CV

Education

Ph.D in Machine Learning, Universiti Teknologi PETRONAS, 2024
M.S. in Mathematics, AMU, India, 2018
B.S. in Mathematics, AMU, India, 2016

Work experience

2021 - 2024 Thesis: Development of adaptive threshold and graph-based methods for electroencephalogram channel selection in brain-computer interface.
- Developed an adaptive threshold-based method integrated with a modified Convolutional Neural Network (CNN) architecture to enhance EEG channel selection in Brain-Computer Interface (BCI) applications. This approach achieved significant improvements in accuracy, reaching 93.90% with the k-adaptEEGCS model and 95.60% with the G-EEGCS model.
- Introduced Hyper-GCN, a novel hyperscanning EEG signal classification approach in an end-to-end manner. This introduces graph convolutional neural networks (GCN) to handle the spatial-temporal dynamics of EEG data for improving the accuracy of neuroimaging studies. The model achieved an accuracy of 95.92% on eight average subject-wise trials and 93.54% on a hyperscanning dataset containing considerable variability in tasks.

Tools

Pandas, Numpy, Matplotlib, Seaborn, Plotly, Sklearn, Keras, PyTorch, R, Matlab, Tableau, Streamlit
GNN, GCN, CNN, K-means, PCA, Transformer
MS Word, Excel, PowerPoint

Skills

Applied Mathematics
Signal Processing
Image Processing
Probability
Python Programming
Deep Learning
Linear Algebra
Calculus
Optimization
Reinforcement Learning

Publications

G-EEGCS: Graph-based optimum electroencephalogram channel selection

Abdullah, Faye, I., Yusoff, M. Z., & Belhaouari, S. B. (2024). G-EEGCS: Graph-based optimum electroencephalogram channel selection. Biomedical Signal Processing and Control, 98, 106763.

k-adaptEEGCS: Adaptive Threshold Based Automatic EEG Channel Selection

Abdullah, I. Faye, M. Tanveer and A. Vurity, " k -AdaptEEGCS: Adaptive Threshold Based Automatic EEG Channel Selection," in IEEE Sensors Letters, vol. 8, no. 10, pp. 1-4, Oct. 2024, Art no. 7501004, doi: 10.1109/LSENS.2024.3458996. keywords:

Artificial Neural Networks Solutions for Solving Differential Equations: A Focus and Example for Flow of Viscoelastic Fluid with Microrotation

Abdullah, Ibrahima Faye, & Laila Amera Aziz. (2023). Artificial Neural Networks Solutions for Solving Differential Equations: A Focus and Example for Flow of Viscoelastic Fluid with Microrotation. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 112(1), 76–83. https://doi.org/10.37934/arfmts.112.1.7683.

Electroencephalogram Channel Selection using Deep Q-Network

Abdullah, I. Faye and M. R. Islam, "Electroencephalogram Channel Selection using Deep Q-Network," 2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON), New Delhi, India, 2023, pp. 340-344, doi: 10.1109/REEDCON57544.2023.10151281.

EEG channel selection techniques in motor imagery applications: a review and new perspectives

Abdullah; Faye, I.; Islam, M.R. EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives. Bioengineering 2022, 9, 726. https://doi.org/10.3390/bioengineering9120726.

Reviewer

Health information science and systems (SPRINGER)
Human-centric intelligent systems (SPRINGER)
ICONIP conference

Abdullah

CV