About

Welcome! I am Bhoj Raj Thapa, a Ph.D. candidate in Electrical Engineering at the University of Kentucky, passionate about pioneering neural engineering to enhance human-machine interactions. My research specializes in biomedical signal processing and EEG-based brain-machine interfaces, leveraging machine learning and signal processing to improve quality of life. With more than 5 publications and contributions to over 15+ interdisciplinary projects, I collaborate with diverse teams to transform innovative research into practical, impactful solutions.

Neural Engineering Researcher.

My research focuses mainly on biomedical signal processing, machine learning (ML), deep learning (DL), and brain-machine interfaces.

Background

My educational and professional experience.

Education

Doctor of Philosopy (PH.D.) in Electrical Engineering

Aug 2021 - Present

University of Kentucky, Lexington, Kentucky, USA

Dissertation Title (working on): EEG-based Brain Machine Interfaces using Freewill for Reaching and Grasping Tasks

Bachelor of Electronics & Communication Engineering (BE)

Sep 2014 - Sep 2018

Nepal Engineering College (Affiliated to Pokhara University), Bhaktapur, Nepal

Thesis Title: Classification of EEG signal before epileptic seizure to detect its onset for a patient-specific case.

Higher Secondary School (+ 2 Science)

2012 - 2014

Liverpool International SS/College, Kathmandu, Nepal

Concentration: Computer Science & Mathematics

From my days in higher secondary school in Nepal, my deep interest in Engineering and Technology was sparked by an enriching education in Computer Science and Mathematics, inspiring me to explore how innovative technological solutions can address real-world challenges.

School Leaving Certificate (SLC)

2012

Shree Mahendra Jyoti Secondary School, Bheerkot, Syangja, Nepal

Research/Work Experience

Graduate Teaching Assistant

Aug 2021 - Present

Department of Eletrical and Computer Engineering, University of Kentucky, Kentucky, USA

  • Utilized MATLAB and LabView to enhance instruction materials, evaluated over 200 assignments, and provided mentorships to 100+ students, improving their understanding of Signals and Systems through two courses: Lecture (EE421G) and Laboratory (EE422G).

Graduate Research Assistant

Aug 2021 - Aug 2022, Summer 2024

Neural Interfaces & Signal Processing (NISP) Lab, University of Kentucky, USA

  • Recorded/acquired multi-modal EEG/EOG data of 22 subjects over 2.5 years for developing goal-driven Brain-Machine Interface (BMI) algorithms for freewill reaching and grasping task.
  • Analyzed and classified EEG data for pre-movement intention motor imagery using Fourier Transform and Spectrogram for frequency domain analysis and KTD-based reinforcement learning algorithms, thereby advancing understanding of neural patterns in movement prediction.
  • Executed precise EEG data recording for EEG-fMRI-based inter-ictal clinical epilepsy research study, contributing to critical insights in inter-ictal clinical research and potential treatment avenues.

Research Assistant

Aug 2019 - Dec 2019

Kathmandu Institute of Applied Sciences, Kathmandu, Nepal

  • Engineered an innovative, cost-effective mobile weather station leveraging Arduino, which captures comprehensive meteorological data, enhancing real-time environmental monitoring capabilities.
  • Led and mentored an intern, guiding the successful development and implementation of the mobile weather station project, ensuring quality and timely project delivery.

Skills

Multi‑disciplinary experience across neural engineering, biomedical signal processing, and Machine Learning (ML) / Deep Learning (DL).

Biomedical Signal Processing
7+ years

Electroencephalogram (EEG)/Electrocardiogram (ECG)/Electrooculogram (EOG) analysis, filtering, feature extraction, time-frequency analysis, artifact removal.

MATLAB
7+ years

Signal processing, toolboxes, scripts, visualization, EEGLAB, BrainStorm.

PyTorch
3+ years

Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), attention mechanisms.

Machine Learning
7+ years

Support Vector Machine, Linear Discriminant Analysis, Principal Component Analysis, Independent Component Analysis, Kalman filters, reinforcement learning.

Python
6+ years

NumPy, SciPy, scikit-learn, MNE-Python, data engineering, visualization, PyQt app development.

TensorFlow/Keras
2+ years

Deep learning models, training, deployment.

Embedded Systems & IoT
5+ years

Arduino, Raspberry Pi, OpenBCI, ROS, sensor integration.

Other Tools
5+ years

Shell Scripting (Bash), Git/GitHub, LaTeX, C/C++, Java, LabView, High Performance Computing (HPC), CAD (Shapr3D).

Awards

Recognition for academic excellence, research contributions, and teaching achievements.

2025 IEEE Engineering in Medicine and Biology Society (EMBS) Travel Award Fellowship

Nov 2025

2026 Lighthouse Beacon Foundation Graduate Fellow Awardee

For advancing brain-machine interface research using signal processing and machine learning at the University of Kentucky. (Jul 2025-Jul 2026)

Elise White Boyd Graduate Teaching Fellow

For performing excellently as a Teaching Assistant at the University of Kentucky. (Jan 2025-May 2025)

Faculty of Science & Technology Scholarship

100% tuition waiver to pursue undergraduate studies. (2014-2018)

Pre-Engineering Fellowship Awardee

100% fee waiver for preparation of an engineering degree. (2012)

Higher Secondary Education Board (HSEB) Scholarship

100% tuition waiver to pursue high school studies. (2012-2014)

Publications

Below are three key publications from my research. For a complete list, visit my Google Scholar profile.

A large electroencephalogram database of freewill reaching and grasping tasks for brain machine interfaces

Thapa, B. R., Boggess, J., and Bae, J. (2025). Scientific Data.

Kernel Temporal Differences for EEG-based Reinforcement Learning Brain Machine Interfaces

Thapa, B. R., Tangarife, D. R., and Bae, J. (2022). 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

Implementing Neural Network and Multi resolution Analysis in EEG signal for early detection of epilepsy

Shrestha, S., Shrestha, R. D., and Thapa, B. R. (2019). SCITECH NEPAL.

Portfolio

Discover a diverse portfolio of projects showcasing my expertise across biomedical signal processing, machine learning, and neural engineering, each designed to push the boundaries of technology and improve quality of life.

  • All
  • Biomedical Signal Processing
  • Machine Learning & Deep Learning
  • Others
Early Epilepsy Prediction
Automatic Weather Station
Customer Churn Prediction
EEG Motor Imagery
QRS in ECG Detection
Window Analysis in Freewill EEG
Kernel Temporal Difference in EEG-based BMI
Transfer Learning (TL) in Kernel Temporal Difference in EEG-based BMI
NFL Helmet Collision Detection using YOLOv5

Blog

What's on my mind...

Contact

I'm always interested in hearing about new opportunities, collaborating on projects, or just connecting to exchange ideas. Whether you're looking for someone to join your team, have a question about my work, or just want to say hello, feel free to reach out to me.

Location:

Lexington, Kentucky, USA

Phone Number:

Upon Request.