Brain-computer interfaces are recently attracting more and more investments from the technological and scientific community. However, the path toward a daily-life system has still several obstacles. Many effort have been made to properly decode neural activity and understand users intentions by mainly focusing on processing algorithms. Nonetheless, it was rightfully assumed that brain activity could be properly recorded. Although this is true for clinical setups, in which wet electrodes (with conductive gels) are employed, data quality is a major issue in wearable brain-computer interfaces targeting daily-life applications. In such a case, dry electrodes are typically considered for user-friendliness, and this poses some issues in terms of setup stability. To overcome this limits, a proper setup must be adopted. Electroencephalography is generally exploited to guarantee low cost, wearability, and portability of the setup. Nevertheless, setup stability must be guaranteed as well as robust processing algorithms must be developed in order to detect and remove unavoidable artifacts. This thesis project thus aims to overcome these metrological issues in order to reach high data quality for further analyses.