Electroencephalography (EEG) is a mechanism to understand the brain’s functioning by analyzing brain electrical signals. More recently, it has been more commonly used in studies that are focused on the causation and effect of dementia. More tools are now available to gather EEG data. This brings about the challenge of understanding brain signals, which involves signal processing. Professionals with an electrical engineering background are very comfortable analyzing EEG data. Still, scientists in computer science and related fields need a source that can identify all the tools available and the process of analyzing the data. This paper deals specifically with the existing EEG data analysis tools and the processes involved in analyzing the EEG data using these tools. Furthermore, the paper goes in-depth into identifying the tools and the mechanisms of data processing techniques. In addition, it lists a set of definitions required for a better understanding of EEG data analysis, which can be challenging. The purpose of this paper is to serve as a reference for not only scientists that are new to EEG data analysis but also seasoned scientists that are looking for a specific data component in EEG and can go straight to the section of the paper that deals with the tool that they are using.

This study investigates the integration of assistive therapeutic robotics, wearable sensors, and spatial sensors within an intelligent environment tailored for dementia care. The feasibility study aims to assess the collective impact of these technologies in enhancing care giving by seamlessly integrating supportive technology in the background. The wearable sensors track physiological data, while spatial sensors monitor geo-spatial information, integrated into a system supporting residents without necessitating technical expertise. The designed space fosters various activities, including robot interactions, medication delivery, physical exercises like walking on a treadmill (Bruce protocol), entertainment, and household tasks, promoting cognitive stimulation through puzzles. Physiological data revealed significant participant engagement during robot interactions, indicating the potential effectiveness of robot-assisted activities in enhancing the quality of life for residents.