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Motor imagery-based brain computer interface (BCI) technology has motor rehabilitation as one of its main fields of application. The use of a BCI as a neuroprosthetic for paralyzed limb motor restoration implies normally absence of muscle activity. It is still an open question whether residual motor activity in healthy individuals or in patients causes a bias in the modulation of a motor imagery-based...
This article describes a patient training evolution to achieve myoelectric control in the remnant limb. The methodology is divided in two main stages: (i) the training procedure, where the patient is submitted to a protocol for educating the muscles of the remnant limb, and (ii) myoelectric signal acquisition and analysis, in order to measure the patient evolution to reach different contraction levels...
Most previous studies of electromyography (EMG) pattern recognition control of multifunctional myoelectric prostheses adopted a conventional sampling rate that is commonly used in EMG research fields. However, it is unknown whether using a lower sampling rate in EMG acquisition still preserves sufficient neural control information for accurate classification of user movement intents. This study investigated...
Recent studies on identifying multiple activation states from mechanomyogram (MMG) signals for the purpose of controlling switching interfaces have employed pattern recognition methods where MMG signal features from multiple muscle sites are extracted and classified. The purpose of this study is to determine if MMG signal features retain enough discriminatory information to allow reliable continuous...
To evaluate the proficiency level of an operating myoelectric hand, we proposed an evaluation index consisting of the accuracy and the reproducibility of electromyography (EMG) signal patterns. Our proposed method is not an absolute evaluation because we use bio-signals, so it is necessary to verify the correlation between the proposed index and performance evaluation to confirm the usefulness of...
Understanding the relationship between neural activity in motor cortex and muscle activity during movements is important both for basic science and for the design of neural prostheses. While there has been significant work in decoding muscle EMG from neural data, decoders often require many parameters which make the analysis susceptible to overfitting, which reduces generalizability and makes the...
The acquisition of myoelectric signals from the Musculus deltoideus of a rhesus monkey is described. Such signals are aimed to be used as control signal for an active myoelectric hand prosthesis. For recording, implantable flexible, polyimide-based multi-site microelectrodes were placed epimysially on the muscle. EMG signals were recorded during voluntary goal-directed movements of the arm, and analyzed...
Myoelectric control of active prostheses requires electrode systems that are easy to apply for daily repositioning of the electrodes by the user. In this study we propose the use of Smart Fabric and Interactive Textile (SFIT) systems as an alternative solution for recording high-density EMG signals for myoelectric control. A sleeve covering the upper and lower arm, which contains 100 electrodes arranged...
Skeletal muscle force can be estimated using surface electromyographic (sEMG) . Usually, the surface location for the sensors is near the respective muscle motor unit points. Skeletal muscles generate a spatial EMG signal, which causes cross talk between different sEMG signal sensors. In this study, an array of three sEMG sensors is used to capture the information of muscle dynamics in terms of sEMG...
The paper proposes a method to estimate wrist kinematics from surface EMG signals for proportional and simultaneous control of multiple degrees of freedom (DOFs). The approach is based on the concurrent detection of surface EMG signals from forearm muscles and hand kinematics of both limbs during mirrored bilateral movements in free space which involve the simultaneous activation of wrist flexion/extension,...
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