**Rationale** ------------- One of the primary goals of consciousness research is to explain and track the neural correlates of phenomenal experience (Tsuchiya et al., 2015). However, previous research has continuously relied on participants to communicate to researchers what they are perceiving using self-report methods. As a result, when attempting to identify the elusive neural correlates of consciousness, a prevailing point is being increasingly cited in contemporary neuroscience: it is important to distill these correlates to the fundamental mechanisms that underpin them (Aru et al., 2012). When asking participants to track their phenomenal experience via self-report, fronto-parietal network activation is associated with metacognition and introspection (Koch et al., 2016) rather than the minimal neural mechanisms necessary for conscious experience to emerge. This evidence therefore demonstrates that the explicit act of reporting can become a confound when attempting to disentangle the neural mechanisms purely associated with conscious awareness. To overcome this issue associated with active reports, researchers have called for more studies that utilise “no-report” paradigms, where (electro)physiological signals are used as reliable corollaries of perceptual status of specific stimuli. One popular visual perceptual phenomenon that has been frequently used to study conscious awareness is binocular rivalry (BR; Wheatstone, 1838). BR is a visual perceptual phenomenon that occurs due to the dichoptic presentation of two distinct images. Presenting these distinct images to each eye separately will lead to the two stimuli alternately reaching conscious awareness, as well as the potential fusion of both stimuli for short periods of time during the transition period (also known as ‘mixed percepts’). The popularity of BR in studies of consciousness is due to the fact that it provides a direct measure of what enters individual's conscious awareness, without modulating the stimuli (Frith et al., 1999). BR paradigms have therefore been used over the past several decades in order to identify necessary factors that permit consciousness (for a review, see Tong et al., 2006). However, much like other studies of consciousness, BR paradigms have relied on self-report methods to elucidate the phenomenal experience of participants. The rationale of this study is to therefore contribute to the current literature of no-report paradigms in the context of binocular rivalry. We intend to use the steady-state visual evoked potential (SSVEP) alongside electroencephalography (EEG) to create an objective measure of the participants' phenomenological percept during binocular rivalry. SSVEPs will be extracted using a novel technique, dubbed rhythmic entrainment source separation (RESS; Cohen & Gulbinaite, 2017). RESS has demonstrably extracted SSVEPs at a higher SNR than other SSVEP extraction techniques. However, it has not been used in a binocular rivalry paradigm prior to this research. We also aim to use a novel method, in conjunction with RESS, and investigate its accuracy in comparison to reported perceptual switch rate. **Design** ---------- The study will use a 2 (frequency: slow vs. fast) x 2 (trial duration: short vs. long) within participants design. The first flicker frequencies presented will be 14Hz & 17Hz (Slow). The second group of flicker frequencies presented will be 29Hz & 34Hz (Fast). The trial durations will last either 1 or 6 minutes. In these separate combinations of trial duration and frequency, participants will do one of the following: a) report perceptual switches, b) report mixed percepts, or c) passively view the stimuli. **Participants** ---------- A power analysis in G*Power (Faul et al., 2009) revealed that we would need to collect 11 participants for a large effect size of 0.8 using a one-tailed hypothesis. This is echoed by our pilot data, as we found a very strong correlation between the turning points of the SSVEPs and the reported perceptual switch rate (r=.95) with 13 participants. Based on this information, and the sample sizes of previous research with SSVEPs in BR (e.g. Zhang et al., 2011; Brown & Norcia, 1997), 16 participants will be gathered for the study. The following inclusion criteria will be applied: fluency in English, between 18-30 years of age, no history of epilepsy or relatives with epilepsy, migraine, vision problems, glasses, colour blindness, amblyopia, or diagnosis of psychiatric/neurological conditions. Participants will be recruited through Vrije Universiteit. **Materials** ---------- Stimuli will be presented to the participants on a 22-inch Samsung SyncMaster2233 monitor in Psychopy 2 (Peirce et al., 2019) on a black and white background. A red and cyan checkerboard stimulus will be presented to the left eye and flickering at either 14Hz or 29Hz, depending on the trial block, while a green and magenta checkerboard stimulus will be presented to the right eye and flickering at either 17Hz or 34Hz. A mirror stereoscope (Geoscope Standard, Stereo Aids) will be used to present the stimuli to each individual eye. **Procedure** ---------- All participants are provided with consent forms, and explained the nature of the study. The researcher will then administer the EEG equipment, and set up the experimental paradigm. Once the EEG equipment has been set up, the participant will be asked to view the stimuli through the mirror stereoscope, as to familiarise themselves with binocular rivalry. These trials will last 2 minutes, with both groups of flicker frequencies being presented. Adjustments will be made subject to the participants needs, in regards to the spatial location of the stimuli, to make sure that binocular rivalry is fully induced. In the main experiment, the participant will undergo eight separate binocular rivalry trials, four trials per flicker frequency block. In the first four trials, the participant will be required to report perceptual switches within the trial. That is, press key '1' if they are primarily perceiving the left stimulus, and press key '3' if they are perceiving the right stimulus. Two of these trials will be 6 minutes, and two will be 1 minute long. The participant will then complete two long trials, each with slow or fast flicker frequencies. Within these trials, the participant will be asked to report 'mixed percepts' alone. Mixed percepts occur during binocular rivalry, where a 'blend' of the two stimuli appear in conscious awareness. These trials will be 6 minutes long. The reason for separately reporting mixed percepts is to make the task more manageable when stimuli change frequently. Finally, the participants will undergo two long (6min) trials (for each frequency block), where they are to passively view the stimuli with no active report. The order of these three blocks of trials will be counterbalanced for each participant. **EEG Data Acquisition and Preprocessing** ---------- EEG data will be collected via 64 electrodes, placed on the scalp using the 10/20 BioSemi ActiveTwo system, with a sampling rate of 512Hz. EEGlab in MATLAB (Delorme & Makeig, 2004) will be used to preprocess the data. The data will be re-referenced using the average of two electrodes placed on the participants' earlobes. The data will be high-pass filtered at 0.1Hz. Excessively noisy channels (Kurtosis Z-score threshold of 8) will be rejected and interpolated using the spherical method. No other preprocessing is necessary to remove artifacts such as blinks, as the RESS method suppresses artifacts (Cohen & Gulbinaite, 2017). **Analysis** ---------- The novel analysis technique within this study is necessary for two reasons. First, previous research has highlighted low SNR as an issue within BR paradigms using SSVEPs (Brown & Norcia, 1997), especially over longer time windows. This issue of low SNR is usually negated by averaging trials together. However, this is not possible as the phenomenology of separate BR trials undergo different time courses and would thus cancel out the ‘waxing and waning’ of the SSVEP signatures. Therefore, to provide researchers with more valuable trials, novel methods need to be explored that maximise SNR when using SSVEPs in BR studies. Second, although counterphase modulation of the SSVEP signatures have been demonstrably linked to the conscious awareness of stimuli in rivalry trials (e.g. Wang, Gao & Gao, 2004; Lansing, 1964), previous research has only illustrated this using single and representative trials. Moreover, there has not been a quantifiable way to establish the perceptual switch rate over many trials. We therefore want to contribute to the literature by applying a novel SSVEP extraction method (RESS; Cohen & Gulbinaite, 2017) to a BR paradigm, to investigate the effectiveness of this method in a no-report paradigm. **RESS** The RESS method circumvents the first issue (i.e., low SNR) by taking a combination of each electrode in the EEG signal to form a weighted timeseries at each specific response frequency. By doing so, this maximises the SNR, thus allowing more trials to be included in subsequent analyses. The RESS method entails using linear spatial filters to multiply the timeseries, and maximise the signal at the response frequency, while minimising noise around the response frequency. The timeseries can then be temporally filtered around the frequency of interest, facilitating subsequent analyses. **Turning Point Technique** We also aim to use a novel method in conjunction with the RESS SSVEP extraction method. Our novel method entails using the turning points of the SSVEP timeseries at the response frequencies as an objective indicator of perceptual switch rate experienced by each individual participant. Although this method does not provide any temporal accuracy regarding what the participant saw, it does circumvent the second issue regarding an inability to quantify perceptual switch rate over a large number of trials. By summating and averaging the number of turning points in the timeseries (i.e. gradient moves from positive to negative or vice-versa), we have the ability to collate multiple trials together. We therefore aim to investigate the correlation of the turning points with both a) the perceptual switch rate as reported by each participant, and b) the inferred perceptual switch rate in the no-report BR trial. We aim to investigate b, as previous research has demonstrated that dominance durations in BR have inter-trial consistency within participants (Patel et al., 2015). We therefore expect that perceptual switch rate will be similar in no-report and active report trials within this study. In addition to the above, we also aim to use the novel turning point technique in conjunction with the 'best-electrode approach' (BEA). The BEA is a classic SSVEP extraction technique, where the timeseries is used from the EEG electrode that displays the highest power at the response frequency (e.g. Fuchs et al., 2009). This is to investigate whether the RESS method is more effective at SSVEP extraction than the BEA. **Data Collection to Date** ---------- Pilot data have been collected. However, these will not be included within the study. No data for this specific study have been collected as of yet. **Timeline** ---------- Data collection will commence from the 14th of September, and last until the desired number of participants are collected. **Hypotheses** ---------- We hypothesise that the turning points of the SSVEP timeseries (at the response frequencies) will strongly correlate with the frequency of perceptual switches reported in the active report trials. Further to this, we hypothesise that the correlation will become stronger as a function of collating the data from separate trials together (for individual participants). We also hypothesise that the RESS method will yield higher SNR at the response frequencies than the BEA. Additionally, we hypothesise that the correlation between reported (and inferred perceptual switch rate) will be higher for turning points in timeseries extracted using the RESS method, in comparison to timeseries extracted using the BEA. Further to this, we hypothesise that the intermodulation frequencies (i.e. the summation of the two flicker frequencies presented) will provide valuable information about the quantity of mixed percepts perceived throughout the trial. We also plan to explore other novel methods for inferring mixed percepts from SSVEPs, since existing research has only moderate success in tracking piecemeal, mixed, or transitional rivalry. **References** Aru, J., Bachmann, T., Singer, W., & Melloni, L. (2012). Distilling the neural correlates of consciousness. Neuroscience & Biobehavioral Reviews, 36(2), 737-746. Brown, R. J., & Norcia, A. M. (1997). A method for investigating binocular rivalry in real-time with the steady-state VEP. Vision research, 37(17), 2401-2408. Cohen, M. X., & Gulbinaite, R. (2017). Rhythmic entrainment source separation: Optimizing analyses of neural responses to rhythmic sensory stimulation. Neuroimage, 147, 43-56. Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behavior research methods, 41(4), 1149-1160. Frith, C., Perry, R., & Lumer, E. (1999). The neural correlates of conscious experience: An experimental framework. Trends in cognitive sciences, 3(3), 105-114. Koch, C., Massimini, M., Boly, M., & Tononi, G. (2016). Neural correlates of consciousness: progress and problems. Nature Reviews Neuroscience, 17(5), 307-321. Tong, F., Meng, M., & Blake, R. (2006). Neural bases of binocular rivalry. Trends in cognitive sciences, 10(11), 502-511. Wheatstone, C. (1838). XVIII. Contributions to the physiology of vision.—Part the first. on some remarkable, and hitherto unobserved, phenomena of binocular vision. Philosophical transactions of the Royal Society of London, (128), 371-394. Zhang, P., Jamison, K., Engel, S., He, B., & He, S. (2011). Binocular rivalry requires visual attention. Neuron, 71(2), 362-369.