P20. Disentangling the effect of pre-stimulus oscillatory power on visual detection using spatio-spectral decomposition and heterogeneous choice models

Introduction Analyzing the influence of pre-stimulus EEG/MEG-oscillations on stimulus detection is typically performed by calculating the oscillatory power in each individual EEG/MEG sensor and a single frequency band and correlating it to the parameters of standard Signal Detection Theory (SDT). In the visual domain, this classical approach revealed an effect of widely distributed alpha activity on the perception bias (i.e., how conservative or liberal subjects decide) but not on the overall sensitivity (i.e., the ability to discriminate between stimulus presence and absence). However, sensor-space EEG/MEG data is spatially non-specific and highly susceptible to far-field activity and noise sources. Moreover, classical equal-variances SDT analysis might lead to spurious results. Here, we extract sources of alpha and theta activity using spatio-spectral decomposition (SSD) and model their collective effect on visual detection effectivity using heterogeneous choice models. Methods 64-multichannel EEG of 30 subjects was recorded during a visual yes/no detection task with 60% stimulus present trials and 40% stimulus absent trials. The stimuli consisted of low-contrast Gabor patches displayed at 10 degrees of visual angle in the left or right hemifield. The two strongest alpha and theta sources common for all subjects were identified by SSD applied at group-level. The rank-normalized alpha and theta powers at stimulus presentation ( t  = 0 ms) were used as regressors in a heterogeneous choice model. Results The heterogeneous choice model replicated the finding that oscillatory alpha power influences the perceptual bias. Additionally, strong occipital alpha power was found to increase the variability in the detection model. Furthermore, the power of a bipolar theta source, tentatively attributable to a deeper origin, was found to be negatively correlated to the overall sensitivity of the visual detection task. Discussion and significance SSD effectively extracted collectively strongest sources of alpha and theta activity across subjects, hereby increasing the spatial specificity of the data and the signal-to-noise ratio. The heterogeneous choice model revealed that alpha oscillations not only influence perceptual bias but also the variability in the model and that strong regional theta oscillations are correlated with less effective visual detection, potentially due to drowsiness. However, further studies are needed to characterize the neurophysiological underpinnings of these observations.