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Psycho-physiology deals with measuring and interpreting physiological signals from humans. Such signals can be used for example to get an objective measure of a user study participant’s reaction to certain events or stimuli. Furthermore, systems can be designed in a way to leverage physiological signals as novel input modalities. In our team we investigate both of these areas.

Used as a measurement tool such signals give a direct insight into how the body reacts. While this reaction still has to be interpreted by researchers (e.g. an increase in sweat level can indicate an affective response), psycho-physiology allows direct and real time measurement or user monitoring.

Used as an interface, these signals afford the design of novel interaction modalities such as Brain-Computer Interfaces (BCI) or contact-less interaction based on eye-gaze tracking. Such systems may be used as a novel interface for computers or robots in situations in which other interaction options are non available (e.g. interfaces for severely physically disabled people).

The most commonly used sensors and physiological signals are:

  • Electroencephalogram (EEG) measuring brain activity (see Figure 1)
  • Electrocardiogram (ECG) measuring heart activity
  • Galvanic Skin Response (GSR) measuring skin conductance (i.e. changes in sweat level)
  • Respiration,
  • Electromyogram (EMG) measuring muscle activity
  • Electrooculogram (EOG) measuring eye activity
  • Eye-gaze tracking and pupillometry (measuring pupil dilation)
Figure 1: Electroencephalography (EEG) measurements to monitor brain activity.

All these approaches use specialised sensors or sensor arrangements for measuring data.

Our work with psycho-physiological sensors includes:

  • Designing an EEG-based BCI for a motor rehabilitation system: This system also includes eye-gaze tracking as an additional input and interaction modality.
  • Eye-gaze tracking to measure changes in pupil dilation: We conduct research on using this information, also called pupillometry, to improve the accuracy of the EEG-based BCI, and as a measure for cognitive processes and operator workload.
  • Eye-gaze tracking for measuring visual-cognitive performance to natural as well as to artificial, fully controlled stimuli.
  • GSR as an interactive measure of the user’s affective state and real-time system input (e.g. measuring fear response to virtual spiders); and GSR as a real-time measure of operator workload as well as team workload.
  • Psycho-physiology based human robot interaction (HRI): we use physiological measures both for quantifying user performance in HRI as well as a control modality for HRI (see Figure 2).
Figure 2: User interacting with a robot arm through multi-modal control, including eye gaze tracking.