Georgi Tsenov

Neural Networks for Online Classification of Hand and Finger Movements Using Surface EMG signals

Myoelectric signals (MES) are the electrical manifestation of muscular contractions and they can be used to create myoelectric prosthesis which is able to function with the amputees muscle movements. This signal recorded at the surface of the skin of the forearm has been exploited to provide recognition of the movement of the hand and finger movements of healthy subject. The objective of the paper is to describe the identification procedure, based on EMG patterns of forearm activity using various neural networks methods and to make a comparison between different intelligent computational methods of identification, which are used in this work. Then an online algorithm for movement identification and classification that utilises the trained neural networks is presented

Approximation Formula for Easy Calculation of Signal-to-Noise Ratio of Sigma-Delta Modulators

The signal-to-noise ratio (SNR) is one of the most significant measures of performance of the sigma-delta modulators. An approximate formula for calculation of signal-to-noise ratio of an arbitrary sigma-delta modulator (SDM) has been proposed. Our approach for signal-to-noise ratio computation does not require modulator modeling and simulation. The proposed formula is compared with SNR calculations based on output bitstream obtained by simulations, and the reasons for small discrepancies are explained. The proposed approach is suitable for fast and precise signal-to-noise ratio computation. It is very useful in the modulator design stage, where multiple performance estimates are required.

Speech recognition using neural networks

This paper presents investigation on speech recognition classification performance when using different standard neural networks structures as a classifier. Those cases include usage of a Feed-forward Neural Network (NN) with back propagation algorithm and a Radial Basis Functions (RBF) Neural Network.

Non-intrusive sleep analyzer for real time detection of sleep anomalies

Solutions for caring for the elderly both efficacious and cost-effective are given by Ambient Assisted Living (AAL) systems that combine the research fields of intelligent systems and communication technologies. These systems are promising for the improvement of the quality of life of elderly and disabled people. One important characteristic of health and well-being is sleep. While sleep quantity is directly measurable, its quality has traditionally been assessed with subjective methods such as questionnaires. In this paper, we propose a non-intrusive sleep analyzer for real time detection of sleep anomalies, part of an effective AAL system. The proposed solution is based on combination of non-invasive sensors and an algorithm for sleep analysis with two stages - low and high level reasoning. It also offers the opportunity to include third party devices. Using the analyzer we can monitor basic sleep behavior and to detect sleep anomalies, which can serve as an important indicator for both mental and physical health.

Implementation of a feed-forward Artificial Neural Network in VHDL on FPGA

Describing an Artificial Neural Network (ANN) using VHDL allows a further implementation of such a system on FPGA. Indeed, the principal point of using FPGA for ANNs is flexibility that gives it an advantage toward other systems like ASICS which are entirely dedicated to one unique architecture and allowance to parallel programming, which is inherent to ANN calculation system and one of their advantages. Usually FPGAs do not have unlimited logical resources integrated in a single package and this limitation forcesrequirement for optimizations for the design in order to have the best efficiency in terms of speed and resource consumption. This paper deals with the VHDL designing problems which can be encountered when trying to describe and implement such ANNs on FPGAs.

A Design procedure for stable high order, high performance sigma-delta modulator loopfilters

In this paper we present the ideas for design of stable high order single bit sigma-delta modulator loopfilter transfer functions that provide high signal-to-noise ratio. The procedure is backed up with example and results made for third order loopfilter sigma-delta modulators and give the performance impact on them when varying the poles of the noise transfer function, when using optimized zeroes. This loopfilter function design is computed with fast theoretical calculation of the signal to noise ratio with mathematical formula, instead of approximation based on simulations and combined with theory that presents approximated value for modulator’s maximal stable DC input signal, resulting in design without the need of simulations of the modulator output bitstream.

Application of neural networks, PCA and feature extraction for prediction of nucleotide sequences by using genomic signals

Converting symbolic sequences into complex genomic signals reveals surprising regularities of genomes, both locally and at a global scale. This approach allows using signal processing methods for the handling and analysis of nucleotide sequences, specifically for the prediction of nucleotides when knowing the preceding ones in the sequence. In this paper we propose both Feature Extraction (FE) and Principal Component Analysis (PCA) as methods to efficiently extract the major features of a genomic signal, using then a neural network to predict the next sample in the sequence.

Nonlinear Programming Approach for Design of High Performance Sigma–Delta Modulators

In this chapter we present a nonlinear programming approach to the design of third-order sigma–delta modulators with respect to maximization of the signal-to-noise ratio, taking into account the modulator’s stability. The proposed approach uses an analytic formula for calculation of the signal-to-noise ratio and an analytic formula for stability of the modulator. Thus the goal function becomes maximization of the signal-to-noise ratio and constraints come from stability issues and bounds of the modulator noise transfer function coefficients. The results are compared with the optimal third-order modulator design provided by DStoolbox. The proposed procedure has low computation requirements. It is described for third-order modulators with one real pole of the loop filter transfer function and can be extended easily and generalized to higher-order modulators.

Humanoid robot control with EEG brainwaves

This paper presents the implementation on a control of humanoid robot with human brainwaves as control input. This control technique is done with real-time measurements of the brain activity with electroencephalography and recognition of the signal types. The recognized signals are sent as control input to a custom build humanoid robot. The presented hardware of the robot combines main controller, 17 servo motors and a servo controller, while the brain activity signals are measured with Emotiv Epoc+Electroencephalography (EEG) headset.

Combined EEG and EMG fatigue measurement framework with application to hybrid brain-computer interface

In recent years, the EEG-based brain-computer interface (BCI) has become one of the most promising areas of research in computer science and robotics. Many internationally renewned research teams combining engineers and doctors, experts in neuroscience are trying to develop useful applications and devices offering disabled people to lead a normal life. Useful BCIs for disabled people suffering from Cerebral palsy, Parkinsons disease, Brain injury, Spinal cord injuries, Multiple sclerosis, Stroke, Post-polio syndrome should allow them to use all their existing brain and muscle abilities as control possibilities. In this paper we present a framework based on the mutimodal fusion approach of the users electromyographic (EMG) and electroencephalographic (EEG) activities in a so called “Hybrid-BCI” (hBCI). Although EEG BCI alone yields good performance as already proved in many research papers, it is outperformed by the fusion of EEG and EMG. We investigate the influence of muscular fatigue on the EMG performance. Such a framework will allow a more reliable control and adaptation of the hBCI if the user get exhausted and loses concentration during the rehabilitation process. We focus our research in aims of improving the lives of many upper limb disabled individuals through a combination of current BCI technologies with existing assistive medical systems.