Engineering
JOSH MURZELLO

Brain Controlled Interface

Bridging mind and machine through neural interfaces

BCI Project Setup

Project Overview

This brain-computer interface project demonstrates the fascinating potential of direct neural control over mechanical systems. Using the NeuroSky MindWave Mobile 2 EEG headset, I developed a system capable of detecting and interpreting specific brainwave patterns—particularly alpha waves (8-12 Hz) associated with relaxation and beta waves (12-30 Hz) linked to active thinking and concentration.

Technology Stack

NeuroSky MindWave Arduino Python Signal Processing

Implementation

The system translates neural activity into real-time control commands for an RC car through an Arduino-based interface. By developing custom signal processing algorithms, I was able to distinguish between different mental states and map them to specific vehicle movements. This creates an intuitive control mechanism where sustained concentration can be used to navigate the vehicle in real-time.

Technical Architecture

Data Acquisition

Real-time EEG data collection through the NeuroSky's ThinkGear™ technology, capturing both raw brainwave data and derived attention/meditation metrics.

Signal Processing

Custom algorithms for noise reduction and feature extraction, focusing on alpha and beta wave isolation for reliable control signals.

Control Interface

Arduino-based system translating processed neural signals into precise motor control commands for the RC vehicle.