A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Using a low-cost electroencephalograph for task classification in HCI research
UIST '06 Proceedings of the 19th annual ACM symposium on User interface software and technology
Feasibility and pragmatics of classifying working memory load with an electroencephalograph
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Using fNIRS brain sensing in realistic HCI settings: experiments and guidelines
Proceedings of the 22nd annual ACM symposium on User interface software and technology
Using EEG and NIRS for brain-computer interface and cognitive performance measures: a pilot study
International Journal of Cognitive Performance Support
Hi-index | 0.00 |
We discuss the physiological metrics that can be measured with electroencephalography (EEG) and functional near infrared spectroscopy (fNIRs). We address the functional and practical limitations of each device, and technical issues to be mindful of when combining the devices. We also present machine learning methods that can be used on concurrent recordings of EEG and fNIRs data. We discuss an experiment that combines fNIRs and EEG to measure a range of user states that are of interest in HCI. While our fNIRS machine learning results showed promise for the measurement of workload states in HCI, our EEG results indicate that more research must be done in order to combine these two devices in practice.