devices to communicate. These systems typically depend on eye-gaze tracking or touch input, which can become unreliable as a user's condition progresses or environmental conditions vary. We present a low-cost, brain-controlled word selection keyboard, developed entirely with consumer-grade components: a Muse 2 electroencephalography (EEG) headband, a smartphone running the Mind Monitor application, and a laptop. The system streams raw four-channel EEG data over Open Sound Control (OSC) streaming and extracts 65 spectral features for every two-second window collected using an FFT-based signal processing pipeline. The input is classified into one of four classes (blink, look-left, look-right, and background) using a compact neural network. A Pygame-based visual keyboard inspired by TD-Snap, a commercial AAC interface, allows users to navigate a word grid through the lateral eye movements. Words are selected with deliberate blinks, which triggers both text-to-speech output and an HID keyboard emulation. We describe the end-to-end system architecture and methods developed for live deployment. We also discuss the practical challenges encountered during real-time operation, mainly the sampling-rate discrepancies between offline and online data, the required action synchronization with the 2 second windowing periods, and the inherent signal limitations of four-channel frontal/temporal EEG. The complete system is open-source and demonstrates the feasibility of using consumer-based BCI hardware as a method of communication for users experiencing motor impairments.