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Brain-computer interface for control of electro-hydraulic servo drive

Wos P., Dindorf R.

Journal of Machine Engineering

2018

Vol. 18, No. 4

86--95

The aim of the study was to perform bioelectric signal analysis focusing on its applicability to control of the electro-hydraulic servo drive. The natural bioelectric signals generated by brain, facial muscles and eye muscles read by the NIA (Neural Impulse Actuator) are translated into control commands in the controller of electro-hydraulic servo drive. Bioelectric signals detected by means of special forehead band with three sensors are sent to the actuator box, where they are interpreted as control signals. The test stand was constructed to control of the electro-hydraulic servo drive by means of bioelectric signals generated by the operator. The control signals from the actuator box are transmitted via a wireless network to the controller of electro-hydraulic positioning drive.

Electromyography and mechanomyography signal recognition: Experimental analysis using multi-way array decomposition methods

Wołczowski A., Zdunek R.

Biocybernetics and Biomedical Engineering

2017

Vol. 37, no. 1

103--113

In this study, we considered the problem of controlling a prosthetic hand with noisy electromyography (EMG) and mechanomyography (MMG) signals. Several dimensionality reduction methods were analyzed to assess their efficiency at classifying these signals, which were registered during the performance of grasping movements with various objects. Using the cross-validation technique, we compared various dimensionality reduction methods, such as principal components analysis, nonnegative matrix factorization, and some tensor decomposition models. The experimental results demonstrated that the high-est classification accuracy (exceeding 95% for all subjects when classifying 11 grasping movements) and lowest computational complexity were obtained when higher-order singular value decomposition was applied to a multi-way array of multi-channel spectrograms, where the temporal EMG/MMG signals from all channels were concatenated.

Wearable biosensing: signal processing and communication architectures issues

Celka P., Vetter R., Renevey P., Verjus C., Neuman V., Luprano J., Decotignie J. D., Piguet C.

Journal of Telecommunications and Information Technology

2005

nr 4

90-104

Long-term monitoring of human vital signs is becoming one of the most important fields of research of biomedical engineering. In order to achieve weeks to months of monitoring, new strategies for sensing, conditioning, processing and communication have to be developed. Several strategies are emerging and show different possible architectures. This paper essentially focuses on issues in wearable biosignal processing and communication architecture currently running at the Swiss Center for Electronics and Microtechnology (CSEM) in the framework of several European projects.