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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.

Combining Electroencephalograph and Functional Near Infrared Spectroscopy to Explore Users’ Mental Workload