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Abstract
Brain-Computer Interface systems based on motor imagery are able to identify an individual's intent to initiate control through the classification of encephalography patterns. Correctly classifying such patterns is instrumental and strongly depends in a robust machine learning block that is able to properly process the features extracted from a subject's encephalograms. The main objective of this work is to provide an overall view on machine learning stages, aiming to answer the following question: "What are the steps in the classification process that we should worry about?". The obtained results suggest that future research in the field should focus on two main aspects: exploring techniques for dimensionality reduction, in particular, supervised linear approaches, and evaluating adequate validation schemes to allow a more precise interpretation of results.

Abstract
This paper looks over the perpetual fight that low density regions must face in what regards the emergence of economic opportunities, employment, income and production growth. It is known that tourism demand for nature and protected areas is increasing in this more and more urban world, what can open some doors for innovation to take place. The project we’ve been working on is focused on sustainable tourism practices in a protected area (Malcata Mountain Reserve), via electric mobility, which is known for its zero emission, non-polluting and noise free travelling. A broader study is taking place under the Interreg Funding Program, known as Moveletur Project. Our goals are to sponsor a model of sustainable and green tourism for visitors of natural areas, to produce a network of green tourism itineraries linking places of natural and/or cultural value using rechargeable vehicles and to encourage tourism sector entrepreneurs with a new service for their activity. Combined work with other natural areas is essential to increase outcomes. After the conclusion of project (by the end of 2018) there will be better information concerning natural and cultural values that natural areas hold and that can be used for tourists’ pleasure, there will be a more dutiful way of doing tourism in natural areas, expectantly it will address job creation and improved territorial competitiveness, tourism experiences will have more quality and, finally, using technological features, will support smart villages further development.

Abstract
The construction of terraces, in the Douro Region, with earthen embankments rises aset of problems related to the hydrological processes. The main objective of this study is the evaluation of the electrical resistivity spatial variation in agriculture terraces atDouro valley (Portugal).To achieve this objective two variables are analysed, the soil electrical resistivity andthe flow direction algorithms. In field survey, 13 electrical resistivity profiles wererecorded. The contributing area calculated with the algorithms D (DeterminiscInfinity Flow) and MFD (Multiple Flow Direction), are the base of the internal runoffmodelling, both supported by the digital elevation model with a special resolution of1m2.Was stablished a correlation between the spatial variation of the soil electrical resistivityrepresented by standard deviation electrical resistivity of each profile and the averagevalue of contributing area coincident with each profile.The electrical resistivity standard deviation seems to be moderately good correlated forthe D algorithm at about 1m of depth and has a good correlaon at 1,5m to 2m ofdepth with the MFD algorithm. The results analysis shows a significant positivestatistical correlation between electrical resistivity standard deviation and thecontributing areas (MFD and D) depending on the soil depths.