# MuseStudio: Brain Activity Data Management Library for Low-Cost EEG Devices
## 4 Validation
### Analyzing the Main Features of MuseStudio  
Using as the input a demonstration of MuseStudio, the evaluation of this library was carried out using DESMET [38]. This is a set of techniques applicable to evaluating both software engineering methods and tools. We used the method based on a qualitative case study, which describes a feature-based evaluation. Following the guidelines specified for this technique, an initial list of features that a library or tool for EEG data management should provide was defined (see Table 1). These features were established by two experts (full professors) in cognitive neuropsychology from the University of Castilla-La Mancha (UCLM). As can be observed, some of the features are directly related to the availability of the BIDS. DESMET was deployed by involving five experts. First, two experts were asked about the main requirements a library for low-cost EEG devices should provide. Second, another three experts were involved to validate MuseStudio by considering the previously proposed requirements. All the experts were professionals with knowledge and skills related to EEG devices, neuroscience, and psychology. Once Table 1 has been filled in by the experts, DESMET determines the importance degree that should be assigned to each identified feature. Specifically, the importance degrees are Mandatory (M), Highly Desirable (HD), Desirable (D), and Nice to have (N). This importance was also established by the consulted experts. By using these importance degrees, Table 2 was filled in. As can be noticed, the most important functional and nonfunctional requirements to be supported are signal visualization, import and export data management, and scalability. Afterwards, according to DESMET, a scale to evaluate each of the described features should be provided. The scale proposed by DESMET (see Table 3) was applied to evaluate each feature according to the following factors: Conformance Acceptability Threshold (CAT) and Conformance score obtained (CSO) for MuseStudio. In particular, three experts (associate professors) from the University of Castilla-La Mancha with experience in the fields emotion recognition, health psychology, and signal processing/computer science agreed about the values of CSOi. \begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline
**Feature** & **Description** \\ \hline Signal visualization & The tool should be able to provide graphic visualization of the associated signals to each sensor of the headband and differentiate among them. \\ \hline Session management (import) & The tool has to store or import the associated data of each session and use and differentiate among them. \\ \hline Session management (export) & The tool has to allow sharing stored data, that is the tool should be able to export the stored data of each session and user. \\ \hline User control & The tool has to provide user control during a session. For instance, the graphical visualization of EEG signals should be stopped and restarted. \\ \hline Scenario identification & The tool should be able to identify rare scenarios, for instance a poorly worn headband. \\ \hline Easy of data reviewing & The stored data of each session should be easy to review and manage. \\ \hline Consistency & The stored data of each session and user should be jointly managed. \\ \hline Real time (same time) & The tool must allow following a session in real time, including minimal latency to improve the performance. \\ \hline At a distance (different place) & The tool must allow following a session at a distance, so that the user/headband can be in different places and the data visualization can be performed in different places. \\ \hline Scalability Guided user interface & The tool must allow using several headbands simultaneously with different users. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of features for MuseStudio’s evaluation.