There is a growing interest in utilising novel signal sources such as EEG (Electroencephalography) in multimedia research. When using such signals, subtle limitations are often not readily apparent without significant domain expertise. Multimedia research outputs incorporating EEG signals can fail to be replicated when only minor modifications have been made to an experiment or seemingly unimportant (or unstated) details are changed. This can lead to overoptimistic or overpessimistic viewpoints on the potential real-world utility of these signals in multimedia research activities. This paper describes an EEG/MM dataset and presents a summary of distilled experiences and knowledge gained during the preparation (and utilisiation) of the dataset that supported a collaborative neural-image labelling benchmarking task. The goal of this task was to collaboratively identify machine learning approaches that would support the use of EEG signals in areas such as image labelling and multimedia modeling or retrieval. The contributions of this paper can be listed thus; a template experimental paradigm is proposed (along with datasets and a baseline system) upon which researchers can explore multimedia image labelling using a brain-computer interface, learnings regarding commonly encountered issues (and useful signals) when conducting research that utilises EEG in multimedia contexts are provided, and finally insights are shared on how an EEG dataset was used to support a collaborative neural-image labelling benchmarking task and the valuable experiences gained.