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1

Evaluation of filters over different stimulation models in evoked potentials

Güven Aysegül, Batbat Turgay

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 2

339--349

EN

Filtering is a key process which removes unwanted parts of signals. During signal recording, various forms of noises distort data. Physiological signals are highly noise sensitive and to evaluate them powerful filtering approaches must be applied. The aim of this study is to compare modern filtering approaches on scalp signals. Brain activities were generally examined by brain signals like EEG and evoked potentials (EP). In this study, data were recorded from university students whose age between 18 and 25 years with visual and auditory stimuli. Discrete wavelet transforms, singular spectrum analysis, empirical mode decomposition and discrete Fourier transform based filters were used and compared with raw data on classification performance. Higuchi fractal dimension and entropy features were extracted from EEG; P300 features were extracted from EP signals. Classification was applied with support vector machines. All filtered data gave better scores than raw data. Empirical mode decomposition (EMD) and Fourier-based filter yielded lower results than the discrete wavelet-based filter. Singular spectrum analysis gave the best result at 84.32%. The current study suggests that singular spectrum analysis removes noise from sensitive physiological signals, and EMD requires new mode selection procedures before resynthesizing.

2

Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework

Tripathy R. K., Rajendra Acharya U.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 4

890--902

EN

Sleep is a physiological activity and human body restores itself from various diseases during sleep. It is necessary to get sufficient amount of sleep to have sound physiological and mental health. Nowadays, due to our present hectic lifestyle, the amount of sound sleep is reduced. It is very difficult to decipher the various stages of sleep manually. Hence, an automated systemmay be useful to detect the different stages of sleep. This paper presents a novel method for the classification of sleep stages based on RR-time series and electroencephalogram (EEG) signal. The method uses iterative filtering (IF) based multiresolution analysis approach for the decomposition of RR-time series into intrinsic mode functions (IMFs). The delta (d), theta (u), alpha (a), beta (b) and gamma (g) waves are evaluated from EEG signal using band-pass filtering. The recurrence quantification analysis (RQA) and dispersion entropy (DE) based features are evaluated from the IMFs of RR-time series. The dispersion entropy and the variance features are evaluated from the different bands of EEG signal. The RR-time series features and the EEG features coupled with the deep neural network (DNN) are used for the classification of sleep stages. The simulation results demonstrate that our proposed method has achieved an average accuracy of 85.51%, 94.03% and 95.71% for the classification of 'sleep vs wake', 'light sleep vs deep sleep' and 'rapid eye movement (REM) vs non-rapid eye movement (NREM)' sleep stages.

3

A Detailed Study of EEG based Brain Computer Interface

Sharma G., Sharma N., Singh T., Agrawal R.

Annals of Computer Science and Information Systems

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2018

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Vol. 14

137--143

EN

Brain Computer Interface (BCI) generate a direct method to communicate with the outside world. Many patients are not able to communicate. For example:- the patient who are suffered with the several disease like post stroke - the process of thinking, remembering \& recognizing can be challenging. Because of spinal cord injuries or brain stem stroke the patient loss the monitoring power. EEG based brain computer interface (BCI) feature is beneficial to scale the brain movement \& convert them into a instruction for monitoring. In this paper our objective is to study about various applications of EEG based signal of the different disease like spinal cord injury, post stroke and ALS (amyotrophic lateral sclerosis) etc.

4

Zastosowanie sieci neuronowej opartej na parametrach charakterystyki widma sygnału EEG w celu rozróżnienia ruchu i zamiaru ruchu oraz przynależności personalnej sygnału

Broniec A.

Acta Bio-Optica et Informatica Medica. Inżynieria Biomedyczna

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2009

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Vol. 15, nr 3

244-247

PL

Badanie zmian zachodzących w sygnale elektroencefalograficznym (EEG) pod wpływem zadanego bodźca wymaga założenia, że ta reakcja ma własną i powtarzalną charakterystykę, w przeciwieństwie do ciągłej, spontanicznej aktywności mózgu, która w tym kontekście może być traktowana jako addytywny szum. Na podstawie powyższego założenia podzielono otrzymane w eksperymencie próbki sygnału EEG na grupy danych, związanych z odpowiedzią mózgu na zadane bodźce. Artykuł przedstawia wyniki otrzymane po zastosowaniu prostej sieci neuronowej LVQ (learning vector quantization) do rozróżnienia otrzymanych w eksperymencie danych. Porównano dane związane z ruchem palca i z wyobrażeniem ruchu palcem oraz przeprowadzono rozróżnienie danych otrzymanych od różnych osób. Zastosowanie sieci neuronowej samoorganizującej LVQ, opartej na parametrach charakterystyki widma EEG, pozwoliło na rozróżnienie przynależności personalnej sygnału EEG pomiędzy dwiema osobami ze średnią skutecznością 87,31% oraz pomiędzy czterema osobami ze średnią skutecznością 77,39%.

EN

The evaluation of changes within electroencephalography signal (EEG) occurred in response to stimuli, requires the assumption that this reaction has its own, repeatable characteristics compare to the continuous spontaneous brain activity, which can be treated in this context as the additive noise. Therefore, the recorded EEG signal samples, were divided into data groups connected with brain response to the stimuli. The simple neural network LVQ (learning vector quantization) was applied to evaluate recorded data. Movement of finger and voluntary intention of movement were examined. The application of simple neural network LVQ based on parameters of EEG-signal spectrum characteristics allowed for differentiation of EEG signal between two persons with the average efficiency of 87.31% and between four persons with the 77.39% accuracy.