# MuseStudio: Brain Activity Data Management Library for Low-Cost EEG Devices
###### Abstract  
Collecting data allows researchers to store and analyze important information about activities, events, and situations. Gathering this information can also help us make decisions, control processes, and analyze what happens and when it happens. In fact, a scientific investigation is the way scientists use the scientific method to collect the data and evidence that they plan to analyze. Neuroscience and other related activities are set to collect their own big datasets, but to exploit their full potential, we need ways to standardize, integrate, and synthesize diverse types of data. Although the use of low-cost ElectroEncephaloGraphy (EEG) devices has increased, such as those whose price is below 300 USD, their role in neuroscience research activities has not been well supported; there are weaknesses in collecting the data and information. The primary objective of this paper was to describe a tool for data management and visualization, called MuseStudio, for low-cost devices; specifically, our tool is related to the Muse brain-sensing headband, a personal meditation assistant with additional possibilities. MuseStudio was developed in Python following the best practices in data analysis and is fully compatible with the Brain Imaging Data Structure (BIDS), which specifies how brain data must be managed. Our open-source tool can import and export data from Muse devices and allows viewing real-time brain data, and the BIDS exporting capabilities can be successfully validated following the available guidelines. Moreover, these and other functional and nonfunctional features were validated by involving five experts as validators through the DESMET method, and a latency analysis was also performed and discussed. The results of these validation activities were successful at collecting and managing electroencephalogram data. brain data; low-cost devices; EEG; BIDS; neuroscience; library +
Footnote †: journal: Medical  
processing are some disciplines that require these kinds of products, especially those that feature dry sensors, knowing that some of them are relatively inexpensive. Regardless of the field of study or preference for defining data (quantitative, qualitative), accurate data collection is essential to maintain the integrity of research. Both the selection of appropriate data collection instruments (existing, modified, or newly developed) and clearly delineated instructions for their correct use reduce the likelihood of errors occurring. One of the main contributions is the compatibility with the Brain Imaging Data Structure (BIDS) [2] standard, which facilitates research activities related to the use of EEG devices. This standard allows researchers to organize and share the data associated with studies carried out in their laboratories. However, some EEG devices available on the market are not compatible with the BIDS. This issue makes managing recordings, sessions, and users a very difficult and inconvenient task. The majority of low-cost EEG devices have this limitation, and even though they are compatible with proprietary software for brain activity, the features included are limited and not very flexible [3]. In this context, we used a low-cost EEG device, known as Interaxon Muse 2 [4] (Muse and Muse S devices are also compatible). The manufacturer offered an SDK with computer support in the past (which was never compatible with Muse 2 and Muse S). However, it was deprecated, and currently, there is no viable alternative to use the devices in a research or professional environment. This only enables their connection to the original smartphone app, which is limited to guided meditation, and not intended for experiments. To overcome the imposed limitations, we developed a Python library, called MuseStudio [5], that allows managing brain activity data from users with several sessions, including other helpful characteristics.