Multidisciplinary development of electroencephalogram (EEG)-based smart head piece prototype for everyday environments monitoring
Gourlay, Banu (2023)
Gourlay, Banu
2023
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202303123417
https://urn.fi/URN:NBN:fi:amk-202303123417
Tiivistelmä
The objective of this thesis was to create an EEG-based monitoring solution concept for consumers for everyday environments. This was done in collaboration with a project between Satakunta University of Applied Sciences (SAMK) and Tampere University (TAU). The thesis followed the Design Thinking method as a roadmap, which was found to be suitable but not without its limitations. Through user focus, collaboration, flexibility, and iteration a complex problem was turned into an innovative solution. Lack of structure, resources, and motivation were identified as the main drawbacks of the method.
The data was collected from a workshop and three focus group meetings. The arousal measurement concept was the main outcome from the ideation workshop. The first focus group outcome included the user interface (UI) prototype I, and head piece design specifications. The focus group II outcome included the first handmade prototype and an updated version with buttons to give a better adjustment, and UI prototype II. After focus group III, the initial design was changed to a modular EEG product design concept on a fabric, and UI design III and a user flowchart were created.
The solution had two components. The modular biosensors would collect real time EEG data and present it to the users in an arousal curve. UI biofeedback curve would show diminishing, optimal and increasing arousal levels in real time and alarm users when they are not in a desired range (sleep, apathy, optimal level, stress, anxiety, panic). The curve would monitor the user’s brain activity status to help maintain optimal arousal levels.
Arousal monitoring biosensors and the software could be used for multiple purposes and applied in different use cases including sleep, stress, anxiety, or panic attacks, allowing a longer period of monitoring brain activity, which may not be possible in a laboratory setting. Depression, anxiety, and stress-related conditions are increasing. The primary risk to employee welfare has been found to be psychological, with mental health and stress being the top two causes of long-term absenteeism. Remote monitoring could lessen the cost of hospitalizations to community and reduce pressure on healthcare system. EEG for everyday environments could also allow users to monitor their wellbeing and adjust activities accordingly. More research needs to be done to validate these concepts.
The data was collected from a workshop and three focus group meetings. The arousal measurement concept was the main outcome from the ideation workshop. The first focus group outcome included the user interface (UI) prototype I, and head piece design specifications. The focus group II outcome included the first handmade prototype and an updated version with buttons to give a better adjustment, and UI prototype II. After focus group III, the initial design was changed to a modular EEG product design concept on a fabric, and UI design III and a user flowchart were created.
The solution had two components. The modular biosensors would collect real time EEG data and present it to the users in an arousal curve. UI biofeedback curve would show diminishing, optimal and increasing arousal levels in real time and alarm users when they are not in a desired range (sleep, apathy, optimal level, stress, anxiety, panic). The curve would monitor the user’s brain activity status to help maintain optimal arousal levels.
Arousal monitoring biosensors and the software could be used for multiple purposes and applied in different use cases including sleep, stress, anxiety, or panic attacks, allowing a longer period of monitoring brain activity, which may not be possible in a laboratory setting. Depression, anxiety, and stress-related conditions are increasing. The primary risk to employee welfare has been found to be psychological, with mental health and stress being the top two causes of long-term absenteeism. Remote monitoring could lessen the cost of hospitalizations to community and reduce pressure on healthcare system. EEG for everyday environments could also allow users to monitor their wellbeing and adjust activities accordingly. More research needs to be done to validate these concepts.