University of Cincinnati · M.S. Computer Science · GPA 3.95/4.0
A Multimodal Neuroimaging Method for the Prediction of Visual Stimuli
Advised by Prof. Vikram Ravindra
EEG–fMRI fusion framework using temporal convolutional networks for neural decoding. 70-channel EEG + 3T fMRI (Wakeman–Henson dataset); SSS, ICA/wavelet-ICA, and DWT preprocessing; MNI-normalized fusiform/occipital ROIs. The combined model reached 84.8% within-subject and 81.1% cross-subject (LOSO) accuracy with ROC-AUC 0.93/0.90 — against 65.5% EEG-only and 74.6% fMRI-only, beating GRU, LSTM-CNN, and SVM baselines.