Abstract

Electroencephalography (EEG) records the EEG signal with very high temporal resolution. The potential difference between the recording electrode and the reference electrode is used as the voltage, and the voltage changes over time to form the waveform of EEG, which can provide a basis not only for clinical diagnosis of mental illnesses but also for academic research to measure the mental effort of subjects. In the field of neuroscience, EEG technology is considered to be one of the most important technical tools for studying the brain, and the EEG signals collected need to be analyzed and processed in several complex steps to obtain the final desired results. The first step involved in the analysis and processing of EEG signals is the pre-processing of EEG signals. Currently, researchers have developed several open source toolkits for EEG signal processing and analysis, and these open source EEG toolkits have contributed to the development of the neuroscience field, but designing and developing a more complete EEG processing toolkit has been a challenging problem. Therefore, the focus of this thesis is to design and develop a user-centered information visualization platform that can preprocess EEG data.

In this study, the platform was designed using the analytical approach of environment-based design, and the platform was implemented using the python language. A series of validations of the platform's feasibility were performed based on data from loosely controlled EEG experiments at Concordia University's Design Lab. Based on the platform development results, a survey questionnaire and informed consent form for subjects were designed, and a survey on user satisfaction was conducted for the participating subjects.