## 1 Introduction
The fundamental steps that a BCI system follows are:  
* measuring electrical, magnetic activity or any other form of physical manifestations of the oscillating activity of the brain;
* interpretating acquired signals;
* transposing signals into viable commands for a computer or various other devices (robotic structures, wheelchair, bionic elements, brain-to-vehicle). In other words, the brain-computer interface translates users' intentions, which have an impact on the encephalographic signal, into control signals for various external computerized devices [25]. The biggest advantage of BCI systems is that there is no need to activate peripheral muscles or nerve structures, and the applications are very diverse [26; 27; 28; 29; 30], in areas such as wheelchair control [31; 32; 33], internet browsing [34; 35], games [36] and brain-to-vehicle [15; 37]. Nowadays, researchers have begun to develop completely different types of BCI systems, which, although at a theoretical stage, do not seem impossible to implement. These new systems are a significant part of the development of artificial intelligence. The fundamental idea is to load all the functions of the brain into a robotic assembly [38; 39], giving it the ability to be autonomous (to make decisions in the field of the space of random variables encountered). The proposed investigation is conducted through experimental studies, which propose improved stimuli and tasks on the interests and preferences of users in a BCI architecture based on motor imagery. A comparison with a second method is made of the cognitive mental states of the users, aiming at the subsequent improvement of the control within a BCI. Paradigms investigate brain fluctuations induced by various stimuli and activities to provide the means to detect significant neural information in brain activity that is critical to a BCI-like application. While the first paradigm considers sensorimotor rhythms, the second paradigm is based on Event-Related Potentials (ERPs). Most BCI paradigms consider the temporal information or spectral information of the activity generated by the brain, but rarely is the research performed as a whole, simultaneously considering the analysis of the time-frequency domain. As will be seen in this paper, the analysis that considers a single domain could be suboptimal because brain activity presents additional information that is visible in both the temporal and spectral domains. Therefore, this article deals with methodological improvements that include complementary information, obtaining a more accurate analysis of data that exceeds the performance of most available methods. BCI systems have a wide range of applications from quite simple to complex. There are BCI applications for spelling, simple computer games, wheelchair control, internet browsing or in a virtual environment [40; 41]. Usually, applications are specially built for a certain type of BCI, being part of an integrated system. BCI systems that can connect and effectively control other devices are quite rare. An increasing number of systems allow the control of more sophisticated devices, including orthoses, prostheses, drones, robotic arms or even mobile robots. BCI systems can also be used for neurohabilitation [42; 43; 44; 45]. In this article, we propose a new approach to a BCI system based on the use of filter benches to reduce noise using UWT wavelet multiresolution analysis, the development of a generalized detector using ICA and \(\mathrm{r}^{2}\) algorithms to extract features and optimize the detection test and, for classification, we use a hybrid neural network consisting of RBF and MLP-RNN. We also combine the two paradigms used in BCI based on five motor imagery tasks and cognitive mental states tasks, which can be determined by simultaneously analyzing the alpha and beta waves used in video games. This approach has a unique character according to the mentioned literature.