The development of smaller and power efficient electronics over the last few decades has facilitated the growth of lower cost, wearable versions of common medical devices. As a result, low-cost, wearable systems that measure neural activity using electroencephalog- raphy—a non-invasive technique that records electrical activity from the brain—have entered the market for consumer purchase. From this expansion, there is a need to assess the potential applications and capabilities of these wearables to measure reliable neural signals.
The thesis work presents a low-cost version of a $100k traditional system. The optimized wearable system, which includes the low-cost OpenBCI Cyton Board with Daisy chain EEG system, comprises of commercially available parts and costs $1.5k. The thesis first discusses the optimization of the out-of-box version of the OpenBCI Cyton Board. The Cyton Board had a number of deficiencies including detected initial timing discrepancies in the design. A number of software patches and hardware modules were developed as a result. The second part of the thesis bench-marks the finalized system with contemporary studies that collected neural signals using clinical EEG systems through two experiments. The first tests the system’s ability to collect neural data from the oddball paradigm and the second tests the system’s ability to collect neural data as elicited by an interactive version of a classical neuroscience research. These experiments demonstrate the potential of collecting high-quality neural signals in every-day work spaces. The third part of the thesis describes a classification system developed from a repository of data collected from the second benchmark experiment. The support vector machine classifier receives the features of a single-trial event related potential (ERP) and predicts the feedback that initiated the ERP.
The work aims to give consumers accessible and affordable neural metrics outside of traditional systems and expand the computer environments in which these signals are monitored.
|Commitee:||Casey, Michael, Luke, Geoffrey, Dikker, Suzanne|
|School Location:||United States -- New Hampshire|
|Source:||DAI-B 81/4(E), Dissertation Abstracts International|
|Subjects:||Biomedical engineering, Neurosciences|
|Keywords:||Brain computer interfaces, Classification, Electroencephalography, ERP, Feedback related response, Wireless EEG|
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