**1. Introduction**

A brain-computer interface (BCI) system uses various techniques to recognize brain activity and transform this biological signal into a command that can be used by computer systems to complete certain tasks [1]. At the same time, it provides feedback to the user on how the intentions are being transformed into actions. In short, a BCI system transforms mental activity into a command that can affect the surroundings without the user making a physical effort. This command can be used for various applications, such as moving orthopedic prostheses through imagery [2]. Using a similar strategy, a recent work explored the use of noninvasive neuroimaging to enhance the control of a robotic device to complete daily tasks [3]. Similarly, BCIs are also used to synthesize speech by people who are unable to communicate due to neurological impairments [4]. BCIs have also been used to improve rehabilitation after a stroke, translating brain signals into the intended movements of a paralyzed limb [5] and, more recently, for the control through thought of applications for smart homes or robots within the Internet of Things (IoT) context [6,7].

It has been established that musical practice involves the activation of numerous areas of the brain that carry out different yet complementary functionalities to perform the complex task of playing a musical instrument, including reading a score, performing complex, highly specific movements, performing from memory, increased attention and concentration levels during performance, controlling the tuning of the instrument, and even improvisation. It is not unreasonable to conclude that musicians' brains have anatomical and functional differences compared to non-practitioners [8]. Therefore, the musician's brain offers a unique example with which to investigate what influence musical exercise can have on brain structures.

Of all the abilities musicians possess, their high motor coordination capacity is especially interesting. Engaging in a musical profession requires years of practice, with a great deal of time each day devoted to rehearsing specific and concrete movements involving the fine musculature of the hands or body. This undoubtedly effects a greater skill in handling the instrument, which is reflected in the individual's brain structures. The hand movements eventually display greater precision and coordination after the years of practice necessary to become a good musician.

Motor imagery, as a strategy to control a BCI system, has been investigated in groups of people with different characteristics, such as airplane pilots carrying out motor imagery tasks [9]. The present work aims to explore the handling of a BCI system through motor imagery by musicians, specifically pianists, for potential applications. It is hereby assumed that motor imagery control would be more intuitive for pianists and they would, therefore, achieve better performance, building on their previous muscle memory experience. Hence, this research aims to examine motor imagery in pianists to instantiate the benefits of musical education as well as show whether it is possible to consider a personalized BCI strategy for each subject according to their previously identified skills.

Following these introductory aspects, Section 2 outlines the prior work on the subject. Section 3 explains the experimental phase, detailing the characteristics of the test subjects, the resources used, and the method of data collection. The results are presented in Section 4. The work ends with the conclusions and future avenues for research in Section 5.

### **2. Music Training and Motor Imagery**

Musicians' brains have been studied extensively over the last few years as a textbook case of neuroscience research. Given that the main theme of this paper is to explore whether musical training (specifically in pianists) has an influence on the control of a BCI system through motor imagery, this section will present the previous work supporting this hypothesis.

Numerous examples in the literature have analyzed the neuroscientific foundations of music. The work of [10] presents an exhaustive review of how musical production and perception influence cognitive abilities, involving the areas of the auditory cortex and the motor cortex. Münte et al. [8] already examined the neuroanatomical peculiarities of musicians' brains, highlighting their greater neuroplasticity. Later works, such as [11], delved further into the preceding ideas. Indeed, learning to play an instrument is a highly complex task that involves the interaction of higher-order cognitive functions and leads to behavioral, structural, and functional changes in the brain. Consequently, due to the need to constantly engage in musical practice, multiple differences have been shown to appear in the following areas of musicians' brains:


it has been proven that this greater size is related to the intensity of musical training (number of hours practiced per day throughout life) as well as the fact of having initiated this training at an early age.

• Brain stem. This structure deals with basic sensory mechanisms. It has been possible to register faster reactions in musicians responding to certain musical and linguistic stimuli [15].

Taking the above into account, it can be stated that there is a positive correlation between the intensity and frequency of musical practice and the anatomical changes in the brain structures.

Musical performance is the complex activity responsible for these structural changes. It is considered extremely complex as it requires three special skills, i.e., the basic motor controls of coordination, sequencing, and spatial organization of movement [16]. Coordination refers to a good arrangement of the rhythmic aspect of music, while sequencing and spatial organization of movement imply the musician playing the notes on the instrument. It has been observed that more complex note sequences require the activity of structures such as the basal ganglia, dorsal premotor cortex, and cerebellum. The spatial organization of the different movements required to play an instrument involves the integration of different channels of spatial, sensory, and motor information, whereby the activation of the parietal, sensorimotor, and premotor cortex is observed.

In addition, audiomotor interactions occur in the brain when performing music as well as in the passive activity of listening to music. The premotor cortex is the link between the auditory system and the motor control, and for this activation to occur, the person must have an identified sound/action relationship [16].

It is evident that musical practice involves an increase in motor skills. In concrete studies focused on secondary motor areas, musicians show a much smaller activation area in these zones than non-musicians, demonstrating that pianists require smaller neural networks than non-musicians when it comes to motor skills, which in turn indicates that they are more efficient in controlling movements [17,18]. For example, practicing a complex fingering task for several months leads to an increase of approximately 25% in the primary motor area activation (M1). Furthermore, while musicians repeating the same sequence show a small area of activation (habituation) when a new music piece is trained for the first time, there is a larger area of activation (enhancement) [19]. During a fingering task performed by musicians and non-musicians, the former showed a rapid increase in the primary motor cortex (M1), while this was not seen in the latter [20].

Another study has shown that there are functional changes in the brains of children after 15 months of music training [10]. Two groups were studied, one receiving musical training and the other not. In the initial phase, the authors of such study found no differences between the groups. However, after the indicated period, it was found that the children who had been trained improved in motor control and melodic-rhythmic tasks, which supports the fact that the changes seen in adults (musicians) are due to musical practice.

As can be seen, intensive musical training leads not only to structural but also functional modifications in the youth's brain. However, these changes can also be induced in adults, thus preserving areas of gray and white substance [21].

With this, functional changes in the motor cortex occur. The motor cortex changes when performing simple piano exercises with five fingers and also increases the activity of the basal ganglia and the cerebellum. Most significantly, these changes take place either if the practice is performed physically or mentally [15]. In fact, the musician's brain is a paradigm of neuroplasticity [8].

Considering the above, it seems clear that musicians have greater motor coordination capacity than non-musicians, derived from intensive musical practice. However, the present study focuses on the performance of motor imagery. The question is whether undertaking music training in a mental (imagined) way can improve both motor coordination and the actual exercise. Indeed, using transcranial magnetic stimulation (TMS), Pascual-Leone et al. [22] demonstrated that the mere mental practice of an exercise in fingering a specific sequence for two hours a day over five days was sufficient to produce a certain reorganization of the motor cortex.

Subsequently, it was shown that the areas involved in motor imagery are approximately the same as those activated in real musical perception [23]. The same conclusion was drawn in another study [24] examining the MRI activity of seven pianists and seven participants with no musical experience. A few years earlier, [25] had indicated that many professional athletes and musicians can use movement imagery to improve their motor skills.

In the specific case of pianists, the most recent study by Zabielska-Mendyk et al. [26] compared the EEG patterns of pianists and non-pianists while executing both real and imagined fingering of different complexities. The power of the alpha and beta bands (mu rhythm modulation) decreased with decreasing fingering complexity (in both real and imagined cases), and this only occurred with the pianists; the non-musicians did not exhibit this attenuation. This capacity varies according to the experience in years that the musician accumulates, which is acquired progressively, as per [27]. This result already suggests a different behavior in terms of BCI performance.

Throughout these reviewed studies, it seems clear that circumstances may exist that improve the motor imagery skills of some individuals over those of others. However, how this advantage can lead to better performance when using a BCI system has thus far not been described. The presence of significant differences in this performance was investigated by Dobrea et al. [28], while it has been suggested that certain individual traits act as precursors in predicting performance in using a BCI system [29]; other predictors include spatial (motor) skills, which encompass the practice of a musical instrument.

Notably, some people appear to have no capacity to control a BCI, a phenomenon that numerous analysts in the literature have termed "BCI illiteracy" [30]. Hereby, it should be noted that BCIs are generally not easy to control, and even with proper instruction, some users cannot control their systems as desired. Nevertheless, BCI illiteracy is an inadequate concept for clarifying the trouble that users can have when working with BCI frameworks. First, it is a methodologically frail idea that depends on the imperfect assumption that BCI users have physiological or useful qualities that forestall capable performance during BCI use [31]. Second, the term BCI illiteracy invites a comparison between learning to use BCIs and spoken or written language acquisition. Hence, to avoid conceptual snares in terms of how BCI use may or may not relate to language learning, some researchers have chosen to use the term "BCI inefficiency".

Various aspects associated with music are widely used to control BCI systems. For example, Makeig et al. [32] set out to control a BCI system by recreating the emotions produced by different pieces of music. In other words, they sought to recognize the emotions generated by a melody. Up to 84% success in certain experiments was achieved with this method.

Next, as this article uses motor imagery to control a BCI system, it reviews the literature evaluating the performance achieved through this control strategy. In [30], the authors explored the different outcomes in motor imagery achieved with different participants, drawing a distinction between the variability among different participants and that among different states of the same participant. They covered previous works, comparing personal characteristics, psychological mood, and anatomical and physiological aspects, concluding that all these components are essential when discussing future BCI performance.

Randolph et al. [33] developed what has become one of the main works through which we delimit our study area and define our research hypothesis. The authors considered factors such as age, sex, playing sports, playing video games, taking psychiatric medication, and playing a musical instrument. They concluded that a series of personal characteristics influence the modulation of the mu rhythm, leading to better outcomes in the control of a BCI. Specifically, having motor dexterity of the hands leads to better control over a BCI device. Furthermore, they examined the characteristics of age, time spent typing per day, the performance of hand-arm movements, and whole-body movements. They found that both age and hand-arm movements correlate positively with the ability to modulate rhythm induced by both real and imagined movements. The possibility of doing sports or playing a musical instrument is implied within these movements.

A large proportion of motor training occurs when the brain anticipates a movement being executed, i.e., if substantial repetition occurs prior to a movement, cerebral training results in a wave anticipating the movement [34]. Overall, some previous studies have pointed to the importance of the chosen movement used to control the BCI system. For instance, [28] discussed different types of tasks, e.g., motor, mathematics, and linguistics skills, whereby the motor tasks included movements of the fingers of the hand (left/right) and arm (left/right), while Soriano et al. [35] reviewed the different imagined movements that have been used in BCI. In terms of specific results, [36] showed that the movements of the right hand generate a differentiated signal on the EEG and cause hemodynamic activity in the motor cortex of the left hemisphere, detected by fMRI. When such movements are imagined, they generate similar, but less stable, patterns [37].

Other movements that have been examined in this context include grasping with the hand [38], the general use of the fingers (fingering) [39], the use of the index finger [40], the use of the big toe [41], and the maximum contraction of the hand [37]. Soriano et al. [35] undertook a comparison of these movements. However, despite the wealth of previous research, there is no specific study analyzing the performance of BCI system control by professional pianists using their high-level skills with fingers and hands.

### **3. Methodology**

### *3.1. Sample Characteristics*

This work aims to investigate whether pianists can control a BCI system by means of motor imagination more efficiently than non-musicians. To this end, we conducted an experiment analyzing the BCI performances of a group of pianists and a group of non-pianists (generally non-musicians), which functioned as a control group. The characteristics of the participants in both groups were collected, such as their sex and age as well as other more specific features, such as musical practice, the practice of other activities that involve finger movement (typing, video games), and playing sports.

The sample size in this experiment was 8 individuals, with 4 in each group. During the experiment, all participants were duly informed of how it would be conducted (passive and non-invasive measurement) as well as how the collected data would be handled. Furthermore, the anonymity of their personal and EEG data was guaranteed at all times. All experimentation was conducted in accordance with the Declaration of Helsinki and the ethics committees of the involved institutions were asked for approval before the sessions began.

In the case of the pianist group, participants were sought who had at least 10 years of musical training. Both men and women were included, some of whom were still in the process of musical training. The group rehearsed for an average of 5.75 h a day, ensuring significant skill in motor coordination. The characteristics of this group are summarized in Table 1.


Various factors were considered in the creation of the control group (non-pianists). First, the study avoided including people in this group who had some musical ability, either with the piano or another instrument. Second, as revealed in the previous literature, some factors can increase the performance in using BCI management systems, such as the practice of tasks that involve digital motor coordination (video games, typing, other professions that require precision motor skills, etc.) and the practice of sports that result in a substantial improvement in motor coordination. Therefore, an attempt was made to choose participants in such a way as to minimize these aspects. Table 2 summarizes the characteristics of the control group.


**Table 2.** Description of the control group.

The non-pianists in the control group were asked about their musical knowledge and musical practice. Those who volunteered for the control group and showed some musical knowledge and/or practiced with musical instruments of any kind (piano or other) were not selected. Likewise, those who practiced a sport at an almost professional level were not selected. Furthermore, all members of the control group had a normal level of digital activity that did not go beyond one or two hours a day spent on work-related typing on a computer keyboard and all practiced sport only sporadically.

The time that the participants in the control group spent on motor practice is contrasted with that of the pianists, as these all began their piano studies at around 10 years of age and had had professional careers lasting between 10 and 15 years. Their beginning at an early age and their years of practice was estimated to translate to about 6 h of daily finger practice (more on some days). This amount undoubtedly exceeded that of the control group, not only in terms of motor coordination but also long-term musical orientation, which has been shown to create characteristic brain structures. In the case of the pianist group, the hours spent on other fine motor coordination practices were also collected, but these values were very close to those of the control group and were also irrelevant compared to the hours of musical practice.
