In the context of EEG/MEG, the term 'volume conduction (VC) effects' refers to the recording of an instantaneous linear mixture of multiple brain source activities by each EEG/MEG channel. VC effects may lead to the detection of spurious functional/effective couplings among EEG/MEG channels that are not caused by brain interactions. It is of importance to determine which detected couplings are indicators of brain interactions and which originate from the VC artefacts. In this paper, a quantitative framework is proposed to explore the origin of detected channel couplings by using two types of surrogate datasets. Also, a sensitivity index (called SI) is proposed to compare the power of different connectivity measures to discriminate between the brain interactions and the instantaneous linear mixing effects. We use seven different functional connectivity estimators to evaluate our method on simulation models and resting state EEG data. The error rate of the proposed framework for simulation data by using each of the connectivity estimators is less than 5.2%. Also, SI ranks these connectivity estimators according to their sensitivity to brain interactions in the presence of VC artefacts. As expected, the connectivity measures which are theoretically robust to VC artefacts yield high SI in simulation models and EEG data. In addition, for EEG data in the alpha frequency band the reproducible functional couplings which are indicators of brain interactions are in the back-front directions. This is consistent with the previous studies in this field.