The susceptibility of electroencephalography (EEG) signal to artifacts is considered a major obstacle preventing the deployment of relevant non-invasive neurotechnology. In spite of a large body of literature dedicated to the identification, rejection and removal of artifactual components in EEG, the study of the impact that different artifacts may have on the EEG signal properties has been mostly qualitative and focused on the source (e.g. muscle activity, electromagnetic interference) rather than the function generating them. This work takes advantage of a unique dataset where EEG of 12 participants elicited during the execution of 9 common human activities (e.g., speaking, blinking, etc.) is co-registered with electromyography (EMG), electrooculography (EOG), accelerometer and gyroscope sensors, and baselined to "resting" (artifact-free) intervals to allow an exact, quantified assessment of the impact of artifacts. We examine several metrics capturing different facets of the influence of artifacts on EEG and measure the extent to which a state-of-the-art artifact removal method is able to eliminate them. In addition to an in-depth, quantified profiling of functional EEG artifacts, our work provides valuable information for precisely tuning the hyper-parameters of artifact rejection and removal algorithms and for designing realistic brain-computer interface (BCI) applications.