Rattanaphon Chaisaen

Rattanaphon Chaisaen

Visiting Student Researcher

Department of Psychiatry, Stanford University

Research Areas:
Brain-Computer Interfaces • Deep Learning • Motor Imagery • Signal Processing • Assistive Technologies
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About Me

Current Position

I am a Visiting Student Researcher at Department of Psychiatry, Stanford University, currently pursuing my Ph.D. in Information Science and Technology at Vidyasirimedhi Institute of Science and Technology (VISTEC). My research focuses on brain-computer interfaces, deep learning, and signal processing with applications in assistive technologies.

Research Interests

My work explores the intersection of neuroscience and artificial intelligence, with a particular emphasis on developing practical brain-computer interface systems. I am passionate about creating technologies that can improve the quality of life for people with motor impairments.

Education

Ph.D. - In Progress

Information Science and Technology

Vidyasirimedhi Institute of Science and Technology (VISTEC), Thailand

Supervisor: Assoc. Prof. Theerawit Wilaiprasitporn

2018 – Present
Bachelor's Degree

Computer Science

Khon Kaen University, Thailand

First-class honors

2014 – 2018
High School

Science Curriculum

Princess Chulabhorn's Science High School Loei, Thailand

Fully-funded scholarship recipient

2011 – 2014

Research Focus

Brain-Computer Interfaces

Developing robust BCI systems that can accurately decode brain signals for real-world applications. Expertise with various BCI hardware including g.HIAMP, OpenBCI, and MUSE EEG headsets.

Deep Learning for Biomedical Signals

Applying state-of-the-art deep learning techniques to EEG and EMG signal classification. Published multiple papers on CNN and neural network architectures for seizure detection and motor imagery.

Multi-Task Learning

Research on subject-independent motor imagery classification using multi-task learning frameworks. Developing generalizable models that work across different users and conditions.

Assistive Technologies

Focus on practical applications of BCI technology for individuals with motor impairments. Designing user-friendly systems for rehabilitation and assistive device control.

Selected Publications

AlphaGrad: Normalized Gradient Descent for Adaptive Multi-Loss Functions in EEG-Based Motor Imagery Classification

R. Chaisaen, P. Autthasan, A. Ditthaporn, and T. Wilaiprasitporn
IEEE Journal of Biomedical and Health Informatics, vol. 29, no. 10, pp. 7116 - 7128, October 2025

MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification

P. Autthasan, R. Chaisaen*, H. Phan, M. D. Vos and T. Wilaiprasitporn
IEEE Internet of Things Journal, vol. 11, no. 17, pp. 28539-28554, September 2024

MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification

P. Autthasan, R. Chaisaen*, T. Sudhawiyangkul, P. Rangpong, S. Kiatthaveephong, N. Dilokthanakul, G. Bhakdisongkhram, H. Phan, C. Guan, and T. Wilaiprasitporn
IEEE Transactions on Biomedical Engineering, vol. 69, no. 6, pp. 2105-2118, June 2022

Decoding EEG Rhythms During Action Observation, Motor Imagery, and Execution for Standing and Sitting

R. Chaisaen, P. Autthasan, N. Mingchinda, P. Leelaarporn, N. Kunaseth, S. Tammajarung, P. Manoonpong, S. C. Mukhopadhyay, and T. Wilaiprasitporn
IEEE Sensors Journal, vol. 20, no. 22, pp. 13776-13786, November 2020
View All Publications

Skills & Achievements

Programming Languages

Python, MATLAB, JavaScript, C++, Java

Deep Learning Frameworks

TensorFlow, PyTorch, Scikit-learn, MNE, EEGLAB

Tools & Technologies

Docker, Conda, Git, MATLAB, Linux, Jupyter

BCI Hardware Expertise

g.HIAMP, g.USBAMP, OpenBCI, MUSE EEG, Delsys Trigno EMG

Scholarships

Fully-funded Ph.D. scholarship from PTT Public Company Limited and The Siam Commercial Bank.


Undergraduate scholarship from Department of Computer Science, Khon Kaen University.