Aims: nowadays, commercial lightweight electroencephalography (EEG) headsets such as Emotiv EPOC and Muse are gaining popularity in the neuroscience community, mostly in research that monitors natural behaviour outside traditional laboratory settings. These devices often utilize only a few dry or active electrodes in specific locations and signal quality is often inferior compared to their traditional counterparts equipped with electrode caps that require conductive paste for proper functioning. In this study, we wanted to assess the feasibility of a portable, dry electrode-based EEG headset manufactured by MindRove for laboratory use.Methods: we implemented three paradigms for acquiring visual evoked potential (VEP), P300 event related potential and motor imagery (MI) related cortical patterns. Measurements were taken using the MindRove device, with a wet electrode system (mBrainTrain Smarting) applied as reference. The performance of the two devices were assessed using traditional signal quality measures (e.g. signal-to-noise ratio) for VEP and P300. Since motor imagery patterns are not trivial to quantify and MI is often utilized in brain–computer interfaces (BCIs) that employ machine learning algorithms, neural network-based and support vector machine-based classifiers were fit to the two MI databases to benchmark the usability of the MindRove headset for use in BCI solutions.Results: based on preliminary research involving eight subjects, signals recorded using the two systems were comparable, with the wet electrode device performing only slightly better than the more easily applicable, easy-to-use commercial headset.Conclusions: the application of the portable MindRove device is feasible for use in research besides qualitative investigations.