RESUMO
Eliminating facial electromyographic (EMG) signal from the electroencephalogram (EEG) is crucial for the accuracy of applications such as brain computer interfaces (BCIs) and brain functionality measurement. Facial electromyography typically corrupts the electroencephalogram. Although it is possible to find in the literature a number of multi-channel approaches for filtering corrupted EEG, studies employing single-channel approaches are scarce. In this context, this study proposed a single-channel method for attenuating facial EMG noise from contaminated EEG. The architecture of the method allows for the evaluation and incorporation of multiple decomposition and adaptive filtering techniques. The decomposition method was responsible for generating EEG or EMG reference signals for the adaptive filtering stage. In this study, the decomposition techniques CiSSA, EMD, EEMD, EMD-PCA, SSA, and Wavelet were evaluated. The adaptive filtering methods RLS, Wiener, LMS, and NLMS were investigated. A time and frequency domain set of features were estimated from experimental signals to evaluate the performance of the single channel method. This set of characteristics permitted the characterization of the contamination of distinct facial muscles, namely Masseter, Frontalis, Zygomatic, Orbicularis Oris, and Orbicularis Oculi. Data were collected from ten healthy subjects executing an experimental protocol that introduced the necessary variability to evaluate the filtering performance. The largest level of contamination was produced by the Masseter muscle, as determined by statistical analysis of the set of features and visualization of topological maps. Regarding the decomposition method, the SSA method allowed for the generation of more suitable reference signals, whereas the RLS and NLMS methods were more suitable when the reference signal was derived from the EEG. In addition, the LMS and RLS methods were more appropriate when the reference signal was the EMG. This study has a number of practical implications, including the use of filtering techniques to reduce EEG contamination caused by the activation of facial muscles required by distinct types of studies. All the developed code, including examples, is available to facilitate a more accurate reproduction and improvement of the results of this study.
RESUMO
Background: The aim of this study was to analyze the gamma-band frequency and motor performance of children with and without music training.Methods: This cross-sectional study included 31 right-handed children, 6-11 years old, who were allocated to two groups: 1) the music group (MG), including children who attended preschool and musical training (n = 16), and 2) the no-music group (NMG), including children who attended preschool but received no additional music training (n = 15). The outcomes were gamma-band frequency measured by electroencephalography, manual dexterity, aim-and-catch, and static and dynamic balance abilities measured by the Movement Assessment Battery for Children, and fine motor skills, overall motor skills, balance, corporal body scheme, spatial organization, temporal orientation, and general motor quotient (GMQ) by a Brazilian scale for motor development.Results: There 1was a significant difference between groups in the peak frequency (p = 0.0195) and median frequency (p = 0.0070) in the F3-F4 regions. Static and dynamic balance (p = 0.03), temporal orientation (p < 0.01), and GMQ (p < 0.03) were higher in MG than in NMG.Conclusion: The musically trained children had increased gamma-peak frequency in the frontal region and greater temporal orientation, balance, and the overall motor quotient.
