This work presents BlinkLens, a portable and low-cost communication system based on Electrooculography (EOG) sensors, designed to assist individuals with speech disabilities. The system recognizes voluntary eye movements from the “Blink to Speak” (BTS) language to encode messages. Using biosignal processing and machine learning, EOG signals are segmented, features extracted, and classified into BTS commands. An ergonomic interface and real-time acquisition environment were developed to support intuitive use. BlinkLens offers an accessible solution with strong potential to improve quality of life and promote social inclusion for people with severe speech and motor impairments. The proposed system achieved classification F1-scores of up to 74.71% using healthy participants. Although promising, the solution has not yet been validated with clinical populations, which represents a critical step for future research.