PRADO, Seigfred V.

PRADO, Seigfred V.

Seigfred V. Prado received his Bachelor of Science in Electronics Engineering (B.Sc. ECE) degree from the University of Santo Tomas (UST) in Manila, Philippines in 2011, Master of Science in Electronic Engineering (M.Sc. ELEG, with High Distinction) degree from The Hong Kong University of Science and Technology (HKUST) in Kowloon, Hong Kong in 2015, and Master of Research in Neurotechnology (MRes NT, with High Merits) degree from Imperial College London in England, United Kingdom in 2019. He is a licensed Electronics Engineer and an active Senior Member of the Institute of Electrical and Electronics Engineers (IEEE), the Institution of Engineering and Technology (IET), the Society for Neuroscience (SfN), the Federation of European Neuroscience Societies (FENS), the British Neuroscience Association (BNA), the Organization for Computational Neurosciences (OCNS), the Association of Biomedical Engineers, the International Society for Optics and Photonics, Asia-Pacific Chemical, Biological and Environmental Engineering Society (APCBEES), and the Institute of Electronics Engineers of the Philippines (IECEP). He is also an Associate Member of the National Research Council of the Philippines (NRCP). He has been invited to present as a plenary speaker in several highly-specialized international conferences in signal processing, biomedical engineering, neuroscience, and neurotechnology, and has published notable journal papers in very high-impact ISI/SCOPUS-indexed journals. He has received several awards both in teaching and research.

He is a candidate for the degree of Doctor of Philosophy (Ph.D.) in Bioengineering, with a specialization in Neurotechnology, at Imperial College London. He finished his doctoral research internship at Harvard Medical School, Harvard University in collaboration with Massachusetts General Hospital. His research interests lie at the interface of signal and image processing, machine learning and artificial intelligence, biomedical photonics, neuroscience, and neurotechnology. More precisely, he desires to investigate the topics of neural information processing and rehabilitation of neurodegenerative diseases such as Alzheimer’s disease.

He currently serves as the Executive Assistant for the University of Santo Tomas – Santa Rosa City campus, the Interim Chair for the UST Engineering Research Ethics Committee, the MSc ECE Program Coordinator of the UST Graduate School, and one of the organization advisers of the UST-IEEE Student Branch.

Updates

Engr. Seigfred V. Prado, MSc, MRes, SMIEEE

Department of Electronics Engineering

svprado@ust.edu.ph

Academic Qualifications

Research Highlights

Research Interests

Academic Qualifications

Degrees

  • Doctor of Philosophy in Bioengineering, major in Neurotechnology, Imperial College London – England, United Kingdom
    • Doctoral Research Internship (Harvard Medical School – Harvard University and Massachusetts General Hospital, USA)
  • Master of Research in Neurotechnology (M.Res. NT with High Merits), Imperial College London – England, United Kingdom 
  • Master of Science in Electronic Engineering (M.Sc. ELEG with High Distinction), The Hong Kong University of Science and Technology – Kowloon, Hong Kong
  • Bachelor of Science in Electronics and Communications Engineering, University of Santo Tomas – Manila, Philippines  

Research Highlights

  • Neuroscience and Neurotechnology
    • Development of an In-Vivo Two-Photon Calcium Imaging Platform for the Characterization of Brain Circuit Dynamics in Neurodegenerative Diseases
    • Development of a Neuronal Source Extraction and Exploration (NeuroSEE) Tool for the Automated Analysis of Two-Photon Calcium Imaging Data
    • Hippocampal Imaging in Awake Alzheimer’s Disease Model Mice
    • Neural Manifold Analysis of Brain Circuit Dynamics in Neurodegenerative Diseases
    • Information and Graph-Theoretic Analyses of Brain Circuit Dynamics
  • Biomedical Signal Processing
    • Characterization of EEG Signal Patterns During Visual Imageries for the Development of Brain-Computer Typing Interface for Locked-In Syndrome Patients
    • Development of an EEG-based Motor Imagery Brain-Computer Interface for Lower Limb Assistive Technologies
    • Characterization of Brain Signal During Deception
    • Characterization of EEG Signals for the Classification of the States of Stress
    • Nonlinear Signal Analysis of EEG Signals for Imagined Speech Recognition
    • Fall Detection Systems using sEMG
  • Speech Signal Processing
    • Rhinolalia Aperta Rudimentary Speech Recognition 

Research Interests

  • Neuroscience and Neurotechnology
    • Neural Coding
    • Neurological Disorders
      • Neurodegenerative Diseases (Alzheimer’s Disease, etc.)
      • Neurodevelopmental Disorders (Autism, ADHD, etc.)
      • Neuropsychiatric Disorders
    • Neurorehabilitation
  • Biomedical Signal Processing
    • Analysis of electrophysiological signals, e.g., EEG, ECG, EoG, EMG, etc.
    • Medical Image Analysis
  • Artificial Intelligence and Machine Learning
    • Artificial Neural Networks and Reinforcement Learning 

Courses Handled

Major Recognitions

Professional Activities

Courses Handled

  • Digital Signal Processing
  • Biomedical Imaging and Instrumentation
  • Digital Communications
  • Probability Theory and Stochastic Processes
  • Embedded Systems and Automation
  • Electronic Devices and Circuit Theory
  • Logic Circuits and Switching Theory
  • Computer Systems Architecture

Major Recognitions

  • ‘Special Citation Award, Magsaysay Future Engineers and Technologists Awards 2021, National Academy of Science and Technology (NAST)
  • IEEE Senior Member Award, Institute of Electrical and Electronics Engineers (IEEE)
  • Best Project of the Year Award, Bank of the Philippine Islands – Department of Science and Technology (BPI-DOST) Science Awards 2019
  • Best in Innovation Award, Bank of the Philippine Islands – Department of Science and Technology (BPI-DOST) Science Awards 2019
  • Best Presenter Award, 5-minute PhD Research Pitch Competition 2019, Imperial College London
  • High Merits Award, Master of Research in Neurotechnology, Imperial College London, 2019
  • Albertus Magnus Award for Outstanding Research, University of Santo Tomas, 2018
  • Best Paper Award, 9th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, 2017
  • Benavides Outstanding Achievement Award, University of Santo Tomas, 2017
  • Albertus Magnus Award for Outstanding Research, University of Santo Tomas, 2017
  • Best Research Award, National Computer Research and Engineering Symposium, 2017
  • High Academic Distinction Award, Master of Science in Electronic Engineering, The Hong Kong University of Science and Technology, 2015
  • Top 1 Instructor Award, University of Santo Tomas, Office for Faculty Evaluation and Development, 2014 

Professional Activities

  • Executive Assistant, Assistant to the Rector for UST Santa Rosa (2022-present)
  • Senior Member, Institute of Electrical and Electronics Engineers (IEEE), IEEE Brain Society, IEEE Signal Processing Society, IEEE Engineering in Medicine and Biology Society, IEEE Life Sciences Community, IEEE Young Professionals
  • Associate Member, National Research Council of the Philippines (NRCP)
  • Professional Member, Institution of Engineering and Technology (IET)
  • Professional Member, Society for Neuroscience (SfN)
  • Professional Member, Federation of European Neuroscience Societies (FENS)
  • Professional Member, British Neuroscience Association (BNA)
  • Professional Member, Organization for Computational Neurosciences (OCNS)
  • Professional Member, Association of Biomedical Engineers
  • Professional Member, International Society for Optics and Photonics
  • Professional Member, Asia-Pacific Chemical, Biological and Environmental Engineering Society (APCBEES)
  • Professional Member, Institute of Electronics Engineers of the Philippines (IECEP) 

Selected Publications

Selected Publications

  • Cruz, J.A., Marquez, J.C., Mendoza, A.M., Reyes, J.I. and Prado, S.V., 2023, June. EEG-based Characterization and Classification of Severity for the Diagnosis of Post-Traumatic Stress Disorder (PTSD). In 2023 5th International Conference on Bio-engineering for Smart Technologies (BioSMART) (pp. 1-7). IEEE., https://doi.org/10.1109/BioSMART58455.2023.10162084
  • Calub, G.I.A., Elefante, E.N., Galisanao, J.C.A., Iguid, S.L.B.G., Salise, J.C. and Prado, S.V., 2023, June. EEG-Based Classification of Stages of Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI). In 2023 5th International Conference on Bio-engineering for Smart Technologies (BioSMART) (pp. 1-6). IEEE., https://doi.org/10.1109/BioSMART58455.2023.10162117
  • Prado, S., Mitchell-Heggs, R., Gava, G.P., Go, M.A. and Schultz, S.R., 2023. Neural manifold analysis of brain circuit dynamics in health and disease. Journal of Computational Neuroscience, 51(1), pp.1-21., https://doi.org/10.1007/s10827-022-00839-3
  • Go, M.A., Prado, S., Rogers, J., Gava, G.P., Davey, C.E., Liu, Y. and Schultz, S.R., 2021. Place cells in head-fixed mice navigating a floating real-world environment. Frontiers in cellular neuroscience, 15, p.19.,  https://doi.org/10.3389/fncel.2021.618658
  • Nieles, J.P.M., Magdaluyo, V.D.P., Mallari, L.B.A., Paliza, R.A.C., Salcedo, J.C.P. and Prado, S.V., 2018. Characterization of EEG Signal Patterns During Visual Imageries of Basic Structures for the Development of Brain-Computer Typing Interface for Locked-In Syndrome Patients. In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (pp. 1-6). IEEE., DOI: 10.1109/HNICEM.2018.8666333
  • Castro, A.J.F., Cruzit, J.N.P., De Guzman, J.J.C., Pajarillo, J.J.T., Rilloraza, A.M.M., Nieles, J.P.M. and Prado, S.V., 2020, February. Development of a Deep Learning-Based Brain-Computer Interface for Visual Imagery Recognition. In 2020 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA) (pp. 166-170). IEEE., DOI: 10.1109/CSPA48992.2020.9068713
  • Ocampo, J.P.F.E., Dizon, J.A.T., Reyes, C.V.I., Capitulo, J.J.C., Tapang, J.K.G. and Prado, S.V., 2017, September. Evaluation of muscle fatigue degree using surface electromyography and accelerometer signals in fall detection systems. In 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (pp. 21-26). IEEE., DOI: 10.1109/ICSIPA.2017.8120573
  • Berbano, A.E.U., Pengson, H.N.V., Razon, C.G.V., Tungcul, K.C.G. and Prado, S.V., 2017, September. Classification of stress into emotional, mental, physical and no stress using electroencephalogram signal analysis. In 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (pp. 11-14). IEEE., DOI: 10.1109/ICSIPA.2017.8120571
  • Aguila, M.J., Basilio, H.D.V., Suarez, P.V.C., Dueñas, J.P.E. and Prado, S.V., 2017, January. Comparative study of linear and nonlinear features used in imagined vowels classification using a backpropagation neural network classifier. In Proceedings of the 7th International Conference on Bioscience, Biochemistry and Bioinformatics (pp. 7-11)., https://doi.org/10.1145/3051166.3051175
  • Von Gwayneth, B.A., Dungca, C.J.N., Lazam, S.A., Pereira, M.N.L., Tan, J.R.O. and Prado, S.V., Development of an EEG-based Motor Imagery Brain-Computer Interface System for Lower Limb Assistive Technologies. In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (pp. 1-6). IEEE., DOI: 10.1109/HNICEM.2018.8666350
  • Marcelo, C.A.G., Pasquin, Z.R.B., Pichay, A.D.T., Tan, M.L.D., Simon, M.F.K.N., Prado, S.V., San Buenaventura, C.V. and Nicasio, M.S., 2017, December. Characterization and comparison of brain wave signals during deception. In 2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (pp. 1-6). IEEE., DOI: 10.1109/HNICEM.2017.8269508