Advanced neuroimaging techniques, particularly functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), have opened up new opportunities for exploring the neural basis of aesthetic processing. EEG, due to its excellent temporal resolution, allows researchers to track brain activity as it unfolds in milliseconds, which is not possible with fMRI. Most existing EEG-based neuroaesthetics research has focused on event-related potential (ERP) analysis that relies on trial-averaged EEG signal waveforms, or time-frequency analysis that examines the brain's oscillatory activity in different frequency bands [8; 9; 10; 11]. These methods provide information about individual electrodes or regions only and are not suitable for detecting cooperation between different brain regions. Consequently, there is growing interest in brain connectivity networks that have the potential to offer a more comprehensive view of how different brain regions interact during the perception and appreciation of art.  
Brain connectivity can be categorized into structural, functional and effective connectivity [12; 13; 14; 15; 16; 17]. Structural connectivity pertains to the physical wiring of the brain, such as white matter tracts connecting different regions [17; 18], which can be studied using techniques like diffusion tensor imaging (DTI). Functional connectivity refers to the temporal correlations between neural activities in different brain areas in the resting state or during task execution [14; 15; 16; 17], and is computed using techniques like M/EEG and fMRI. Effective connectivity, on the other hand, involves extracting the causal relationships between brain regions and how one area influences another's activity [14; 15; 17; 18].  
In this study, we report on whether and how sensor space functional connectivity analysis can help in uncovering and eventually understanding how different brain regions work together to process artistic stimuli. Our analysis is based on phase-based connectivity techniques that are closely related to brain oscillations (fhythmic electrical activity in the brain) [19; 20; 21; 22], and offer distinct advantages over traditional methods such as amplitude correlation-based connectivity. Oscillations play a critical role in inter-regional communication within the brain, with different frequency bands associated with various cognitive functions [23; 24; 25]. Phase-based methods rely on phase synchronization information across time or trials, and express the presence of connections with measures such as the phase locking value (PLV) [19] or the phase lag index (PLI) [26] that are less influenced by volume conduction, a common issue in EEG where signals from different regions can interfere with each other due to their overlapping electromagnetic fields [19; 22; 26; 27; 28; 29; 30; 31].  
This exploratory research aims to identify differences in brain connectivity when subjects engage with abstract versus figurative paintings. Using phase-based connectivity analyses with the debiased weighted phase lag index (dwPLI) [28] as the calculation method, this study seeks to uncover the neural communication patterns linked to the perception, recognition and processing of different artistic styles. The distinction between figurative art, that depicts recognizable real-world objects or scenes, and abstract art, that focuses on shapes, colors and forms, often without specific references to real objects, provides a unique opportunity to investigate how the brain processes different types of visual artistic stimuli.