Identifying workload levels with a low-cost EEG device using an arithmetic task

Wiersma, M. (2016) Identifying workload levels with a low-cost EEG device using an arithmetic task. Bachelor's Thesis, Artificial Intelligence.

Preview	Text AI_BA_2016_MerelWiersma.pdf - Published Version Download (38MB) | Preview
	Text Toestemming.pdf - Other Restricted to Backend only Download (491kB)

Abstract

EEG-based systems are widely used because of their high temporal resolution, but they're not affordable for everyone. One of the newest low-cost EEG devices that was released is the Emotiv Insight, which is a mobile five channel headset promoted to produce clean, robust signals anytime, anywhere. The objective of this research was to test its abilities concerning distinguishing different workload levels, since monitoring workload can help us for example at work create a safer and better working environment, causing higher productivity and motivation. Previous research revealed that in event related potential (ERP) measures, the P300 reflects attentional and working memory processes. Therefore, we have manipulated workload levels by varying long term memory retrieval and working memory updates. The ERP results showed small significant differences around the P300 for absence compared to the presence of working memory updates at the AF4 channel. But for the other channels and the results concerning long term memory retrievel, no significant differences were found around the P300. Therefore, the conclusion of this research is that the Emotiv Insight was not capable of distinguishing between different workload levels.

Item Type:	Thesis (Bachelor's Thesis)
Degree programme:	Artificial Intelligence
Thesis type:	Bachelor's Thesis
Language:	English
Date Deposited:	15 Feb 2018 08:24
Last Modified:	15 Feb 2018 08:24
URI:	https://fse.studenttheses.ub.rug.nl/id/eprint/14473

Actions (login required)

View Item