Dr. Deepa Beeta thiyam | Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology | India
🎓 Thiyam Deepa Beeta, Ph.D., is a researcher in Biomedical Engineering at Vel Tech University 🏫, with expertise in EEG signal processing 🧠 and Brain-Computer Interface (BCI) systems. She completed her co-directed PhD from VIT University, India 🇮🇳, and University of Seville, Spain 🇪🇸, focusing on motor imagery movement classification. Thiyam’s work aims to design robust algorithms for paralyzed patients using BCI technology 🌐. With extensive research experience and several publications 📚, she also contributes to teaching and mentoring future engineers and scientists 👩🏫. Her work is funded by various prestigious grants 💡.
Professional Profile:
ORCID
SCOPUS
Suitability for Women Researcher Award
Thiyam Deepa Beeta is highly suitable for the Women Researcher Award due to her outstanding contributions in the field of Biomedical Engineering, specifically in EEG signal processing and Brain-Computer Interface (BCI) systems. Her expertise in developing algorithms for motor imagery movement classification holds immense potential in improving the quality of life for paralyzed patients. This innovative work directly aligns with advancing healthcare through cutting-edge technologies, which makes her an exemplary candidate.
Education and Experience 🎓💼
- PhD in Biomedical Engineering (VIT University, India) & Automática, Electrónica y Telecomunicaciones (University of Seville, Spain) (2012–2018)
Research: EEG Signal Processing for Motor Imagery BCI Systems - M.Tech in Biomedical Engineering (VIT University, India) (2008–2010)
- B.Tech in Biomedical Instrumentation Engineering (Dr. MGR Educational & Research Institute, India) (2004–2008)
- Associate Professor at Vel Tech Rangarajan Dr. Sagunthala R & D Institute (2023–Present)
- Assistant Professor at Vel Tech Rangarajan Dr. Sagunthala R & D Institute (2019–2023)
- Teaching & Research Associate at VIT University, Vellore (2012–2017)
Professional Development
Thiyam Deepa Beeta has demonstrated her leadership in Biomedical Engineering 🏥 by mentoring students 👩🏫 and contributing to academic journals 📖. As an Associate Professor at Vel Tech University, she teaches subjects like Biomedical Instrumentation and Microcontrollers 💻. Her expertise in EEG signal processing 🧠 and Brain-Computer Interfaces has shaped her research and helped her secure funding for projects 💡. Thiyam has also been a reviewer for international journals and conferences 🌍, such as IEEE Access and Biosignal Processing and Control, making her a prominent contributor to the field 📚.
Research Focus
Thiyam Deepa Beeta’s research focuses on EEG signal processing 🧠, specifically in Brain-Computer Interface (BCI) systems for motor imagery movements 💡. Her goal is to develop robust algorithms for paralyzed individuals 🦽, using BCI to help them regain control of their motor functions. She works on signal classification techniques 📊 for motor tasks and explores hybrid BCI systems for improved performance. Her research integrates AI 🤖 and machine learning models, especially CNN-based systems for medical applications 💉, pushing the boundaries of biomedical engineering towards life-changing innovations for patients.
Awards and Honors 🏆
- 🏅 CSIR Travel Grant for attending IEEE TENCON 2016 (Singapore)
- 🎓 Heritage Erasmus Mundus Scholarship for research at University of Seville, Spain
- 💡 Vel Tech University Internal Seed Fund for research on Motor Imagery EEG Signal Classification
- 🏅 Project Funding from Ministry of Economy and Competitiveness, Spain
Publication Top Notes:
“A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system,” Scientific Reports 🌐🧠
“Motor Imagery EEG Signal Classification Using Optimized Convolutional Neural Network,” Przeglad Elektrotechniczny ⚡🧠
“Performance Analysis of HybridA-BCI Signals Using CNN for Motor Movement Classification,” Traitement du Signal 📊💻 |
“Simulational Study for Designing Lung on-Chip,” ICBSII Conference
“Biocompatibility of oxide nanoparticles,” Oxides for Medical Applications 📚🧪
“Signal Processing for Hybrid BCI Signals,” Journal of Physics: Conference Series )📡🔧
“A customized knee brace for osteoarthritis patient using 3D printing,” ICICV Conference