On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems

Abstract

Motor imagery (MI)-based brain-computer interface (BCI) systems detect electrical brain activity patterns through electroencephalogram (EEG) signals to forecast user intention while performing movement imagination tasks. As the microscopic details of individuals’ brains are directly shaped by their rich experiences, musicians can develop certain neurological characteristics, such as improved brain plasticity, following extensive musical training. Specifically, the advanced bimanual motor coordination that pianists exhibit means that they may interact more effectively with BCI systems than their non-musically trained counterparts; this could lead to personalized BCI strategies according to the users’ previously detected skills. This work assessed the performance of pianists as they interacted with an MI-based BCI system and compared it with that of a control group. The Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) machine learning algorithms were applied to the EEG signals for feature extraction and classification, respectively. The results revealed that the pianists achieved a higher level of BCI control by means of MI during the final trial (74.69%) compared to the control group (63.13%). The outcome indicates that musical training could enhance the performance of individuals using BCI systems.

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

“…In reference [ 124 ], the results indicated an 89.84% classification accuracy with the BCI-Competition IV data set. Similarly, a previous report [ 34 ] has indicated a 74.69% classification accuracy with EEG brain signals recorded from eight pianists. In addition to CSP, WT is a powerful tool that can be used to extract desired features in MI tasks.…”

“…Two additional studies [ 34 , 124 ] have examined the performance of CSP and LDA for feature extraction and classification, respectively. In reference [ 124 ], the results indicated an 89.84% classification accuracy with the BCI-Competition IV data set.…”

“…The model and learned parameters are then applied to unseen or new data to predict the class label of the new data [ 27 ]. Among different types of classification tasks, binary and multi-label classifications are widely used in clinical studies, and in studies of cognitive function, motor imagery (MI) processing, emotion recognition (ER), and brain disorders, including brain injury, attention disorders, and multiple sclerosis [ 31 , 32 , 33 , 34 ]. In addition to these recognized areas, various computational neural models have been considered, such as the medial prefrontal cortex (mPFC) and anterior cingulate cortex in modeling of learning predictions and monitoring behavior [ 35 ].…”

The Use of Brain–Computer Interfaces in Games Is Not Ready for the General Public

“…This couples with the fact that around 20% of the users are not proficient using a typical BCI. In fact, BCI illiteracy is an issue which is also well-established in the existing literature (e.g., Nijholt et al, 2009;Allison and Neuper, 2010;Marshall et al, 2013;Vasiljevic and Miranda, 2019), although the idea that physiological traits are responsible for BCI illiteracy is controverted (Thompson, 2019;Riquelme-Ros et al, 2020). These limitations impact replay and the difficulty of games, as players who do not succeed in a BCI task will become stuck in the game without knowing how to improve.…”

Sensor Signal and Information Processing III

“…As the microscopic details of individuals’ brains are directly shaped by their rich experiences, musicians can develop certain neurological characteristics, such as improved brain plasticity following extensive musical training. Riquelme-Ros et al [ 10 ] have developed a new approach to assess the performance of pianists as they interacted with an MI-based BCI system and compared it with that of a control group. The outcome indicates that musical training could enhance the performance of individuals using BCI systems.…”