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Identifying Objective EEG Based Markers of Linear Vection in Depth
Stephen Palmisano1, Robert J Barry2, Frances M De Blasio2
1Centre for Psychophysics, Psychophysiology, and Psychopharmacology, University of WollongongWollongong, NSW, Australia; School of Psychology, University of WollongongWollongong, NSW, Australia.
Frontiers in Psychology
|August 26, 2016
Summary
This study shows electroencephalography (EEG) can objectively measure vection, or illusory self-motion. Specific EEG activity patterns correlate with the intensity of perceived self-motion during visual stimulation.
Area of Science:
- Neuroscience
- Human Perception
- Cognitive Psychology
Background:
- Vection, the illusion of self-motion, is a complex perceptual phenomenon.
- Objective measures for quantifying vection intensity are lacking.
- Electroencephalography (EEG) offers potential for real-time neural correlates of perception.
Purpose of the Study:
- To investigate the feasibility of using time-frequency EEG analysis to objectively assess vection.
- To identify specific EEG spectral patterns associated with vection experiences.
- To correlate EEG findings with subjective ratings of vection intensity.
Main Methods:
- Compared event-related spectral perturbation (ERSP) data from 10 observers using EEG.
- Utilized time-frequency Principal Components Analysis (t-f PCA) for data decomposition.
- Presented participants with either a vection-inducing optic flow display or a scrambled control display.
Main Results:
- An increase in 10 Hz alpha activity, occurring 14 seconds after motion onset, correlated positively with vection intensity.
- Decreases in beta and delta activity were negatively related to vection intensity.
- Post-stimulus alpha band increases correlated with vection intensity, possibly reflecting aftereffects or reporting preparation.
Conclusions:
- Time-frequency EEG analysis can provide objective markers for detecting vection.
- Specific EEG spectral changes reliably indicate the presence and intensity of illusory self-motion.
- This approach offers a novel method for studying the neural basis of vection.
