Brainwave-Controlled Indoor Navigation and Object Manipulation: An Integration of EEG Signal Processing and Localization Technologies

Abstract

This thesis presents a novel integration of Electroencephalography (EEG) signal processing with advanced localization techniques to develop a brain-computer interface (BCI) that enables users to control physical objects within an indoor environment, specifically the opening and closing of a door. The research is structured into two primary phases: the first phase involves the processing of an existing EEG dataset derived from motor imagery tasks related to hand movements, while the second focuses on implementing localization technologies to enhance user interaction with the environment. In the initial phase, the study leverages a publicly available EEG dataset from PhysioNet, specifically the EEG Motor Movement/Imagery Dataset, which includes recordings from many subjects performing various tasks, including real and imagined opening and closing of both fists. This data, originally in EDF format, was converted to CSV for better usability. A subset of the data relevant to the desired motor tasks was extracted and labeled for effective classification, applying several feature extraction techniques to distinguish between imaginary and real movements. This refined dataset serves as the foundation for developing algorithms capable of classifying these movements, intended to be deployed in real-time using a Ganglion Board and Raspberry Pi in the subsequent phase of the project to interpret user intentions regarding door control commands. The second phase focuses on integrating Bluetooth Low Energy (BLE) localization technologies with a specific localization algorithm to precisely determine the user’s position within an indoor setting. This capability is crucial for interaction between the user’s EEG-based commands and a fixed-point device installed on a door to enable the system to respond to the user and control opening or closing the door based on the user’s location and command. The system’s efficacy is evaluated based on its accuracy in classifying EEG signals and the precision of the localization method. The results demonstrate the feasibility of using EEG signals combined with localization technologies to interact with and control elements in a physical space, paving the way for broader applications in smart home systems and assistive technologies for individuals with mobility impairments. This thesis not only advances the field of BCIs but also contributes to the interdisciplinary applications of neuroscience and engineering.Master of Science in Applied Computer Scienc