>, 8:500, 2014.<\/li>\n
                  65. \u201c15-nW biopotential LPFs in 0.35-\u00b5m CMOS using subthreshold-source-follower biquads with and without gain compensation,\u201d IEEE Transactions<\/em> on Biomedical Circuits and Systems<\/em>, 7(5), 690-702, 2013.<\/li>\n
                  66. \u201cOne-unit second order blind identification (SOBI) with reference for short transient signals,\u201d Information Sciences<\/em>, 227, 90-101, 2013.<\/li>\n
                  67. \u201cIndividual alpha neural feedback training effect on short-term memory,\u201d International Journal of Psychophysiology<\/em>, 86(1): 83-87, 2012.<\/li>\n
                  68. \u201cA 0.83-\u00b5W QRS detection processor using quadratic spline wavelet transform for wireless ECG acquisition in 0.35-\u00b5m CMOS,\u201d IEEE Transactions on Biomedical Circuits and Systems<\/em>, 6(6): 586-595, 2012.<\/li>\n
                  69. \u201cA fast adaptive model reduction method based on Takenaka-Malmquist systems,\u201d Systems and Control Letters<\/em>, 61(1): 223-230, 2012.<\/li>\n
                  70. \u201cFurther study on the parameter convergence of fuzzy models in nonlinear system identifications,\u201d Acta Automatica Sinica<\/em>, 33(1): 109-112, 2007.<\/li>\n
                  71. \u201cHow to determine the minimum number of fuzzy rules to achieve given accuracy: A computational geometric approach to SISO case,\u201d Fuzzy Sets and Systems<\/em>, 150(2): 199-209, 2005.<\/li>\n
                  72. \u201cNonlinear discrete-time system identifications based on fuzzy models: Algorithms and performance analyses,\u201d Acta Automatica Sinica<\/em>, 30(6): 844-853, 2004.<\/li>\n
                  73. \u201cStructured neural networks for constrained model predictive control,\u201d Automatica<\/em>, 37(8): 1235-1243, 2001.<\/li>\n<\/ol>\n

                    Recent Conference Papers<\/h4>\n
                      \n
                    1. \u201cEEG-based emotion recognition under convolutional neural network with differential entropy feature maps,\u201d 2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications <\/em>(CIVEMSA), Tianjin, June 2019.<\/li>\n
                    2. \u201cInfluence of stimuli color combination on online SSVEP-based BCI performance,\u201d 2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications <\/em>(CIVEMSA), Tianjin, June 2019.<\/li>\n
                    3. \u201cA spiking neural network model mimicking the olfactory cortex for handwritten digit recognition,\u201d The 9th International IEEE\/EMBS Conference on Neural Engineering<\/em> (NER), pp: 1167-1170, San Francisco, CA, USA, March 2019.<\/li>\n
                    4. \u201cLearning prototype spatial filters for subject-independent SSVEP-based brain computer interface\u201d, 2018 IEEE International Conference on Systems, Man, and Cybernetics <\/em>(SMC), Miyazaki, Japan, Oct. 7-10, 2018.<\/li>\n
                    5. \u201cArea-to-area transfer improves single-channel SSVEP classification\u201d, The 7th International Brain-Computer Interface <\/em>(BCI) Meeting<\/em>, Pacific Grove, CA, May 21-25, 2018.<\/li>\n
                    6. \u201cBetween-class CCA for SSVEP based BCI\u201d, The 7th International Brain-Computer Interface <\/em>(BCI) Meeting<\/em>, Pacific Grove, CA, May 21-25, 2018.<\/li>\n
                    7. \u201cChange of brain functional connectivity associate with fatigue in SSVEP-BCI applications\u201d, The 7th International Brain-Computer Interface <\/em>(BCI) Meeting<\/em>, Pacific Grove, CA, May 21-25, 2018.<\/li>\n
                    8. \u201cNeurofeedback improves SSVEP-BCI performance on subject with both ‘high’ and ‘low’ performance\u201d, The 7th International Brain-Computer Interface <\/em>(BCI) Meeting<\/em>, Pacific Grove, CA, May 21-25, 2018.<\/li>\n
                    9. \u201cAdaptive Fourier decomposition based R-peak detection for noisy ECG signals\u201d, The<\/em> 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society<\/em>, Jeju, Korea, July 11-15, 2017.<\/li>\n
                    10. \u201cOnline optimization of visual stimuli for reducing fatigue in SSVEP-based BCIs,\u201d The 6th International Brain-Computer Interface <\/em>(BCI) Meeting<\/em>, Pacific Grove, CA, USA, May 30 \u2013 June 3, 2016.<\/li>\n
                    11. \u201cFatigue evaluation through EEG analysis using multi-scale Entropy in SSVEP-based BCIs,\u201d The 6th International Brain-Computer Interface <\/em>(BCI) Meeting<\/em>, Pacific Grove, CA, USA, May 30 \u2013 June 3, 2016.<\/li>\n
                    12. \u201cFrequency recognition based on wavelet-independent component analysis for SSVEP-based BCIs,\u201d The 12th International Symposium on Neural Networks<\/em>, ISNN 2015, Jeju Island, Korea, October 15-18, 2015. Also in X. Hu et al. (eds.): Advances in Neural Networks – ISNN 2015<\/em>: Lecture Notes in Computer Science 9377, pp. 315-323, Springer International Publishing Switzerland 2015.<\/li>\n
                    13. \u201cFast basis searching method of adaptive Fourier decomposition based on Nelder-Mead algorithm for ECG signals,\u201d The 12th International Symposium on Neural Networks<\/em>, ISNN 2015, Jeju Island, Korea, October 15-18, 2015. Also in X. Hu et al. (eds.): Advances in Neural Networks – ISNN 2015<\/em>: Lecture Notes in Computer Science 9377, pp. 305-314, Springer International Publishing Switzerland 2015.<\/li>\n
                    14. \u201cBeta\/theta ratio neurofeedback training effects on the spectral topography of EEG,\u201d in the Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society<\/em>, Milan, Aug. 23-29, 2015.<\/li>\n
                    15. \u201cA multi-channel SSVEP-based BCI for computer games with analogue control,\u201d 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications<\/em>, Shenzhen, China, June 12-14, 2015.<\/li>\n
                    16. \u201cReliability and sensitivity analysis on the center of pressure measures in healthy young adults using Nintendo Wii balance board,\u201d 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications<\/em>, Shenzhen, China, June 12-14, 2015.<\/li>\n
                    17. \u201cAdaptive Fourier decomposition approach for lung-heart sound separation,\u201d 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications<\/em>, Shenzhen, China, June 12-14, 2015.<\/li>\n
                    18. \u201cBeta\/Theta neurofeedback training effects in physical balance of healthy people,\u201d IUPESM World Congress on Medical Physics and Biomedical Engineering<\/em> (IUPESM WC 2015), Toronto, Canada, June 7-12, 2015.<\/li>\n
                    19. \u201cHow mental strategy affects Beta\/Theta neurofeedback training,\u201d IUPESM World Congress on Medical Physics and Biomedical Engineering<\/em> (IUPESM WC 2015), Toronto, Canada, June 7-12, 2015.<\/li>\n
                    20. \u201cTime varying VEP evaluation as a prediction of vision fatigue using stimulated brain-computer interface,\u201d in H. Lijenstrom (ed.) Advances in Cognitive Neurodynamics<\/em> (IV), pp. 157-160, Springer Science + Business Media Dordrecht 2015.<\/li>\n<\/ol>\n
                      \n

                      Professional Affiliations & Services<\/h3>\n