SSVEP signatures of binocular rivalry during simultaneous EEG and fMRI

Abstract

Background: Binocular rivalry is a perceptual phenomenon that arises when two incompatible images are presented separately, one to each eye, and the observer experiences involuntary perceptual alternations between the two images. If the two images are flickering at two distinct frequencies, electroencephalography (EEG) can be used to track the frequency-tagged steady-state visually evoked potential (SSVEP) driven by each image as they compete for awareness, providing an objective measure of the subjective perceptual state. This spontaneous alternation in perceptual dominance is believed to be driven by neural processes across widespread regions in the brain, but the real-time mechanisms of these processes remain unclear.

New method: The goal of this study was to determine the feasibility of investigating binocular rivalry using a simultaneous EEG-fMRI approach in order to leverage the high temporal resolution of EEG with the high spatial resolution of fMRI.

Results: We have developed novel techniques for artifact removal and signal optimization for the rivalry-related SSVEP data collected simultaneously during fMRI.

Comparison with existing methods: Our methods address several significant technical challenges of recording SSVEP data in the noisy fMRI environment, and enabled us to successfully reconstruct SSVEP signatures of rivalry in a group of healthy human subjects.

Conclusion: Further development and application of these techniques will enable more comprehensive integration of EEG and fMRI data collected simultaneously and could have significant implications for EEG-fMRI studies of brain activity in general.

Keywords: Binocular rivalry; EEG; Frequency tagging; Multimodal imaging; SSVEP; fMRI.

Figure 1. Binocular rivalry stimulus design

Left) Rivalry condition and Right) Smooth replay condition, showing the red wedge smoothly expanding over a 1 sec interval.

Figure 2. Binocular rivalry stimulus presentation inside MRI scanner

Prism lenses were used to bring stimuli from two sides of the screen to appear in the center of the subject's visual field. A divider prevented crosstalk, and the inner wall of the bore was covered with a black felt panel to reduce peripheral reflection of the stimuli.

Figure 3. EEG preprocessing pipeline

Precise determination of heartbeat timing was crucial for removing the artifact. When ECG recordings were corrupted, we identified the EEG electrode with the strongest autocorrelation peak (Channel selection inset). EEG channels lack the distinct peak present in ECG, and detected artifacts are frequently misaligned (Artifact realignment insert, red arrows). We realigned artifacts after initial detection by their peak cross-correlation lag.

Figure 4. Using EEG electrode for CBA detection

(a) The electrode with the sharpest autocorrelation peak was selected for CBA detection (red box). (b) The FC4 artifact had several similar peaks, which can lead to misalignment of some of the artifacts. (c) Using electrode FC4 for CBA detection improves artifact removal compared to ECG timing (bottom middle vs. bottom left). After an additional cross-correlogram realignment step, the artifact is more consistent and the residual artifact after removal is reduced significantly (bottom right). [Only a subset of electrode autocorrelograms is shown for illustration. CBA artifacts are from electrode POz in a representative subject. Solid gray filled traces at the bottom of each graph represent the artifact variance across beats.]

Figure 5. Optimized spatial filter for each subject

(a) Electrode weights were unitless and constrained between [-1, 1] for the optimization procedure. For each subject, these weights were normalized with respect to the maximum weight value for visualization. The optimized filter topographies appear quite variable, but are largely concentrated on the occipital area, with additional variability to help reduce residual noise. (b) The spectral SNR for each subject using the Hjörth transform (red), spherical Laplacian (green), and the optimized filter (blue). The optimized filter consistently outperforms both Laplacian approximations. (c) The mean across subjects of each spatial filter type illustrates the spatial features that are preserved across subjects.

Figure 6. SSVEP Reconstruction using Phase-Specific Demodulation

SSVEP envelopes were extracted following the use of both an optimized spatial filter (top) and a standard Laplacian spatial filter (bottom). Representative results for a single subject are displayed for a 42 second period of continuous rivalry. See text for details.

Figure 7. SSVEP signal improvement at each processing stage

Red bars indicate power at the stimulus frequency. The timecourses show the amplitude of the SSVEP signal around locations where one frequency peaks. The light gray trace is the average of these peaks. The dark gray trace is the average of the other frequency. During binocular rivalry, the other frequency is generally suppressed at peaks of the first frequency. The greater depth of suppression seen at right shows that the optimized filtering greatly improved our ability to see this neural correlate of rivalry. Topographies are displayed using normalized weight units.

Figure 8. Individual subject co-localization of SSVEP sources and fMRI activation during binocular rivalry

Both SSVEP source maps (top row) and fMRI activation maps (bottom row) show significant changes in primary visual cortex and extrastriate cortex during rivalry. Similar activation patterns were observed for the replay conditions across subjects.