Brain signals into accessible interaction.

My EEG and brain-computer interface research asks how physiological signals can become dependable inputs for real-time computing systems. The work connects wearable electroencephalography, intentional eye-blink patterns, nonlinear signal analysis, machine learning, and emerging agentic-AI architectures. Across these projects, the central challenge is not simply classifying a signal in a clean dataset; it is deciding which signal representations remain meaningful when recordings are noisy, people differ, and a system must respond quickly enough to support interaction.

One line of work treats eye blinks as intentional control signals rather than artifacts to discard. A real-time study of consecutive eye blinks compared statistical, time-domain, amplitude, and frequency-domain representations with conventional machine-learning and neural models. The result is a research pathway toward hands-free commands and more accessible interfaces. Related work studies attention in EEG through nonlinear and chaos-based features, while a broader survey organizes nonlinear techniques used to characterize complex brain dynamics.

A complementary direction explores whether low-frequency acoustic signals associated with cerebral blood flow could provide another non-invasive view of brain activity. That concept is deliberately framed as an early sensing hypothesis, not a clinical diagnostic system. Together, the EEG and acoustic studies define a broader program: combine carefully designed sensing with transparent signal processing, compare multiple modeling strategies, and validate every proposed interaction beyond a single controlled experiment.