Date of Award
Summer 2026
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computer Science
First Advisor
Sheikh Iqbal Ahamed
Second Advisor
Praveen Madiraju
Third Advisor
Rumi Ahmed Khan
Abstract
Neurological disorders are among the leading causes of death and disability worldwide, yet the tools to observe brain function remain costly, cumbersome, and clinic-bound: a single electroencephalogram (EEG) in the United States averages roughly US$972 and requires trained staff and gel electrodes. This dissertation develops EchoBrain, an artificial-intelligence-enhanced framework that assesses brain function from cognitive signals, namely conventional scalp EEG together with a novel acoustic view of cerebral activity (the infrasound produced by cerebral blood flow), and moves toward an accessible, low-cost, non-invasive alternative to traditional EEG. The work spans four aims. Aim 1 introduces an interpretable, chaos-theory-based method for detecting attention from EEG (time delay, embedding dimension, and correlation dimension), framed by a survey of nonlinear EEG techniques. Aim 2 delivers a real-time brain-computer interface (BCI) that classifies no-, single-, and consecutive two-blink events, reaching 89% accuracy with classicalmodels and 98.67% recall with a YOLOv8 detector. Aim 3 advances the central hypothesis that blood-flow infrasound carries brain-state information, implementing the NeuroEcho pipeline that maps acoustic windows to EEG band-states across ten participants and fifty sessions. Aim4 frames the system within agentic AI for BCIs and validates an EchoBrain prototype with ten participants performing blink-based yes/no communication, attaining 97% mean accuracy and a System Usability Scale score of 94.25. Together the aims demonstrate an end-to-end path from interpretable EEG analysis to a validatedmultimodal brain-assessment system; the dissertation also documents current limitations and outlines future work, including generative waveformreconstruction, an earplug infrasound device, and larger clinical cohorts.