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1

Classifying Various EMG and EOG Artifacts in EEG Signals

Aydemir O., Pourzare S., Kayikcioglu T.

Przegląd Elektrotechniczny

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2012

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R. 88, nr 11a

218-222

EN

EEG is the most popular potential non-invasive interface, mainly due to its fine temporal resolution, ease of use, portability and low set-up cost. However, it has some limitations. The main limitation is that EEG is frequently contaminated by various artifacts. In this paper, a novel approach to classify various electromyography and electrooculography artifacts in EEG signals is presented. EEG signals were acquired at the Department of Electrical and Electronics Engineering Karadeniz Technical University from three healthy human subjects in age groups between 28 and 30 years old and on two different days. Extracted feature vectors based on root mean square, polynomial fitting and Hjorth descriptors were classified by k-nearest neighbor algorithm. The proposed method was successfully applied to the data sets and achieved an average classification rate of 94% on the test data.

PL

W artykule przedstawiono nową metodę analizy sygnałów w technice EEG pod względem klasyfikacji błędów zakłóceniowych w wynikach badań elektromiografii i elektrookulografii. Badanie przeprowadzone zostało na podstawie rzeczywistych wyników EEG.

2

A New Hybrid Feature Extraction Method for Partial Discharge Signals Classification

Liao R., Wang K., Yang L., Duan L., Li J.

Przegląd Elektrotechniczny

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2012

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R. 88, nr 11a

191-195

EN

In this paper, a new hybrid feature extraction method combining adaptive optimal radially Gaussian kernel (AORGK) time-frequency representation with two dimensional nonnegative matrix factorization (2DNMF) is proposed for partial discharge (PD) classification. Firstly, AORGK is applied to obtain the time-frequency matrices of PD ultra-high-frequency (UHF) signals. Then 2DNMF is employed to compress the AORGK amplitude (AORGKA) matrices to extract various feature vectors with different (d1, d2) combinations, i.e. (5, 5), (5, 10), (10, 5) and (10, 10). Finally, the extracted features are classified by fuzzy k nearest neighbor (FkNN) classifier and back propagation neural network (BPNN). 600 samples sam pled from four typical artificial defect models in Laboratory are adopting for testing of the proposed feature extraction algorithm. It is shown that the successful rate by FkNN and BPNN are all higher than 80%, and FkNN has superior classification accuracies than BPNN under four circumstances of (d1, d2) combinations. In addition, FkNN achieves the highest classification accuracy 93.73% with (10, 5) combination. The results demonstrate that it is feasible to apply the proposed algorithm to PD signal classification.

PL

W artykule przedstawiono nową hybrydową metodę klasyfikacji wyładowań niezupełnych (ang. Partial Discharge), wykorzystującą algorytm AORGK (ang. Adaptive Optimal Radially-Gaussian Kernel) o nieujemnej, matrycowej faktoryzacji dwuwymiarowej (ang. 2-Dimensional Nonnegative Matrix Factorization). W metodzie wykorzystano także algorytm k najbliższych sąsiadów oparty na teorii zbiorów rozmytych (ang. Fuzzy k Nearest Neighbour Classifier) oraz sieci neuronowe (ang. Back Propagation Neural Network).

3

Personal identification using retina

Choraś R.S.

Journal of Medical Informatics & Technologies

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2009

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Vol. 13

53--58

EN

This paper proposes a biometric system for authentication that uses the retina blood vessel pattern. The retina biometric analyzes the layer of blood vessels located at the back of the eye. The blood vessels at the back of the eye have a unique pattern, from eye to eye and person to person. The retina, a layer of blood vessels located at the back of the eye, forms an identity card for the individual under investigation. In particular retinal recognition creates an ”eye signature” from its vascular configuration and its artificial duplication is thought to be virtually impossible.

4

A note on Töeplitz matrix-based model in biometrics

Saeed K.

Journal of Medical Informatics & Technologies

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2008

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Vol. 12

29--31

EN

This paper presents a summary of the work presented as an invited paper at MIT 2008 International Conference. The work comprises a general note on the problems we meet in our everyday contact with biometrics and their different systems. A particular attention is paid to the anti-spoofing approaches in having a safe and convenient system of human verification for personal identification. A conclusion is drawn that neither stand-alone nor multi-system Biometrics are ideal and convenient to people for their daily necessity of being identified. The author suggests a system that may seem practical in banks and cash machines, for example, in which a biometric system is used (fingerprint or face identification for example) in conjunction with the popular means of account securing, the PIN code.

5

Robust texture classification using wavelet frames

Acharyya M., Kundu M.

Image Processing & Communications

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1999

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Vol. 5, no 2

19-37

EN

In this paper we present an approach to characterize textures at multiple scales using wavelet transforms and discuss the issues of translational and rotational invariance and noise immunity of a texture analysis system. We employ the non-separable discrete wavelet frames analysis which gives an overcomplete wavelet decomposition. Discrete Wavelet Frame (DWF) decompose the textures into a set of frequency channels. A texture is characterized by a set of these channel variances in this work. Classification experiments using twenty Brodazt textures indicate that texture signatures based on wavelet frame analysis are beneficial for accomplishing subtle discrimination of textures and robust classification against rotation translation and noise.