This final year project developed a Brain-Computer Interface (BCI) system that enables users to control smart home appliances entirely through brainwave signals - specifically EEG (electroencephalography) data captured via the NeuroSky MindWave headset and processed in real-time using MATLAB.
The motivation was accessibility: designing a control system that requires no physical interaction, enabling hands-free operation for people with limited motor function. The system responds to deliberate eye blink patterns detected in the raw EEG stream, translating them into appliance on/off commands via Bluetooth and Arduino-controlled relay modules.
The NeuroSky headset transmits raw EEG data over Bluetooth to a laptop running a custom MATLAB signal processing pipeline. The pipeline applies bandpass filtering to isolate relevant frequency bands (delta, theta, alpha) and uses amplitude thresholding to distinguish deliberate eye blinks from background neural noise.
Detected blink patterns (single, double, sustained) are mapped to specific appliance commands - lights, fans, and power sockets - and transmitted via the HC-05 Bluetooth module to an Arduino Uno, which drives 5V relay modules connected to real AC appliances in a mock home setup.
SQL was used to log command history, session timestamps, and device state - enabling basic usage analytics and session review for usability testing during the project evaluation phase.
"Combining signal processing with hardware control and structured data logging gave this project real-world complexity - it wasn't just a prototype, it was a working system that bridged neuroscience, embedded engineering, and software."
Go beyond the overview. Explore the complete project documentation, process, and supporting files on GitHub.