Riemannian classification of single-trial surface EEG and sources during checkerboard and navigational images in humans

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...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2022-01, Vol.17 (1), p.e0262417-e0262417
Main Authors: Simar, Cédric, Petit, Robin, Bozga, Nichita, Leroy, Axelle, Cebolla, Ana-Maria, Petieau, Mathieu, Bontempi, Gianluca, Cheron, Guy
Format: Article
Language:English
Subjects:
Accuracy
Adult
Algorithms
Analysis
Biology and Life Sciences
Biomechanics
Brain
Brain stimulation
Brain-Computer Interfaces
Classification
Clinical trials
Computer applications
Computer science
Dipoles
EEG
Electroencephalography
Electroencephalography - methods
Engineering and Technology
Evoked Potentials, Visual - physiology
Female
Frequency dependence
Geometry
Health aspects
Healthy Volunteers
Human-computer interface
Humans
Image classification
Image Processing, Computer-Assisted - methods
Implants
Indexing
Information processing
Laboratories
Locking
Machine learning
Male
Medicine and Health Sciences
Methods
Navigation behavior
Neurophysiology
Neurosciences
Physical Sciences
Pipelines
Research and Analysis Methods
Signal Processing, Computer-Assisted
Social Sciences
Spatial filtering
Visual discrimination
Visual evoked potentials
Visual evoked response
Visual Perception - physiology
Visual signals
Visual stimuli
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. 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. 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%.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0262417