Vahid Abootalebi

This paper investigates sparse representation-based classification methods for 14-tumor microarray data, showing improved speed and accuracy by selecting subset atoms and using SL0 sparse coding.

This paper introduces a framework for constructing EEG-based brain graphs with small-world and scale-free properties, improving motor imagery decoding and brain fingerprinting performance.

This paper introduces a method to transform EEG data into a spectral representation using learned graphs for motor imagery decoding, showing improved performance over existing methods.

This paper introduces a method to estimate brain graphs using graph signal processing and demonstrates its superior performance over correlation-based functional connectomes in individual characterization.

This paper introduces a dimensionality reduction approach for motor imagery BCI using functional clustering, graph signal processing, and differential evolution, achieving high classification accuracy.