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. 2020 Aug 10;20(16):4452.
doi: 10.3390/s20164452.

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

José-Vicente Riquelme-Ros  1 , Germán Rodríguez-Bermúdez  2 , Ignacio Rodríguez-Rodríguez  3 , José-Víctor Rodríguez  4 , José-María Molina-García-Pardo  4
Affiliations

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

José-Vicente Riquelme-Ros et al. Sensors (Basel). .

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.

Keywords: brain-computer interface; internet of things; machine learning; motor imagery; pianists.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Electrodes location.
Figure 2
Figure 2
Experiment deployment.
Figure 3
Figure 3
Timing of the brain-computer interface (BCI) System.
Figure 4
Figure 4
Offline results of the group of pianists in the third session. Measurements in Run 2 and 3 were taken from feedback trials.
Figure 5
Figure 5
Offline results of the non-pianist group in the third session. Measurements in Run 2 and 3 were taken from feedback trials.
Figure 6
Figure 6
Comparison of offline results in both groups. Measurements in Run 2 and 3 were taken from feedback trials.
Figure 7
Figure 7
Examples of Projected electroencephalogram (EEG) signal after a Common Spatial Pattern (CSP) filter for Left and Right MI of Pianist 3 and Non-Pianist 2. Subfigures (a) and (b) belong to Pianist 3 (left/right, respectively), and subfigures (c) and (d) belong to Non-pianist 2 (left/right, respectively).
See this image and copyright information in PMC

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