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1

Outlier detection in EEG signals

Duraj Agnieszka, Chomątek Łukasz

Przegląd Elektrotechniczny

|

2023

|

R. 99, nr 1

237--240

EN

In this paper, the topic of detection of outliers in EEG signals was discussed, which facilitates making decisions about the diagnosis of a patient based on this study. We used two methods to detect outliers: the support vector machine and the k nearest neighbors method. The experiments were performed on a publicly available dataset containing EEG test results for 500 patients. The obtained results showed that the methods we used allow for the outlier detection efficiency at the level of 93%.

PL

W niniejszej pracy podjęto temat detekcji wyjątków w sygnałach EEG, co pozwala na ułatwienie podejmowania decyzji co do diagnozy pacjenta na podstawie tego badania. Do detekcji wyjątków wykorzystaliśmy dwie metody: maszynę wektorów nośnych i metodę k najblizszych sąsiadów. Eksperymenty zostały przeprowadzone na ogólnodostępnym zbiorze danych zawieraj ącym wyniki badania EEG dla 500 pacjentów. Uzyskane wyniki pokazały, że u żyte przez nas metody pozwalają na uzyskanie skuteczności detekcji wyjątków na poziomie 93%.

2

Ocular artifact elimination from electroencephalography signals: A systematic review

Ranjan Rakesh, Chandra Sahana Bikash, Kumar Bhandari Ashish

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 3

960--996

EN

Electroencephalography (EEG) is the signal of intrigue that has immense application in the clinical diagnosis of various neurological, psychiatric, psychological, psychophysiological, and neurocognitive disorders. It is significantly crucial in neural communication, brain-computer interface, and other practical tasks. EEG signal is exceptionally susceptible to artifacts, which are external noise signals originated from non-cerebral regions. The interference of artifacts in EEG signals can potentially affect the original recorded EEG signal quality and pattern. Therefore, artifact removal from EEG signal is critically important before applying it to a specific task for accurate outcomes. Researchers have proposed numerous techniques to remove various artifacts present in the contaminated EEG signal. However, neither optimum method nor criterion stands standard for endorsement of clinically recorded EEG signals. Therefore, the research related to artifact elimination from EEG signal is challenging and perplexing task. This paper attempts to give an extensive outline of the advancement in methodologies to eliminate one of the most common artifacts, i.e., ocular artifact. It is anticipated that the study will enlighten the researchers on all the existing ocular artifact elimination techniques with a validated simulation model on the recorded EEG signal. In future advancements, Standard norms in artifact elimination techniques are expected to diminish the neurologist’s load by substantiating the clinical diagnosis after gaining correct information from artifact-free EEG signals.

3

Schizophrenia detection using Multivariate Empirical Mode Decomposition and entropy measures from multichannel EEG signal

Krishnan Palani Thanaraj, Joseph Raj Alex Noel, Balasubramanian Parvathavarthini, Chen Yuanzhu

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

1124--1139

EN

Multivariate analysis of the EEG signal for the detection of Schizophrenia condition is proposed here. Multivariate Empirical Mode Decomposition (MEMD) is used to decompose the EEG signal into Intrinsic Mode Functions (IMF) signal. The randomness measure of the IMF signal is determined by computing the entropy of the signal. Five entropy measures such as Approximate entropy, Sample entropy, Permutation entropy, Spectral entropy, and Singular Value Decomposition entropy are measured from the IMF signal. These entropy measures showed a significant difference ( p < 0.01) between the healthy controls (HC) and Schizophrenia (SZ) subjects. Many state-of-the-art (SoA) machine learning classifiers are trained on the feature matrix obtained from entropy values of the IMF signal, amongst them Support Vector Machine based on Radial Basis Function (SVM-RBF) provided the highest accuracy and F1-score of 93% for the 95 features. The area under the curve (AUC) value of 0.9831 was obtained using this classifier. These performance metrics suggests that computation of randomness measure such as entropy in the multivariate IMF domain provided better discriminating power in detection of Schizophrenia condition from the multichannel EEG signal.

4

Scattering transform-based features for the automatic seizure detection

Jiang Yun, Chen Wanzhong, You Yang

Biocybernetics and Biomedical Engineering

|

2020

|

Vol. 40, no. 1

77--89

EN

Developing the automatic detection system is of great clinical significance for assisting neurologists to detect epilepsy using electroencephalogram (EEG) signals. In this research, we explore the ability of a newly-developed algorithm named scattering transform in seizure detection. The preprocessed signal is initially decomposed into scattering coefficients with various orders and scales employing scattering transform. Fuzzy entropy (FuzzyEn) and Log energy entropy (LogEn) of the sub-band coefficients are obtained to characterize the epileptic seizure signals. Then the joint features are fed into five classifiers including support vector machine (SVM), least squares-support vector machine (LS-SVM), genetic algorithm-support vector machine (GA-SVM), extreme learning machine (ELM) and probabilistic neural network (PNN) for the verification of the effectiveness of the proposed scheme. Finally, we not only compare the classification results and the time efficiency derived from different classifiers, but also explore the discrimination performance of the proposed methodology based on ten different classification tasks with great clinical significance. The prominent classification accuracy (ACC) of 99.87 %, 99.59 %, 99.58 %, 99.56 % and 99.80 % are achieved using the above five classifiers respectively. The average ACC and Matthews correlation coefficient (MCC) of 99.75 % and 0.99 are also yielded based on all tasks. Furthermore, the result of Kruskal-Wallis Test for the verification of statistical significance confirms the reliability of the proposal. The comparison with the latest state-of-the-art techniques indicates the superior performance of the proposal. A tradeoff between classification accuracy and time complexity of the proposed approach is accomplished in our work and the possibility for clinical application is also demonstrated.

5

Classification of EEG signal by methods of machine learning

Alyamani Amina, Yasniy Oleh

Applied Computer Science

|

2020

|

Vol. 16, no 4

56--63

EN

Electroencephalogram (EEG) signal of two healthy subjects that was available from literature, was studied using the methods of machine learning, namely, decision trees (DT), multilayer perceptron (MLP), K-nearest neighbours (kNN), and support vector machines (SVM). Since the data were imbalanced, the appropriate balancing was performed by Kmeans clustering algorithm. The original and balanced data were classified by means of the mentioned above 4 methods. It was found, that SVM showed the best result for the both datasets in terms of accuracy. MLP and kNN produce the comparable results which are almost the same. DT accuracies are the lowest for the given dataset, with 83.82% for the original data and 61.48% for the balanced data.

6

Comparison of information on sleep apnoea contained in two symmetric EEG recordings

Prucnal Monika A., Polak Adam G.

Metrology and Measurement Systems

|

2019

|

Vol. 26, nr 2

229--239

EN

Electroencephalogram (EEG) is one of biomedical signals measured during all-night polysomnography to diagnose sleep disorders, including sleep apnoea. Usually two central EEG channels (C3-A2 and C4-A1) are recorded, but typically only one of them are used. The purpose of this work was to compare discriminative features characterizing normal breathing, as well as obstructive and central sleep apneas derived from these central EEG channels. The same methodology of feature extraction and selection was applied separately for the both synchronous signals. The features were extracted by combined discrete wavelet and Hilbert transforms. Afterwards, the statistical indexes were calculated and the features were selected using the analysis of variance and multivariate regression. According to the obtained results, there is a partial difference in information contained in the EEG signals carried by C3-A2 and C4-A1 EEG channels, so data from the both channels should be preferably used together for automatic sleep apnoea detection and differentiation.

7

Gray-level co-occurrence matrix of Fourier synchrosqueezed transform for epileptic seizure detection

Mamli Shamzin, Kalbkhani Hashem

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 1

87--99

EN

Epilepsy is a brain disorder that many persons of different ages in the world suffer from it. According to the world health organization, epilepsy is characterized by repetitive seizures and more electrical discharge in a group of brain neurons results in sudden physical actions. The aim of this paper is to introduce a new method to classify epileptic phases based on Fourier synchro-squeezed transform (FSST) of electroencephalogram (EEG) signals. FSST is a time-frequency (TF) analysis and provides sharper TF estimates than the conventional short-time Fourier transform (STFT). Absolute of FSST of EEG signal is computed and segmented into five non-overlapping frequency sub-bands as delta (d), theta (u), alpha (a), beta (b), and gamma (g). Each sub-band is considered as a gray-scale image and then we propose to obtain the gray-level co-occurrence matrix (GLCM) of each sub-band as features. We concatenate the features of different sub-bands to obtain the final feature vector. After selecting informative features by infinite latent feature selection (ILFS) method, the support vector machine (SVM) and K-nearest neighbor (KNN) classifiers are used separately to classify EEG signals. We use the EEG signals from Bonn University database and different combinations of its sets are considered. Simulation results show that the proposed method efficiently classifies the EEG signals and can be used to determine the phase of epilepsy.

8

Facial expressions as an artifact in EEG signal used in the process of controlling a mobile robot with LabVIEW

Paszkiel Sz.

Przegląd Elektrotechniczny

|

2017

|

R. 93, nr 4

156--160

EN

This paper describes the control of a mobile vehicle by means of LabVIEW environment and artifacts in the EEG signal. The solutions by Emotiv and the LabVIEW environment were combined in order to conduct an electrophysiological data analysis as a function of time correlated with a specific potential. Artifacts occurrence in a given area was analysed by means of the EEG signal, which was measured with electrodes. The input signals processed were used in order to control a real object in the form of a mobile vehicle.

PL

W artykule opisano kwestię sterowania pojazdem mobilnym przy wykorzystaniu środowiska LabVIEW oraz artefaktów pojawiających się w sygnale EEG. W pracy dokonano połączenia rozwiązań udostępnianych przez firmę Emotiv ze środowiskiem LabVIEW w celu przeprowadzenia analizy danych elektrofizjologicznych w funkcji czasu, skorelowanych z konkretnym potencjałem. Występowanie artefaktów w danym obszarze analizowane było w oparciu o sygnał EEG, który mierzony był za pomocą elektrod. Przetworzone sygnały wejściowe użyte zostały na potrzeby sterowania obiektem rzeczywistym w postaci robota mobilnego.

9

Effect of Feature Extraction on Automatic Sleep Stage Classification by Artificial Neural Network

Prucnal M., Polak A. G.

Metrology and Measurement Systems

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2017

|

Vol. 24, nr 2

229--240

EN

EEG signal-based sleep stage classification facilitates an initial diagnosis of sleep disorders. The aim of this study was to compare the efficiency of three methods for feature extraction: power spectral density (PSD), discrete wavelet transform (DWT) and empirical mode decomposition (EMD) in the automatic classification of sleep stages by an artificial neural network (ANN). 13650 30-second EEG epochs from the PhysioNet database, representing five sleep stages (W, N1-N3 and REM), were transformed into feature vectors using the aforementioned methods and principal component analysis (PCA). Three feed-forward ANNs with the same optimal structure (12 input neurons, 23 + 22 neurons in two hidden layers and 5 output neurons) were trained using three sets of features, obtained with one of the compared methods each. Calculating PSD from EEG epochs in frequency sub-bands corresponding to the brain waves (81.1% accuracy for the testing set, comparing with 74.2% for DWT and 57.6% for EMD) appeared to be the most effective feature extraction method in the analysed problem.

10

Concept of expert system interpreting correctness of measurement and method of the EEG signal analysis for needs of the brain-computer interface

Paszkiel S.

Poznan University of Technology Academic Journals. Electrical Engineering

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2016

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No. 88

209--216

EN

The concept of construction of the expert system interpreting correctness of measurement and the method of the EEG signal analysis for needs of the brain-computer interface (BCI) are described in the article. The general orientations related with the methodology of creating the expert systems based on the knowledge base (KB) are characterised. Also, the brain-computer interface technology was described, which has been recently gaining more and more popularity within the scope of its application in control processes.

11

Informatics system for concentration state analysis on the basis of a device operating in the brain – computer interface technology

Paszkiel Sz.

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

|

2016

|

nr 2

24--27

EN

The informatics system designed for the needs of the workgroup working at the Faculty of Automatic Control and Computer Sciences of Opole University of Technology consisting of two applications, of which one is currently the most popular operating system in smart phones was described in the article. The objective of operation of the mobile application is connection of functionality of a device for electroencephalographic measurements with a daily used mobile phone. Thanks to applied connection in the form of an application it is possible to verify the concentration state of the particular person during execution of the particular action with the special consideration to the tasks, which require high concentration. Thanks to the elaborated mobile application we are able to determine the most effective daytime for learning and to draw the characteristics of the concentration loss time. The second application in the system is used as the synchronization server.

PL

W artykule opisany został zaprojektowany na potrzeby grupy roboczej pracującej w ramach Instytutu Automatyki i Informatyki Politechniki Opolskiej system informatycznym składający się z dwóch aplikacji, z których jedna działa pod najpopularniejszym obecnie systemem operacyjnym na smartfonach. Celem pracy aplikacji mobilnej jest połączenie funkcjonalności urządzenia do pomiarów elektroencefalograficznych z używanym na co dzień urządzeniem telefonii mobilnej. Dzięki zastosowanemu połączeniu w postaci aplikacji, możliwa jest weryfikacja stanu skupienia danej osoby podczas wykonywania konkretnej czynności ze szczególnym uwzględnieniem tych zadań, które wymagają wysokiego skupienia. Dzięki opracowanej aplikacji mobilnej jesteśmy w stanie określić najbardziej efektywne pory dnia na naukę oraz wykreślić charakterystykę czasu utraty skupienia. Druga z aplikacji w systemie służy jako serwer synchronizacji.

12

Epileptic seizure detection based on improved wavelet neural networks in long-term intracranial EEG

Geng D., Zhou W., Zhang Y., Geng S.

Biocybernetics and Biomedical Engineering

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2016

|

Vol. 36, no. 2

375--384

EN

Automatic seizure detection is of great importance for speeding up the inspection process and relieving the workload of medical staff in the analysis of EEG recordings. In this study, a method based on an improved wavelet neural network (WNN) is proposed for automatic seizure detection in long-term intracranial EEG. WNN combines the traditional back propagation neural network (BPNN) with wavelet transform. Compared with classic WNN architectures, a modified point symmetry-based fuzzy c-means (MSFCM) algorithm is applied to the initialization of wavelet transform's translations, which has been successful in multiclass cancer classification. In addition, Fast-decaying Morlet wavelet is chosen as the activation function to make the WNN learn faster. Relative amplitude and relative fluctuation index are extracted as a feature vector to describe the variation of EEG signals, and the feature vector is then fed into WNN for classification. At last, post-processing including smoothing, channel fusion and collar technique is adopted to achieve more accurate and stable results. This system performs efficiently with the average sensitivity of 96.72%, specificity of 98.91% and false-detection rate of 0.27 h_1. The proposed approach achieves high sensitivity and low false detection rate, which demonstrates its potential for clinical usage.

13

Nowoczesna metoda komunikacji człowieka z komputerem : neurony źródłem informacji w technologii BCI

Paszkiel S.

Śląskie Wiadomości Elektryczne

|

2015

|

Nr 2 (119)

4--5

PL

W niniejszym artykule przedstawiona została nowoczesna metoda komunikacji człowieka z komputerem, jaką jest technologia mózg-komputer. Scharakteryzowano aspekt źródła informacji w wyżej wymienionej technologii, jakim są neurony występujące w mózgu człowieka. Artykuł przedstawia także informacje na temat nowoczesnych rozwiązań technologicznych opracowanych na bazie technologii mózg-komputer dostępnych na rynku. W skrócie wymienione zostały także potencjalne zastosowania metody komunikacji mózgu człowieka z komputerem zarówno w zakresie nauk technicznych jak i społecznych.

EN

This paper presents a modern method of human-computer communication, which is the brain-computer technology. Characterized aspect of sources of information in the above technology, which neurons are present in the human brain. Article also presents information on new technological solutions developed on the basis of brain-computer technology commercially available. In short listed as a potential application of the method of communication of the human brain with a computer, both in terms of technical and society sciences.

14

Rejestracja i analiza sygnału EEG na użytek neuromarketingu

Kołodziej M., Tarnowski P., Majkowski A., Rak J. R., Dec D.

Przegląd Elektrotechniczny

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2015

|

R. 91, nr 5

9-12

PL

Głównym celem eksperymentów jest znalezienie w zarejestrowanych sygnałach EEG cech, które umożliwiają rozróżnienie prezentowanych bodźców dźwiękowych o wysokim i niskim pobudzeniu emocjonalnym. Do wywołania określonych emocji wykorzystano bazę dźwięków IADS. W analizie sygnału wykorzystano wskaźniki, wbudowane w strukturę użytego oprogramowania, takie jak: Attention, Meditation, Delta (1-3 Hz), Theta (4-7 Hz) , Alpha1 (8-9 Hz), Alpha2 (10-12 Hz), Beta1 (13-17 Hz), Beta2 (18-30 Hz), Gamma1 (31-40 Hz), Gamma2 (41-50 Hz). Wykazano, że wskaźniki Attention, Alpha1, Alpha2 oraz Theta są powiązane w sposób istotny statystycznie ze stanem pobudzenia osoby badanej.

EN

The main objective of the experiments is to find, in recorded EEG signals, such features that allow to distinguish the presented sound stimuli with high and low emotional arousal. To evoke certain emotions IADS sound base was used. In signal analysis parameters built into the structure of the used software, such as Attention, Meditation, Delta (1-3 Hz), Theta (4-7 Hz) , Alpha1 (8-9 Hz), Alpha2 (10-12 Hz), Beta1 (13-17 Hz), Beta2 (18-30 Hz), Gamma1 (31-40 Hz), Gamma2 (41-50 Hz) were used. It has been shown that the parameters Attention, Alpha1, Alpha2 and Theta are related in a statistically significant way with the subject's state of arousal.

15

Dekompozycja sygnałów EEG w dziedzinie czasu przy zastosowaniu transformacji Hilberta-Huanga HHT

Gawędzki W., Socha M., Sławik P.

Przegląd Elektrotechniczny

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2015

|

R. 91, nr 5

33-36

PL

Do dekompozycji sygnałów EEG w dziedzinie czasu zastosowana została empiryczna metoda EMD (ang. Empirical Mode Decomposition), która w wersji rozszerzonej o transformację Hilberta funkcjonuje pod nazwą transformacji HHT (ang. Hilbert-Huang Transform). Transformacja ta umożliwia poprawną dekompozycję sygnału EEG na sumę quasi-harmonicznych składowych, których amplitudy oraz częstotliwości są parametrycznymi funkcjami czasu. W przeciwieństwie do stosowanych aktualnie w diagnostyce transformacji Fouriera DFT oraz STFT nadaje się ona do analizy zjawisk o charakterze zarówno nieliniowym jak i niestacjonarnym.

EN

An Empirical Mode Decomposition method extended with the Hilbert transform (Hilbert-Huang Transform) was used for EEG decomposition in time domain. This transformation allows for proper EEG signal decomposition into quasi-harmonic components that amplitudes and frequencies are time dependence functions. In contrast to commonly used in diagnostic’s DFT and STFT transformations, proposed method is suitable for non-stationary and nonlinear phenomenon’s.

16

Feature selection for EEG-based discrimination between imagination of left and right hand movements

Binias B., Palus H.

Measurement Automation Monitoring

|

2015

|

Vol. 61, No. 4

94--97

EN

: In this article was analyzed an influence of selected features on the accuracy of discrimination between imagination of right and left hand movements based on recorded EEG waveforms. The study showed a significant advantage that individual selection of features and a classification algorithm for analyzed data holds over the more general approach. The results were compared with the results obtained by the participants of the "BCI competition IV" and placed in the top three.

17

Synchronous SSVEP Data Acquisition System

Kotyra S., Wójcik G. M., Smolira M.

Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica

|

2014

|

Vol. 14, no. 3

15--19

EN

Steady State Visually Evoked Potentials have been known for several decades and they appear in the primary visual cortex of brain as a result of light stimulation of the sense of sight. In this article a simple method for electroencephalographic data acquisition is presented. The system is based on the DSM-51 unit connected to goggles with blinking diodes and Mindset-1000 EEG amplifier with 16 channels. We present self-developed hardware and method of effective synchronization for the light stimulation and brain activity recording.

18

Neurofeedback - eksperymenty w LabVIEW

Rak R. J., Majkowski A., Kołodziej M., Sułko G.

Przegląd Elektrotechniczny

|

2013

|

R. 89, nr 10

263-265

PL

W artykule przedstawiono aplikację do samodzielnego treningu umysłu z wykorzystaniem neurofeedbacku. Aplikacja została stworzona w środowisku LabVIEW, z użyciem otwartej platformy BCI2000. Najważniejsze części aplikacji to: moduł zbierania danych oraz moduł przetwarzania sygnału EEG przy użyciu szybkiego przekształcenia Fouriera. Kluczowym elementem systemu jest moduł, który dla określonej przez użytkownika częstotliwości, dokonuje pomiaru energii sygnału. Wyniki wyświetlane są na panelu aplikacji użytkownika, zapewniając pożądane sprzężenie zwrotne.

EN

This paper presents an application for self-training of the mind with the use of neurofeedback. The application was developed in LabVIEW environment, using the open BCI2000 platform. The most important parts of the application are data acquisition module and EEG signal processing module implementing Fast Fourier Transform. The key element of the system is the module that, for a user-specified frequency, measures signal energy. The results are then displayed to provide the desired feedback.

19

Brain-computer interaction analysis based on EEG signal and gaze-tracking information

Kaszuba K., Kostek B.

Elektronika : konstrukcje, technologie, zastosowania

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2012

|

Vol. 53, nr 5

21-25

EN

The article presents an attempt to integrate EEG signal analysis with information about human visual activities, i.e. gaze fixation point. The results from gaze-tracking-based measurement were combined with the standard EEG analysis. A search for correlation between the brain activity and the region of the screen observed by the user was performed. The preliminary stage of the study consists in electrooculography (EOG) signal processing. The EOG signal was obtained in a series of experiments and served as reference data. An attempt to correlate this Information with the EEG signal analysis is described and multiple approaches of signal pre-processing, feature extraction and classification are applied.

PL

W niniejszym artykule podjęto próbę analizy sygnału EEG z informacją o aktywności wzrokowej człowieka w kontekście interfejsów mózg-komputer. Wykorzystano funkcjonalności rejestratora sygnału EEG oraz systemu śledzenia punktu fiksacji wzroku. Poszukiwana była korelacja pomiędzy obserwowanym obszarem ekranu a aktywnością mózgu. Sygnał EOG, nagrany w trakcie serii wstępnych eksperymentów, posłużył jako dane referencyjne. Zbadano możliwość automatycznej detekcji podobnej informacji w sygnale EEG poprzez zastosowanie różnych metod wstępnego przetwarzania, ekstrakcji cech sygnału oraz zastosowaniu różnych klasyfikatorów.

20

Notes on the models of brain structures based on subject literature and problems of their implementation

Paszkiel S.

Zeszyty Naukowe. Elektryka / Politechnika Opolska

|

2011

|

z. 66

67-68

EN

Models enable to test the influence of different kinds of stimuli or changes of parameters of a model on its functioning as well as enable to formulate new hypotheses concerning a shaped system. Problems connected with implementations of models of EEG signal during conducted research are briefly described in this article.