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1

Application of bioelectrical brain activity research to assess the level of attention of a farm tractor operator

Trzyniec Karolina, Kądzioła Dawid, Stachurska Maria

Przegląd Elektrotechniczny

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2023

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R. 99, nr 1

319--322

EN

The aim of the research was to analyze the attention of a farm tractor operators during work performed in the field. The scope of work included the testing of the level of EEG signals in 4 drivers while driving a tractor with simultaneous operation of the navigation system and the analysis of the obtained results. Drivers' EEG signals were tested with the Emotiv Epoc Flex neuro-helmet. The level of beta waves (Low Beta and High Beta) responsible for attention was determined and the variability of the level was analyzed.

PL

Celem badań była analiza uwagi operatorów ciągnika rolniczego podczas prac polowych. Zakres prac obejmował badanie poziomu sygnałów EEG u 4 kierowców podczas jazdy ciągnikiem przy jednoczesnej pracy systemu nawigacyjnego oraz analizę uzyskanych wyników. Sygnały EEG kierowców zostały przetestowane za pomocą neurohełmu Emotiv Epoc Flex. Określono poziom fal beta (Low Beta i High Beta) odpowiedzialnych za uwagę i przeanalizowano zmienność poziomu.

2

EEG signal analysis for monitoring concentration of operators

Rykała Łukasz

Journal of Automation Mobile Robotics and Intelligent Systems

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2023

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Vol. 17, No. 1

30--39

EN

Often, operators of machines, including unmanned ground vehicles (UGVs) or working machines, are forced to work in unfavorable conditions, such as high tem‐ peratures, continuously for a long period of time. This has a huge impact on their concentration, which usu‐ ally determines the success of many tasks entrusted to them. Electroencephalography (EEG) allows the study of the electrical activity of the brain. It allows the determination, for example, of whether the operator is able to focus on the realization of his tasks. The main goal of this article was to develop an algorithm for determining the state of brain activity by analyzing the EEG signal. For this purpose, methods of EEG sig‐ nal acquisition and processing were described, including EEG equipment and types and location of electrodes. Particular attention was paid to EEG signal acquisition, EEG signal artifacts, and disturbances, and elements of the adult’s correct EEG recording were described in detail. In order to develop the algorithm mentioned, basic types of brain waves were discussed, and exem‐ plary states of brain activity were recorded. The influ‐ ence of technical aspects on the recording of EEG sig‐ nals was also emphasized. Additionally, a block diagram was created which is the basis for the operation of the said algorithm. The LabVIEW environment was used to implement the created algorithm. The results of the research showing the operation of the developed EEG signal analyzer were also presented. Based on the results of the study, the EEG analyzer was able to accurately determine the condition of the examined person and could be used to study the concentration of machine operators.

3

EEG based workload and stress assessment during remote ship operations

Kari R., Gausdal A. H., Steinert M.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2022

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Vol. 16 No. 2

295--305

EN

Autonomous and remotely controlled ships present new types of human factor challenges. An investigation of the underlying human factors in such operations is therefore necessary to mitigate safety hazards while improving operational efficiency. More tests are needed to identify operators’ levels of control, workload and stress. The aim of this study is to assess how increases in mental workload influence the stress levels of Shore Control Centre (SCC) operators during remote ship operations. Nine experiments were performed to investigate the stress levels of SCC operators during human-human and human-machine interactions. Data on the brain signals of human operators were collected directly by electroencephalography (EEG) and subjectively by the NASA task load index (TLX). The results show that the beta and gamma band powers of the EEG recordings were highly correlated with subjective levels of workload and stress during remote ship operations. They also show that there was a significant change in stress levels when workload increased, when ships were operating in harsh weather, and when the number of ships each SCC operator is responsible for was increased. Furthermore, no significant change in stress was identified when SCC operators established very high frequency (VHF) communication or when there was a risk of accident.

4

Analysis of the application of brain-computer interfaces of a selected paradigm in everyday life

Mróz Katarzyna, Plechawska-Wójcik Małgorzata

Journal of Computer Sciences Institute

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2022

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Vol. 23

118--122

EN

The main objective of this paper is to carry out a research on the analysis of the use of brain-computer interface in everyday life. The article presents the method of recording brain activity, electroencephalography, which was used in the study. The brain activity used in the brain-computer interface and the general principle of brain-computer interface design are also described. The performed study allowed to develop an analysis of the obtained results in the matter of evaluating the usability of brain-computer interfaces using motor imagery. As a result of the process of analyzing the results obtained during the research, it was found that each subsequent experiment allowed for obtaining more favourable results than the previous one. The reason for this was the use of an additional training session for the next test person. In the final stage, it was possible to evaluate the usability of the brain-computer interface in everyday life

PL

Głównym celem artykułu jest przeprowadzenie badania nad analizą wykorzystania interfejsu mózg-komputer w życiu codziennym. W artykule przedstawiono metodę rejestrowania aktywności mózgu, elektroencefalografię, która została wykorzystana w badaniu. Opisano również aktywność mózgu wykorzystywaną w interfejsie mózg-komputer oraz ogólną zasadę projektowania interfejsu mózg-komputer. Przeprowadzone badanie pozwoliło na opracowanie analizy uzyskanych wyników w zakresie oceny użyteczności interfejsów mózg-komputer z wykorzystaniem obrazowania motorycznego. W wyniku procesu analizy wyników uzyskanych podczas przeprowadzania badań ustalono, iż każdy następnie zrealizowany eksperyment pozwalał na uzyskanie korzystniejszych wyników od poprzedniego. Powodem tego było zastosowanie dodatkowej sesji treningowej dla kolejnych badanych osób. W końcowym etapie można było ocenić przydatność interfejsu mózg-komputer w życiu codziennym

5

EEG_GENet: A feature-level graph embedding method for motor imagery classification based on EEG signals

Wang Huiyang, Yu Hua, Wang Haixian

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 3

1023--1040

EN

In recent years, the success of deep learning has driven the development of motor imagery brain-computer interfaces (MI-BCIs) based on electroencephalography (EEG). However, unlike image or language data, motor imagery EEG signals are of multielectrodes with topology information. As a means of integrating graph topology information into feature maps, few studies studied motor imagery classification involving graph embeddings. To decode EEG signals more accurately, this paper proposes a feature-level graph embedding method and combines the method with EEGNet; this new network is called EEG_GENet. Specifically, time-domain features are obtained by convoluting raw EEG signals for each electrode. Then, the adjacent matrix, conceptualized as a graph filter, performs graph convolution and uses the time-domain features to embed the topology information. This process can also perform multi-order graph embeddings. In addition, the adjacency matrix in this paper can adapt to different brain network connectivities for different subjects. We evaluate the proposed method on two benchmark EEG datasets for motor imagery classification. Experimental results on the BCICIV-2a and High_Gamma datasets demonstrate that EEG_GENet achieves 79.57% and 96.02% classification accuracy, respectively. These results indicate that the proposed method is superior to state-of-the-art methods. In addition, various ablation experiments further verify the advantages of the feature-level graph embedding method. To conclude, the feature-level graph embedding method can improves the network’s ability to decode raw motor imagery EEG signals.

6

A novel approach for detection of consciousness level in comatose patients from EEG signals with 1-D convolutional neural network

Altıntop Çiğdem Gülüzar, Latifoğlu Fatma, Akın Aynur Karayol, Çetin Bilge

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

16--26

EN

Coma is an unresponsive state of unconsciousness from which a person cannot be awakened. Glasgow Coma Score (GCS) is a clinical scale for determining the depth and length of a coma. GCS plays an important role in effective and accurate patient evaluation and is critical in planning the right treatment modalities and patient care because it shows patient outcomes and is a measurement performed several times a day. The GCS is universally accepted as a gold standard and validated scale for assessing a patient’s level of consciousness. However, the scale’s success has been questioned due to variations in interobserver reliability performance. In this study, the data set generated from Electroencephalography (EEG) signals obtained from 39 comatose patients was used in the training of deep neural networks for the classification of consciousness level. The EEG signals were recorded during nurse and family interaction with comatose patients. The level of consciousness was classified with the proposed 1D-CNN model. Consequently, the two classes that we label as low and high consciousness are classified with 83.3% accuracy. To our best knowledge, no prior studies are using 1D-CNN for the classification of EEG-based level of consciousness using the proposed recording process. Our study is unique from other studies in terms of recording procedure and methods.

7

Sequence-to-sequence deep neural network with spatio-spectro and temporal features for motor imagery classification

Rammy Sadaqat Ali, Abbas Waseem, Mahmood Syed Shahid, Riaz Haider, Rehman Haseeb Ur, Abideen Rao Zain Ul, Aqeel Muhammad, Zhang Wu

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

97--110

EN

Electroencephalography (EEG) is a method of the brain–computer interface (BCI) that measures brain activities. EEG is a method of (non-)invasive recording ofthe electrical activity ofthe brain. This can be used to build BCIs. From the last decade, EEG has grasped researchers' attention to distinguish human activities. However, temporal information has rarely been retained to incorporate temporal information for multi-class (more than two classes) motor imagery classification. This research proposes a long-short-term-memory-based deep learning model to learn the hidden sequential patterns. Two types of features are used to feed the proposed model, including Fourier Transform Energy Maps (FTEMs) and Common Spatial Patterns (CSPs) filters. Multiple experiments have been conducted on a publicly available dataset. Extraction of spatial and spectro-temporal features using CSP filters and FTEM allow the sequence-tosequence based proposed model to learn the hidden sequential features. The proposed method is trained, evaluated, and optimized for a publicly available benchmark data set and resulted in 0.81 mean kappa value. Obtained results depict the model robustness for the artifacts and suitable for real-life applications with comparable classification accuracy. The code and findings will be available at https://github.com/waseemabbaas/Motor-Imagery-Classification.git.

8

Detection of Parkinson’s disease using automated tunable Q wavelet transform technique with EEG signals

Khare Smith K., Bajaj Varun, Acharya U. Rajendra

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

679--689

EN

Deep brain simulations play an important role to study physiological and neuronal behavior during Parkinson’s disease (PD). Electroencephalogram (EEG) signals may faithfully represent the changes that occur during PD in the brain. But manual analysis of EEG signals is tedious, and time consuming as these signals are complex, non-linear, and non-stationary nature. Therefore EEG signals are required to decompose into multiple subbands (SBs) to get detailed and representative information from it. Experimental selection of basis function for the decomposition may cause system degradation due to information loss and an increased number of misclassification. To address this, an automated tunable Q wavelet transform (A-TQWT) is proposed for automatic decomposition. A-TQWT extracts representative SBs for analysis and provides better reconstruction for the synthesis of EEG signals by automatically selecting the tuning parameters. Five features are extracted from the SBs and classified different machine learning techniques. EEG dataset of 16 healthy controls (HC) and 15 PD (ON and OFF medication) subjects obtained from ”openneuro” is used to develop the automated model. We have aimed to develop an automated model that effectively classify HC subjects from PD patients with and without medication. The proposed method yielded an accuracy of 96.13% and 97.65% while the area under the curve of 97% and 98.56% for the classification of HC vs PD OFF medication and HC vs PD ON medication using least square support vector machine, respectively.

9

Automated detection of driver fatigue from electroencephalography through wavelet-based connectivity

Ahmadi Amirmasoud, Bazregarzadeh Hanieh, Kazemi Kamran

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

316--332

EN

Background: Mental fatigue is one of the most causes of road accidents. Identification of biological tools and methods such as electroencephalogram (EEG) are invaluable to detect them at early stage in hazard situations. Methods: In this paper, an expert automatic method based on brain region connectivity for detecting fatigue is proposed. The recorded general data during driving in both fatigue (the last five minutes) and alert (at the beginning of driving) states are used in analyzing the method. In this process, the EEG data during continuous driving in one to two hours are noted. The new feature of Gaussian Copula Mutual Information (GCMI) based on wavelet coefficients is calculated to detect brain region connectivity. Classification for each subject is then done through selected optimal features using the support vector machine (SVM) with linear kernel. Results: The designed technique can classify trials with 98.1% accuracy. The most significant contributions to the selected features are the wavelet coefficients details 1_2 (corresponding to the Beta and Gamma frequency bands) in the central and temporal regions. In this paper, a new algorithm for channel selection is introduced that has been able to achieve 97.2% efficiency by selecting eight channels from 30 recorded channels. Conclusion: The obtained results from the classification are compared with other methods, and it is proved that the proposed method accuracy is higher from others at a significant level. The technique is completely automatic, while the calculation load could be reduced remarkably through selecting the optimal channels implementing in real-time systems.

10

Artifact removal from EEG signals recorded in non-restricted environmentArtifact removal from EEG signals recorded in non-restricted environment

Jamil Zainab, Jamil Afshan, Majid Muhammad

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

503--515

EN

Electroencephalography (EEG) signals are always accompanied by endogenous and exogenous artifacts. Research carried out in the past few years focused on EEG artifact removal considered EEG signals recorded in a restricted lab environment. Considering the importance of EEG in daily life activities, no definitive approach is presented in removing blink artifacts from non-restricted EEG recordings. In this paper, a new supervised artifact removal method is proposed that classifies EEG chunks having eye movements and then utilizes independent component analysis and discrete wavelet transform to eliminate the ocular artifacts. The EEG data is acquired from 29 subjects in a non-restricted environment where the subject has to watch videos while walking and giving gestures and facial expressions. Thirteen morphological features are extracted from the recorded EEG signals to classify chunks with eye movements. The EEG chunks with eye movements are further processed to remove noise without distorting the morphology of signals. The proposed method is tested for eye movements and shows an improved performance in terms of correlation, mutual information, phase difference, and computational time over unsupervised modified multi-scale sample entropy and kurtosis, and wavelet enhanced independent component analysis based approaches. Moreover, the computed values of statistical parameters including sensitivity and specificity show the robustness of the proposed scheme.

11

Initial study on changes in activity of brain waves during audio stimulation using noninvasive brain-computer interfaces: choosing the appropriate filtering method

Browarska Natalia, Kawala-Sterniuk Aleksandra, Zygarlicki Jarosław

Bio-Algorithms and Med-Systems

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2021

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Vol. 17, no. 2

79--93

EN

Objectives: In this paper series of experiments were carried out in order to check the influence of various sounds on human concentration during visually stimulated tasks performance. Methods: The obtained data was filtered. For the study purposes various smoothing filters were tested, including Median and Savitzky-Golay Filters; however, median filter only was applied. Implementation of this filter made the obtained data more legible and useful for potential diagnostics purposes. The tests were carried out with the implementation of the Emotiv Flex EEG headset. Results: The obtained results were promising and complied with the initial assumptions, which stated that the “relax”- phase, despite relaxing sounds stimuli, is strongly affected with the “focus”-phase with distracting sounds, which is clearly visible in the shape of the recorded EEG data. Conclusions: Further investigations with broader range of subjects is being currently carried out in order to confirm the already obtained results.

12

Brain-computer interface for electric wheelchair based on alpha waves of EEG signal

Banach Kacper, Małecki Mateusz, Rosół Maciej, Broniec Anna

Bio-Algorithms and Med-Systems

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2021

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Vol. 17, no. 3

165--172

EN

Objectives: Helping patients suffering from serious neurological diseases that lead to hindering the independent movement is of high social importance and an interdisciplinary challenge for engineers. Brain–computer interface (BCI) interfaces based on the electroencephalography (EEG) signal are not easy to use as they require time consuming multiple electrodes montage. We aimed to contribute in bringing BCI systems outside the laboratories so that it could be more accessible to patients, by designing a wheelchair fully controlled by an algorithm using alpha waves and only a few electrodes. Methods: The set of eight binary words are designed, that allow to move forward, backward,turn right andleft, rotate 45° as well as toincrease and decrease the speed of the wheelchair. Our project includes: development of a mobile application which is used as a graphical user interface, real-time signal processing of the EEG signal, development of electric wheelchair engines control system and mechanical construction. Results: The average sensitivity, without training, was 79.58% and specificity 97.08%, on persons who had no previous contact with BCI. Conclusions: The proposed system can be helpful for people suffering from incurable diseases that make them closed in their bodies and for whom communication with the surrounding world is almost impossible.

13

Using brain-computer interface technology as a controller in video games

Zając Błażej, Paszkiel Szczepan

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2020

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T. 10, nr 3

26--31

EN

Nowadays, control in video games is based on the use of a mouse, keyboard and other controllers. A Brain Computer Interface (BCI) is a special interface that allows direct communication between the brain and the appropriate external device. Brain Computer Interface technology can be used forcommercial purposes, for example as a replacement for a keyboard,mouse or other controller. This article presents a method of controlling video games using the EMOTIV EPOC + Neuro Headset as a controller.

PL

W obecnych czasach sterowanie w grach wideo jest oparte na wykorzystaniu myszki, klawiatury oraz innych kontrolerów. Brain-Computer Interface w skrócie BCI to specjalnyinterfejspozwalający na bezpośrednią komunikację międzymózgiem,a odpowiednim urządzeniem zewnętrznym. Technologia Brain-Computer Interface może zostać użyta w celach komercyjnych na przykład jako zamiennik myszki klawiatury lub innego kontrolera. Wartykule przedstawiono sposób sterowania w grach wideo przy pomocy neuro-headsetu EMOTIV EPOC+ jako kontrolera.

14

Detection of SSVEP based on empirical mode decomposition and power spectrum peaks analysis

Antelis Javier M., Rivera Camilo A., Galvis Eduard, Ruiz-Olaya Andres F.

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

1010--1021

EN

Steady-state visual evoked potential (SSVEP) based brain–computer interfaces have been widely studied because these systems have potential to restore capabilities of communication and control of disable people. Identifying target frequency using SSVEP signals is still a great challenge due to the poor signal-to-noise ratio of these signals. Commonly, this task is carried out with detection algorithms such as bank of frequency-selective filters and canonical correlation analysis. This work proposes a novel method for the detection of SSVEP that combines the empirical mode decomposition (EMD) and a power spectral peak analysis (PSPA). The proposed EMD+PSPA method was evaluated with two EEG datasets, and was compared with the widely used FB and CCA. The first dataset is freely available and consists of three flickering light sources; the second dataset was constructed and consists of six flickering light sources. The results showed that proposed method was able to detect SSVEP with high accuracy (93.67 ± 9.97 and 78.19 ± 23.20 for the two datasets). Furthermore, the detection accuracy results achieved with the first dataset showed that EMD+PSPA provided the highest detection accuracy (DA) in the largest number of participants (three out of five), and that the average DA across all participant was 93.67 ± 9.97 which is 7% and 4% more than the average DA achieved with FB and CCA, respectively.

15

Classification of pilots' mental states using a multimodal deep learning network

Han Soo-Yeon, Kwak No-Sang, Oh Taegeun, Lee Seong-Whan

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

324--336

EN

An automation system for detecting the pilot's diversified mental states is an extremely important and essential technology, as it could prevent catastrophic accidents caused by the deteriorated cognitive state of pilots. Various types of biosignals have been employed to develop the system, since they accompany neurophysiological changes corresponding to the mental state transitions. In this study, we aimed to investigate the feasibility of a robust detection system of the pilot's mental states (i.e., distraction, workload, fatigue, and normal) based on multimodal biosignals (i.e., electroencephalogram, electrocardiogram, respiration, and electrodermal activity) and a multimodal deep learning (MDL) network. To do this, first, we constructed an experimental environment using a flight simulator in order to induce the different mental states and to collect the biosignals. Second, we designed the MDL architecture – which consists of a convolutional neural network and long short-term memory models – to efficiently combine the information of the different biosignals. Our experimental results successfully show that utilizing multimodal biosignals with the proposed MDL could significantly enhance the detection accuracy of the pilot's mental states.

16

Classification of epileptic EEG signals using PSO based artificial neural network and tunable-Q wavelet transform

George S. Thomas, Subathra M. S. P., Sairamya N. J., Susmitha L., Joel Premkumar M.

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 2

709--728

EN

Epilepsy is a widely spread neurological disorder caused due to the abnormal excessive neural activity which can be diagnosed by inspecting the electroencephalography (EEG) signals visually. The manual inspection of EEG signals is subjected to human error and is a tedious process. Further, an accurate diagnosis of generalized and focal epileptic seizures from normal EEG signals is vital for the supervision of pertinent treatment, life advancement of the subjects, and reduction in cost for the subjects. Hence the development of automatic detection of generalized and focal epileptic seizures from normal EEG signals is important. An approach based on tunable-Q wavelet transform (TQWT), entropies, Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) is proposed in this work for detection of epileptic seizures and its types. Two EEG databases namely, Karunya Institute of Technology and Sciences (KITS) EEG database and Temple University Hospital (TUH) database consisting of normal, generalized and focal EEG signals is used in this work to analyze the performance of the proposed approach. Initially, the EEG signals are decomposed into sub-bands using TQWT and the non-linear features like log energy entropy, Shannon entropy and Stein's unbiased risk estimate (SURE) entropy is computed from each sub-band. The informative features from the computed feature vectors are selected using PSO and fed into ANN for the classification of EEG signals. The proposed algorithm for KITS database achieved a maximum accuracy of 100% for four experimental cases namely, (i) normal-focal, (ii) normal-generalised, (iii) normal-focal + generalised and (iv) normal-focal-generalised. The TUH database achieved an accuracy of 95.1%, 97.4%, 96.2% and 88.8% for the four experimental cases. The proposed approach is promising and able to discriminate the epileptic seizure types with satisfactory classification performance.

17

A robust method for early diagnosis of autism spectrum disorder from EEG signals based on feature selection and DBSCAN method

Abdolzadegan Donya, Moattar Mohammad Hossein, Ghoshuni Majid

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

482--493

EN

Electroencephalogram (EEG) is one of the most important signals for diagnosis of Autism Spectrum Disorder (ASD). There are different challenges such as feature selection and the existence of artifacts in EEG signals. This article aims to present a robust method for early diagnosis of ASD from EEG signal. The study population consists of 34 children with ASD between 3–12 years and 11 healthy children in the same ranges of age. The proposed approach uses linear and nonlinear features such as Power Spectrum, Wavelet Transform, Fast Fourier Transform (FFT), Fractal Dimension, Correlation Dimension, Lyapunov Exponent, Entropy, Detrended Fluctuation Analysis and Synchronization Likelihood for describing the EEG signal. In addition Density Based Clustering is utilized for artifact removal and robustness. Besides, features selection is applied based on different criterions such as Mutual Information (MI), Information Gain (IG), Minimum-Redundancy Maximum-Relevancy (mRmR) and Genetic Algorithm (GA). Finally, the K-Nearest-Neighbor (KNN) and Support Vector Machines (SVM) classifiers are used for final decision. As a result, the investigation indicates that the classification accuracy of the approach using SVM is 90.57% while for KNN it is 72.77%. Moreover, the sensitivity of the proposed method is 99.91% for SVM and 91.96% for KNN. Also, experiments show that DFA, LE, Entropy and SL features have considerable influence in promoting the classification accuracy.

18

Cooperation of CUDA and Intel multi-core architecture in the independent component analysis algorithm for EEG data

Gajos-Balińska Anna, Wójcik Grzegorz M., Stpiczyński Przemysław

Bio-Algorithms and Med-Systems

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2020

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Vol. 16, no. 3

art. no. 20200044

EN

Objectives: The electroencephalographic signal is largely exposed to external disturbances. Therefore, an important element of its processing is its thorough cleaning. Methods: One of the common methods of signal improvement is the independent component analysis (ICA). However, it is a computationally expensive algorithm, hence methods are needed to decrease its execution time. One of the ICA algorithms (fastICA) and parallel computing on the CPU and GPU was used to reduce the algorithm execution time. Results: This paper presents the results of study on the implementation of fastICA, which uses some multi-core architecture and the GPU computation capabilities. Conclusions: The use of such a hybrid approach shortens the execution time of the algorithm.

19

Analysis of brain waves changes in stressful situations based on horror game with the implementation of virtual reality and braincomputer interface system: a case study

Browarska Natalia, Kawala-Sterniuk Aleksandra, Chechelski Przemysław, Zygarlicki Jarosław

Bio-Algorithms and Med-Systems

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2020

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Vol. 16, no. 4

art. no. 20200050

EN

Objectives: This presents a case for fear and stress stimuli and afterward EEG data analysis. Methods: The stress factor had been evoked by a computer horror game correlated with virtual reality (VR) and brain-computer interface (BCI) from OpenBCI, applied for the purpose of brain waves changes observation. Results: Results obtained during the initial study were promising and provide conclusions for further research in this field carried out on an expanded group of involved participants. Conclusions: The study provided very promising and interesting results. Further investigation with larger amount of participants will be carried out.

20

An empirical survey of electroencephalographybased brain-computer interfaces

Wankhade Megha M., Chorage Suvarna S.

Bio-Algorithms and Med-Systems

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2020

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Vol. 16, no. 4

art. no. 20190053

EN

Objectives: The Electroencephalogram (EEG) signal is modified using the Motor Imagery (MI) and it is utilized for patients with high motor impairments. Hence, the direct relationship between the computer and brain is termed as an EEG-based brain-computer interface (BCI). The objective of this survey is to presents an analysis of the existing distinct BCIs based on EEG. Methods: This survey provides a detailed review of more than 60 research papers presenting the BCI-based EEG, like motor imagery-based techniques, spatial filtering-based techniques, Steady-State Visual Evoked Potential (SSVEP)- based techniques, machine learning-based techniques, Event-Related Potential (ERP)-based techniques, and online EEG-based techniques. Subsequently, the research gaps and issues of several EEG-based BCI systems are adopted to help the researchers for better future scope. Results: An elaborative analyses as well as discussion have been provided by concerning the parameters, like evaluation metrics, year of publication, accuracy, implementation tool, and utilized datasets obtained by various techniques. Conclusions: This survey paper exposes research topics on BCI-based EEG, which helps the researchers and scholars, who are interested in this domain.