Optimal parameters for rapid (invisible) frequency tagging using MEG.

Frequency tagging has been demonstrated to be a useful tool for identifying representational-specific neuronal activity in the auditory and visual domains. However, the slow flicker (<30 Hz) applied in conventional frequency tagging studies is highly visible and might entrain endogenous neuronal oscillations. Hence, stimulation at faster frequencies that is much less visible and does not interfere with endogenous brain oscillatory activity is a promising new tool. In this study, we set out to examine the optimal stimulation parameters of rapid frequency tagging (RFT/RIFT) with magnetoencephalography (MEG) by quantifying the effects of stimulation frequency, size and position of the flickering patch. Rapid frequency tagging using flickers above 50 Hz results in almost invisible stimulation which does not interfere with slower endogenous oscillations; however, the signal is weaker as compared to tagging at slower frequencies so certainty over the optimal parameters of stimulation delivery are crucial. The here presented results examining the frequency range between 60 Hz and 96 Hz suggest that RFT induces brain responses with decreasing strength up to about 84 Hz. In addition, even at the smallest flicker patch (2°) focally presented RFT induces a significant and measurable oscillatory brain signal (steady state visual evoked potential/field, SSVEP/F) at the stimulation frequency (66 Hz); however, the elicited response increases with patch size. While focal RFT presentation elicits the strongest response, off-centre presentations do generally mainly elicit a measureable response if presented below the horizontal midline. Importantly, the results also revealed considerable individual differences in the neuronal responses to RFT stimulation. Finally, we discuss the comparison of oscillatory measures (coherence and power) and sensor types (planar gradiometers and magnetometers) in order to achieve optimal outcomes. Based on our extensive findings we set forward concrete recommendations for using rapid frequency tagging in human cognitive neuroscience investigations.