Alpha and beta oscillatory activity during a sequence of two movements

OBJECTIVE: We studied movement-related electroencephalographic oscillatory changes in the alpha and beta range during a sequence of two movements in 7 healthy volunteers, in order to investigate the relationship between these changes and each component in the sequence. METHODS: The sequence consisted of a wrist active extension-passive flexion followed by a first and second finger pincer. A total of 10.5 s sweeps were recorded using the level of surface electromyographic (EMG) activity in wrist extensors as trigger, including a 7.5 s pre-stimulus. The sweeps were also realigned manually offline using as trigger the end of the first EMG burst, or the beginning of the second movement. An index of the changes in non-phase-locked energy in the 7-37 Hz range was obtained by averaging single-sweep time-frequency transforms. RESULTS: The duration of each of the movements in the sequence and the relationship between them were compatible with the use of two different motor programmes in the sequence. In the beta band, a decrease in energy (event-related desynchronisation, ERD) began 1.5 s before the onset of the first movement, and was sustained until the end of the second movement. No energy increases were observed until the end of the second movement. In the alpha band, the ERD began 0.5 seconds before the first movement and was sustained throughout the recording. CONCLUSION: These findings suggest that the beta-event-related synchronisation is related to the end of the whole motor process, and not to the end of each motor programme

Sliding window averaging in normal and pathological motor unit action potential trains

Objective: To evaluate the performance of a recently proposed motor unit action potential (MUAP) averaging method based on a sliding window, and compare it with relevant published methods in normal and pathological muscles. Methods: Three versions of the method (with different window lengths) were compared to three relevant published methods in terms of signal analysis-based merit figures and MUAP waveform parameters used in the clinical practice. 218 MUAP trains recorded from normal, myopathic, subacute neurogenic and chronic neurogenic muscles were analysed. Percentage scores of the cases in which the methods obtained the best performance or a performance not significantly worse than the best were computed. Results: For signal processing figures of merit, the three versions of the new method performed better (with scores of 100, 86.6 and 66.7%) than the other three methods (66.7, 25 and 0%, respectively). In terms of MUAP waveform parameters, the new method also performed better (100, 95.8 and 91.7%) than the other methods (83.3, 37.5 and 25%). Conclusions: For the types of normal and pathological muscle studied, the sliding window approach extracted more accurate and reliable MUAP curves than other existing methods. Significance: The new method can be of service in quantitative EMG

sEMG Wavelet-based Indices predicts Muscle Power Loss during Dynamic Contractions.

Purpose: To compare the sensitivity to estimate acute exercise-induced changes on muscle power output during a dynamic fatiguing protocol from new surface electromyography (sEMG) indices based on the discrete wavelet transform, as well as from amplitude and spectral indices of muscle fatigue (i.e. mean average voltage, median frequency and ratios between spectral moments). Methods: 15 trained subjects performed 5 sets consisting of 10 leg press, with 2 minutes rest between sets. sEMG was recorded from vastus medialis (VM) muscle. Several surface electromyographic parameters were computed. These were: mean average voltage (MAV), median spectral frequency (Fmed), Dimitrov spectral index of muscle fatigue (FInsm5), as well as other five parameters obtained from the discrete wavelet transform (DWT) as ratios between different scales. Results: The new wavelet indices as a single parameter predictor accounted for 46.6% of the performance variance of changes in muscle power and the log FInsm5 and MAV as a two factor combination predictor accounted for 49.8%. On the other hand, they showed the highest robustness in presence of additive white Gaussian noise for different signal to noise ratios (SNRs). Conclusions: The sEMG wavelet indices proposed may be a useful tool to map changes in muscle power output during dynamic high-loading fatiguing task

Métodos de procesamiento y análisis de señales electromiográficas

La electromiografía clínica es una metodología de registro y análisis de la actividad bioeléctrica del músculo esquelético orientada al diagnóstico de las enfermedades neuromusculares. Las posibilidades de aplicación y el rendimiento diagnóstico de la electromiografía han evolucionado paralelamente al conocimiento de las propiedades de la energía eléctrica y al desarrollo de la tecnología eléctrica y electrónica. A mediados del siglo XX se introdujo el primer equipo comercial de electromiografía para uso médico basado en circuitos electrónicos analógicos. El desarrollo posterior de la tecnología digital ha permitido disponer de sistemas controlados por microprocesadores cada vez más fiables y potentes para captar, representar, almacenar, analizar y clasificar las señales mioeléctricas. Es esperable que el avance de las nuevas tecnologías de la información y la comunicación pueda conducir en un futuro próximo a la aplicación de desarrollos de inteligencia artificial que faciliten la clasificación automática de señales así como sistemas expertos de apoyo al diagnóstico electromiográfico.Clinical electromyography is a methodology for recording and analysing the bioelectrical activity of the skeletal muscle tissue in order to diagnose neuromuscular pathology. The possibilities of application and the diagnostic performance of electromyography have evolved parallel to a growing understanding of the properties of electricity and the development of electrical and electronic technology. The first commercially available electromyography equipment for medical use was introduced in the middle of the 20th century. It was based on analog electronic circuits. The subsequent development of digital technology made available more powerful and accurate systems, controlled by microprocessors, for recording, displaying, storing, analysing, and classifying the myoelectric signals. In the near future, it is likely that advances in the new information and communication technologies could result in the application of artificial intelligence systems to the automatic classification of signals as well as expert systems for electromyographic diagnosis support

Métodos de procesamiento y análisis de señales electromiográficas

La electromiografía clínica es una metodología de registro y análisis de la actividad bioeléctrica del músculo esquelético orientada al diagnóstico de las enfermedades neuromusculares. Las posibilidades de aplicación y el rendimiento diagnóstico de la electromiografía han evolucionado paralelamente al conocimiento de las propiedades de la energía eléctrica y al desarrollo de la tecnología eléctrica y electrónica. A mediados del siglo XX se introdujo el primer equipo comercial de electromiografía para uso médico basado en circuitos electrónicos analógicos. El desarrollo posterior de la tecnología digital ha permitido disponer de sistemas controlados por microprocesadores cada vez más fiables y potentes para captar, representar, almacenar, analizar y clasificar las señales mioeléctricas. Es esperable que el avance de las nuevas tecnologías de la información y la comunicación pueda conducir en un futuro próximo a la aplicación de desarrollos de inteligencia artificial que faciliten la clasificación automática de señales así como sistemas expertos de apoyo al diagnóstico electromiográfico.Clinical electromyography is a methodology for recording and analysing the bioelectrical activity of the skeletal muscle tissue in order to diagnose neuromuscular pathology. The possibilities of application and the diagnostic performance of electromyography have evolved parallel to a growing understanding of the properties of electricity and the development of electrical and electronic technology. The first commercially available electromyography equipment for medical use was introduced in the middle of the 20th century. It was based on analog electronic circuits. The subsequent development of digital technology made available more powerful and accurate systems, controlled by microprocessors, for recording, displaying, storing, analysing, and classifying the myoelectric signals. In the near future, it is likely that advances in the new information and communication technologies could result in the application of artificial intelligence systems to the automatic classification of signals as well as expert systems for electromyographic diagnosis support