Wavelet analysis is often very effective because it provides a simple approach for dealing with local aspects of a signal. Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classi...

bci is one of the most intriguing technologies among other hci systems, mostly because of its capability of recording brain activities. spelling bcis, which help paralyzed people to maintain communication, are one of the striking topics in the field of bci. in this scientific a spelling bci system with high transfer rate and accuracy that uses ssvep signals is proposed. in addition, we suggeste...

Surface electromyogram pattern recognition (EMG-PR) has been considered as a promising approach for predicting amputees’ motion intentions to control myoelectric prostheses. However, EMG recordings are mostly contaminated by various interferences, which decay the prediction of EMG-PR methods, thus affecting performance prosthesis. One common way solve this issue is improve quality signals via f...

Improving EEG signal interpretation, specificity, and sensitivity is a primary focus of many current investigations, and the successful application of EEG signal processing methods requires a detailed knowledge of both the topography and frequency spectra of low-amplitude, high-frequency craniofacial EMG. This information remains limited in clinical research, and as such, there is no known reli...

An electromygraphic (EMG) Silent Speech Interface is a system which recognizes speech by capturing the electric potentials of the human articulatory muscles, thus enabling the user to communicate silently. This study is concerned with introducing an EMG recording system based on multi-channel electrode arrays. We first present our new system and introduce a method to deal with undertraining eff...

EMG-based gesture recognition shows promise for human–machine interaction. Systems are often afflicted by signal and electrode variability which degrades performance over time. We present an end-to-end system combating this variability using a large-area, high-density sensor array and a robust classification algorithm. EMG electrodes are fabricated on a flexible substrate and interfaced to a cu...

Wavelets are a powerful tool for biomedical signal processing. Wavelets are used for the processing of signals that are non-stationary and time varying. The EMG signal contains transient signals related to muscle activity. EMG signals have typically many transient components, which are very interesting to isolate and classify according to their physiological significance. Wavelet based denoisin...

For researchers in electromyography (EMG), and similar biosginals, signal processing is naturally an essential topic. There are a number of excellent tools available. To these one may add the freely available open source statistical software package R, which is in fact also a programming language. It is becoming one of the standard tools for scientists to visualize and process data. A large num...