Best of my Research
My research focuses on causal representation learning, signal processing and machine learning algorithms for different applications, mainly medicine, neuroscience and brain-computer interfaces.
Publications
Subject-independent P300 Speller Classification using Time-Frequency Representation and Double Input CNN with Feature Concatenation
DSP 2023 • 2023
Zangar Ermaganbet, Ayana Mussabayeva, Muhammad Tahir Akhtar, Prashant Kumar Jamwal
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.
Z. Ermaganbet, A. Mussabayeva, M. T. Akhtar, P. K. Jamwal, "Subject-independent P300 speller classification using time-frequency representation and double input CNN with feature concatenation," 2023 IEEE International Conference on Digital Signal Processing (DSP), pp. 1-5, 2023.
Cited by: 3
Event-related spectrogram representation of EEG for CNN-based P300 speller
APSIPA ASC 2021 • 2021
Ayana Mussabayeva, Muhammad Tahir Akhtar, Prashant Kumar Jamwal
This research explores the use of event-related spectrograms as a representation method for EEG signals in CNN-based P300 speller systems.
A. Mussabayeva, M. T. Akhtar, P. K. Jamwal, "Event-related spectrogram representation of EEG for CNN-based P300 speller," 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1267-1272, 2021.
Cited by: 5
Ensemble learning approach for subject-independent P300 speller
EMBC 2021 • 2021
Ayana Mussabayeva, Muhammad Tahir Akhtar, Prashant Kumar Jamwal
This paper proposes an ensemble learning approach to improve the accuracy and robustness of subject-independent P300 speller systems.
A. Mussabayeva, M. T. Akhtar, P. K. Jamwal, "Ensemble learning approach for subject-independent P300 speller," 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 6629-6632, 2021.
Cited by: 7
Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller
Applied Sciences 2021 • 2021
Ayana Mussabayeva, Muhammad Tahir Akhtar, Prashant Kumar Jamwal
This study presents an ensemble voting-based approach for multichannel EEG classification in subject-independent P300 speller systems.
A. Mussabayeva, M. T. Akhtar, P. K. Jamwal, "Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller," Applied Sciences, vol. 11, no. 23, p. 11252, 2021.
Cited by: 9
Comparison of Generic and Subject-Specific Training for Features Classification in P300 Speller
APSIPA ASC 2020 • 2020
Ayana Mussabayeva, Muhammad Tahir Akhtar, Prashant Kumar Jamwal
This research compares the effectiveness of generic and subject-specific training approaches for feature classification in P300 speller systems.
A. Mussabayeva, M. T. Akhtar, P. K. Jamwal, "Comparison of Generic and Subject-Specific Training for Features Classification in P300 Speller," 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1105-1110, 2020.
Cited by: 4
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.
Medical AI
Investigating medical applications of AI for neural disorders, such as Alzheimer's disease.
Causality in ML
Investigating causal relationships in machine learning models and their applications.