Date of Award
Master of Science in Electrical Engineering (MSEE)
Electrical, Computer, and Biomedical Engineering
The brain is one of the most important and complex organs in the human body. It is responsible for the essential functions of the body. Brain activity monitoring is important in order the diagnosis of neurological disorders and for improving our understanding of the nervous system. To understand the structure and functionality of the brain, it must be monitored in natural or nonclinical settings such as during daily activities including finger tapping and thinking, as well as in outdoor activities like walking, jogging and cycling. This monitoring should assist in the detection of these natural circumstances. There are several techniques to monitor the brain’s structure and function including functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magnetoencephalography (MEG), positron emission tomography (PET), and single positron emission tomography (SPECT).
Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain monitoring method which emits NIR light into the brain tissue and detects the transmitted or reflected light from the brain.
fNIRS has many advantages over its counterparts in terms of its relatively high temporal/spatial resolution, mobility, portability and safety. It has a higher temporal resolution than fMRI and a greater spatial resolution than EEG. The preparation for fNIRS measurement is also easier than for EEG, as conductive gel is not generally required. This also means patients do not need to wash their hair after fNIRS measurements. Moreover, it is safer compared to LASER technology due to the usage of NIR light.
Despite advances in fNIRS technologies, there remain challenges. Its physical set up, including both space requirements and the presence of long cables, contributes to the difficulty in using fNIRS systems for non-clinical environments. Preparation time and price are also concerns. For its use in non-clinical experiments, a portable fNIRS system must be wearable or easily carried by the subject. The cables must also be short and durable.
This master thesis research was aimed at addressing the hardware limitations of fNIRS by developing a wearable, small form factor fNIRS system. I have developed an fNIRS system based on the Intel Edison, a high-end embedded system designed for internet of things applications. The Edison on the fNIRS system drives the lighting and data collection process. The system is comprised of two components: a headband containing fNIRS sensors and a control unit which controls the data collection and transmission. The control unit includes an LED driver circuit to turn LEDs on and off, an analog to digital converter to digitize the analog value from the photodetectors and a microprocessor to control data collection and transmission. The control unit is connected to a computer via serial communication. The computer acts as a computing unit, storing the collected data and making the necessary evaluations and calculations. The thesis research involved developing three versions to achieve the goal of creating a robust, wearable, wireless system. We also optimized the intensity of emitted light, the sampling rate and communication between control unit and computing unit.
To develop a control unit, I first tested all the components on a breadboard. Then I combined the system on a single 6cm x 7.7cm PCB, effectively miniaturizing the system. Then I connected the sensor with the computing unit and tested the system on a human to measure hemodynamic activity. Simultaneously data was collected from subjects’ fingertips and arms for validation. In conclusion, the system successfully detected the subjects’ pulses and hemodynamic changes.
Since the system is small enough to carry in a pocket or put in the back of the head cap, its next version will be a portable and wearable device with the potential to be used in ambulatory experiments.
Cay, Gozde, "Design of a Wearable fNIRS Neuroimaging Device with an Internet-of-Things Architecture" (2017). Open Access Master's Theses. Paper 1121.