Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography.

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.

Gamma-Band Frequency Analysis and Motor Development in Music-Trained Children: A Cross-Sectional Study.

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.

Alpha and beta cortical activity during guitar playing: task complexity and audiovisual stimulus analysis.

PURPOSE: Some studies have explored the relationship between music and cortical activities; however, there are just few studies investigating guitar performance associated with different sensory stimuli. Our aim was to evaluate alpha and beta activity during guitar playing. MATERIALS AND METHOD: Twenty healthy right-handed people participated in this study. Cortical activity was measured by electroencephalogram (EEG) during rest and 4 tasks (1: easy music with an auditory stimulus; 2: easy music with an audiovisual stimulus; 3: complex music with an auditory stimulus; 4: complex music with an audiovisual stimulus). The peak frequency (PF), median frequency (MF) and root mean square (RMS) of alpha and beta EEG signals were assessed. RESULTS: A higher alpha PF at the T3-P3 was observed, and this difference was higher between rest and task 3, rest and task 4, tasks 1 and 3, and tasks 1 and 4. For beta waves, a higher PF was observed at C4-P4 and a higher RMS at C3-C4 and O1-O2. At C4-P4, differences between rest and tasks 2 and 4 were observed. The RMS of beta waves at C3-C4 presented differences between rest and task 3 and at O1-O2 between rest and task 2 and 4. CONCLUSION: The action observation of audiovisual stimuli while playing guitar can increase beta wave activity in the somatosensory and motor cortexes; and increase in the alpha activity in the somatosensory and auditory cortexes and increase in the beta activity in the bilateral visual cortexes during complex music execution, regardless of the stimulus type received. Abbreviations: bpm: beats per minute; C: central; EEG: electroencephalogram; F: frontal; Hz: hertz; LABCOM: Laboratory of Motor Control and Biomechanics; MD: mean difference; MF: median frequency; O: occipital; P: parietal; PF: peak frequency; R: rest; RMS: root mean square; T: temporal; T1: task 1; T2: task 2; T3: task 3; T4: task 4; UFTM: Federal University of Triângulo Mineiro.