Abstract
Recent advances have made it possible to decode various aspects of visually presented stimuli from patterns of scalp EEG measurements. As of recently, such multivariate methods have been commonly used to decode visual-spatial features such as location, orientation, or spatial frequency. In the current study, we show that it is also possible to track visual colour processing by using Linear Discriminant Analysis on patterns of EEG activity. Building on other recent demonstrations, we show that colour decoding: (1) reflects sensory qualities (as opposed to, for example, verbal labelling) with a prominent contribution from posterior electrodes contralateral to the stimulus, (2) conforms to a parametric coding space, (3) is possible in multi-item displays, and (4) is comparable in magnitude to the decoding of visual stimulus orientation. Through subsampling our data, we also provide an estimate of the approximate number of trials and participants required for robust decoding. Finally, we show that while colour decoding can be sensitive to subtle differences in luminance, our colour decoding results are primarily driven by measured colour differences between stimuli. Colour decoding opens a relevant new dimension in which to track visual processing using scalp EEG measurements, while bypassing potential confounds associated with decoding approaches that focus on spatial features.
Original language | English |
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Article number | 118030 |
Pages (from-to) | 1-8 |
Number of pages | 8 |
Journal | NeuroImage |
Volume | 237 |
Early online date | 6 May 2021 |
DOIs | |
Publication status | Published - 15 Aug 2021 |
Bibliographical note
Funding Information:This research was funded by an ESRC Grand Union studentship and the Scatcherd European Scholarship awarded to J.E.H., a Marie Skłodowska-Curie Fellowship from the European Commission ( ACCESS2WM ) and an ERC Starting Grant from the European Research Council (MEMTICIPATION, 850636 ) to F.v.E., and was supported by a James S. McDonnell Foundation Scholar Award ( 220020405 ), an ESRC grant ( ES/S015477/1 ), the Medical Research Council Career Development Award ( MR/J009024/1 ), and a Biotechnology and Biological Sciences Research Council award ( BB/M010732/1 ) to M.G.S., as well as a James S. McDonnell Foundation Understanding Human Cognition Collaborative Award (number 220020448 ), and a Wellcome Trust Senior Investigator Award ( 104571/Z/14/Z ) to A.C.N., and was supported by the NIHR Oxford Health Biomedical Research Centre. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust ( 203139/Z/16/Z ). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Publisher Copyright:
© 2021 The Authors
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
Funding
This research was funded by an ESRC Grand Union studentship and the Scatcherd European Scholarship awarded to J.E.H., a Marie Skłodowska-Curie Fellowship from the European Commission ( ACCESS2WM ) and an ERC Starting Grant from the European Research Council (MEMTICIPATION, 850636 ) to F.v.E., and was supported by a James S. McDonnell Foundation Scholar Award ( 220020405 ), an ESRC grant ( ES/S015477/1 ), the Medical Research Council Career Development Award ( MR/J009024/1 ), and a Biotechnology and Biological Sciences Research Council award ( BB/M010732/1 ) to M.G.S., as well as a James S. McDonnell Foundation Understanding Human Cognition Collaborative Award (number 220020448 ), and a Wellcome Trust Senior Investigator Award ( 104571/Z/14/Z ) to A.C.N., and was supported by the NIHR Oxford Health Biomedical Research Centre. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust ( 203139/Z/16/Z ). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Funders | Funder number |
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MEMTICIPATION | |
James S. McDonnell Foundation | ES/S015477/1, 220020405 |
Wellcome Trust | 104571/Z/14/Z |
Horizon 2020 Framework Programme | 850636 |
Medical Research Council | MR/J009024/1 |
Biotechnology and Biological Sciences Research Council | BB/M010732/1, 220020448 |
Economic and Social Research Council | |
European Commission | ACCESS2WM |
European Research Council | |
NIHR Oxford Biomedical Research Centre | 203139/Z/16/Z |
Keywords
- Color
- Decoding
- EEG
- Features
- Supervised learning
- Vision