Research
My research focuses on machine learning algorithms for brain-computer interface systems, particularly EEG-based P300 Speller technology, and other areas of AI/ML.
Publications
Subject-independent P300 speller classification using double input CNN with feature concatenation
DSP 2023 • 2023
This paper presents a novel approach to P300 speller classification using a double input CNN architecture that concatenates features for improved accuracy in subject-independent scenarios.
Event-related spectrogram representation of EEG for CNN-based P300 speller
APSIPA ASC 2021 • 2021
This research explores the use of event-related spectrograms as a representation method for EEG signals in CNN-based P300 speller systems.
Ensemble learning approach for subject-independent P300 speller
EMBC 2021 • 2021
This paper proposes an ensemble learning approach to improve the accuracy and robustness of subject-independent P300 speller systems.
Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller
Applied Sciences 2021 • 2021
This study presents an ensemble voting-based approach for multichannel EEG classification in subject-independent P300 speller systems.
Comparison of Generic and Subject-Specific Training for Features Classification in P300 Speller
APSIPA ASC 2020 • 2020
This research compares the effectiveness of generic and subject-specific training approaches for feature classification in P300 speller systems.
Research Interests
Brain-Computer Interfaces
Developing machine learning algorithms for EEG-based brain-computer interface systems, with a focus on P300 speller technology.
Deep Learning
Exploring novel deep learning architectures for signal processing and classification tasks.
Numerical Methods
Developing mathematical approaches to infer kinetics as a system of ODEs from time-series data.
Causality in ML
Investigating causal relationships in machine learning models and their applications.