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Riemannian classification of single-trial surface EEG and sources during checkerboard and navigational images in humans
- Source :
- PLoS ONE, PLoS ONE, Vol 17, Iss 1, p e0262417 (2022)
- Publication Year :
- 2022
- Publisher :
- Public Library of Science, 2022.
-
Abstract
- Objective Different visual stimuli are classically used for triggering visual evoked potentials comprising well-defined components linked to the content of the displayed image. These evoked components result from the average of ongoing EEG signals in which additive and oscillatory mechanisms contribute to the component morphology. The evoked related potentials often resulted from a mixed situation (power variation and phase-locking) making basic and clinical interpretations difficult. Besides, the grand average methodology produced artificial constructs that do not reflect individual peculiarities. This motivated new approaches based on single-trial analysis as recently used in the brain-computer interface field. Approach We hypothesize that EEG signals may include specific information about the visual features of the displayed image and that such distinctive traits can be identified by state-of-the-art classification algorithms based on Riemannian geometry. The same classification algorithms are also applied to the dipole sources estimated by sLORETA. Main results and significance We show that our classification pipeline can effectively discriminate between the display of different visual items (Checkerboard versus 3D navigational image) in single EEG trials throughout multiple subjects. The present methodology reaches a single-trial classification accuracy of about 84% and 93% for inter-subject and intra-subject classification respectively using surface EEG. Interestingly, we note that the classification algorithms trained on sLORETA sources estimation fail to generalize among multiple subjects (63%), which may be due to either the average head model used by sLORETA or the subsequent spatial filtering failing to extract discriminative information, but reach an intra-subject classification accuracy of 82%.
- Subjects :
- Male
Physiology
Vision
Event-Related Potentials
Social Sciences
Medicine and Health Sciences
Image Processing, Computer-Assisted
Psychology
Evoked Potentials
Clinical Neurophysiology
Brain Mapping
Multidisciplinary
Covariance
Electroencephalography
Signal Processing, Computer-Assisted
Healthy Volunteers
Electrophysiology
Signal Filtering
Bioassays and Physiological Analysis
Brain Electrophysiology
Brain-Computer Interfaces
Physical Sciences
Visual Perception
Medicine
Engineering and Technology
Sensory Perception
Female
Algorithms
Research Article
Adult
Imaging Techniques
Science
Neurophysiology
Geometry
Neuroimaging
Research and Analysis Methods
Membrane Potential
Computational Techniques
Tangents
Humans
Electrophysiological Techniques
Computational Pipelines
Cognitive Psychology
Biology and Life Sciences
Random Variables
Probability Theory
Signal Processing
Cognitive Science
Evoked Potentials, Visual
Perception
Clinical Medicine
Mathematics
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 17
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....9ffb6f3151bf1694f3c0e4211744955a