Local field potentials (LFPs) encode visual information via variations in power at many frequencies. These variations are complex and depend on stimulus and cognitive state in ways that have yet to be fully characterized. Specifically, the frequencies (or combinations of frequencies) that most robustly encode specific types of visual information are not fully known. To address this knowledge gap, we used intracranial EEG to record LFPs at 858 widely distributed recording sites as human subjects (six males, five females) indicated whether briefly presented natural scenes depicted one of three attended object categories. Principal component analysis applied to power spectra of the LFPs near stimulus onset revealed a broadband component (1–100 Hz) and two narrowband components (1–8 and 8–30 Hz, respectively) that encoded information about both seen and attended categories. Interestingly, we found that seen and attended categories were not encoded with the same fidelity by these distinct spectral components. Model-based tuning and decoding analyses revealed that power variations along the broadband component were most sharply tuned and offered more accurate decoding for seen than for attended categories. Power along the narrowband delta–theta (1–8 Hz) component robustly decoded information about both seen and attended categories, while the alpha–beta (8–30 Hz) component was specialized for attention. We conclude that, when viewing natural scenes, information about the seen category is encoded via broadband and sub-gamma (<30 Hz) power variations, while the attended category is most robustly encoded in the sub-gamma range. More generally, these results suggest that power variation along different spectral components can encode qualitatively different kinds of visual information.
Bibliographical noteFunding Information:
This research was supported by National Eye Institute Grant R01-EY-023384. We thank the Medical University of South Carolina Comprehensive Epilepsy Center for support in the execution of this study.
Copyright © 2020 the authors.
Copyright 2020 Elsevier B.V., All rights reserved.
- Computational modeling
- Intracranial EEG
- Local field potentials
- Spectral patterns
- Visual attention
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural