This paper proposes the design of a rehabilitation robot for upper limb based on physiological electrical signals. The system uses surface electromyography (sEMG) and electroencephalography (EEG) signals to control robot's movements. has modular structure consisting mechanical hand, system, data acquisition system. STM32 microcontroller Bluetooth module real-time transmission control. collects ...

One of the important issues in designing a brain-computer interface system is to select the type of mental activity to be imagined. In some of these systems, mental activity varies with user intent and action that must be controlled by the brain-computer system, and in a number of other signals, the received signals contain the same activity-related mental activity that should be performed by t...

Electroencephalography (EEG) signals reƒect activities on certain brain areas. E‚ective classi€cation of time-varying EEG signals is still challenging. First, EEG signal processing and feature engineering are time-consuming and highly rely on expert knowledge. In addition, most existing studies focus on domain-speci€c classi€cation algorithms which may not be applicable to other domains. Moreov...

An EEG acquisition system using 0.18μm technology is proposed. EEG input signals are of the range of few micro-voltage. Hence a high gain operational transconductance amplifier with gain of 62.45dB is designed for the acquisition system. The proposed amplifier and multiplexer have been tested for the EEG signals and their performance is reported at operating voltage of 1.8 volts. This design ca...

Electroencephalography (EEG) is one fundamental tool for functional brain imaging. EEG signals tend to be represented by a vector or a matrix to facilitate data processing and analysis with generally understood methodologies like time-series analysis, spectral analysis and matrix decomposition. Indeed, EEG signals are often naturally born with more than two modes of time and space, and they can...

EEG signals contain information directly related to cognitive activity. This paper presents a method classify the images person imagines via provided by signals. The relating objects ‘tree’, ‘house’, ‘plane’ and ‘dog’ have been reconstructed. We used convolutional neural networks obtain reconstruction of genetic algorithm find parameters this network. results obtained evaluated means Chebychev ...