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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.
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.
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.
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.
5
Content available remote Classification of pilots' mental states using a multimodal deep learning network
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.
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.
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.
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.
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.
10
Content available remote An empirical survey of electroencephalographybased brain-computer interfaces
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.
11
Content available remote Interfejs mózg-komputer jako moduł mechatroniczny
PL
Komunikacja z otoczeniem to jedna z podstawowych potrzeb człowieka, z zaspokojeniem której mają problem osoby niepełnosprawne i w podeszłym wieku, napotykając na bariery utrudniające im poruszanie się i przekaz werbalny. Interfejs mózg-komputer to urządzenie, które wykorzystuje oczyszczony i przetworzony sygnał bioelektryczny człowieka do komunikacji z urządzeniem bezprzewodowym. Pomaga zdiagnozować nieprawidłową pracę mózgu. Poprzez gry komputerowe rozwija refleks i uczy koncentracji. Zastosowany jako moduł mechatroniczny umożliwia sterowanie urządzeniami i systemami mechatronicznymi.
EN
Communication to envirnment constitutes one of the basis poaple's need. Meet of this need creates significant problem both for disabled people and elderly people due to mobility limitations and verbal communication limitations. Brain-computer interfaces (BCI) conctitutes device which uses filtered and processed human's bioelectrical signal to communicate to wireless device. It helps diagnise improper work of the brain. It also develops reflex and concentration thanks to BCI0controlled computer games. BCI-based mechatronic module allows to contorl mechatronic devices and systems.
12
Content available remote Wykorzystanie analizy spektralnej przy tworzeniu trendu CFM/aEEG
PL
W pracy zaprezentowano badania CFM, które są przetworzonym zapisem klasycznego EEG przedstawionym (po odpowiedniej transformacji i analizie matematycznej) w postaci trendu sygnału. Pokazują one wielogodzinny zapis aktywności mózgu w sposób zbiorczy, co pozwala na ocenę długoterminową stanu centralnego układu nerwowego. Prace związane z doskonaleniem algorytmów obrazowania, skłoniły nas do próby tworzenia nowego typu trendu, gdzie zamiast koloru kodującego amplitudę próbujemy przy pomocy koloru kodować informację związaną z częstotliwością – co z powodzeniem może być użyteczne dla neonatologów.
EN
The paper presents CFM studies, which is a processed version of the classic EEG presented (after appropriate transformation and mathematical analysis) in the form of signal trends. They show a multi-hour record of brain activity in a collective way, allowing for a long-term assessment of the central nervous system. The work on improving imaging algorithms has led us to try to create a new trend type, where, instead of the color coding for amplitude, we try to encode frequency information using color – which can be useful for neonatologists.
PL
Artykuł opisuje badania polegające na porównaniu czasów reakcji na bodźce wzrokowe i słuchowe przy pomocy potencjałów wywołanych EEG. Do realizacji badań wykorzystano dwa eksperymenty. Pierwszy badał czasy reakcji na bodźce wzrokowe, drugi badał czasy reakcji na bodźce słuchowe. Po przeprowadzeniu analizy danych uzyskane rezultaty pozwoliły określić, że bodźce wzrokowe wywołują szybszą reakcję niż bodźce słuchowe.
EN
The paper describes results of comparison of reactions times to visual and auditory stimuli using EEG evoked potentials. Two experiments were used to applied. The first one explored reaction times to visual stimulus and the second one to auditory stimulus. After conducting an analysis of data, received results enable determining that visual stimuli evoke faster reactions than auditory stimuli.
EN
Nowadays, brain-computer interfaces are gaining more and more popularity. Research centers develop new methods of human communication with devices through thoughts. There are many methods used for this kind of interfaces, however, the most widespread is electroencephalography (EEG). There are many reasons for this fact, it is a method that is relatively cheap compared to other methods. Less complex technical tools and apparatus are required to operate it. Another advantage of this method, unlike others, is its non-invasiveness. Unfortunately, current brain-computer interfaces do not offer high data rates. However, time plays a smaller role when we are dealing with a disabled person who regains the ability to communicate with the world through the interface controlled by thoughts. This paper is the beginning of a series of papers in which the author will describe in detail the elements of brain-computer interfaces, as well as improvements that can be applied to them to improve their properties.
PL
W obecnych czasach interfejsy mózg-komputer zyskują coraz większą popularność. Ośrodki badawcze opracowują nowe metody komunikacji człowieka z urządzeniami za pomocą myśli. Jest wiele metod stosowanych do tego rodzajów interfejsów jednak najbardziej rozpowszechnioną jest Elektroencefalografia. Jest wiele powodów tego faktu, jest to metoda która jest stosunkowo tania w porównaniu z innymi metodami. Do jej obsługi wymagane są mniej złożone technicznie narzędzia i aparatura. Kolejnym atutem tej metody w przeciwieństwie do innych jest jej nieinwazyjność. Niestety obecne interfejsy mózg-komputer nie oferują wysokiej szybkości przesyłania danych. Jednak czas odgrywa mniejszą rolę gdy mamy do czynienia z osobą niepełnosprawną, która odzyskuję możliwość komunikacji ze światem za pomocą interfejsu sterowanego myślami. Niniejszy artykuł jest początkiem serii artykułów w których autor będzie szczegółowo opisywał elementy interfejsów mózg-komputer, a także usprawnienia jakie można do nich zastosować aby polepszyć ich właściwości.
15
Content available remote Fast statistical model-based classification of epileptic EEG signals
EN
This paper presents a supervised classification method to accurately detect epileptic brain activity in real-time from electroencephalography (EEG) data. The proposed method has three main strengths: it has low computational cost, making it suitable for real-time implementation in EEG devices; it performs detection separately for each brain rhythm or EEG spectral band, following the current medical practices; and it can be trained with small datasets, which is key in clinical problems where there is limited annotated data available. This is in sharp contrast with modern approaches based on machine learning techniques, which achieve very high sensitivity and specificity but require large training sets with expert annotations that may not be available. The proposed method proceeds by first separating EEG signals into their five brain rhythms by using awavelet filter bank. Each brain rhythm signal is then mapped to a low-dimensional manifold by using a generalized Gaussian statistical model; this dimensionality reduction step is computationally straight-forward and greatly improves supervised classification performance in problems with little training data available. Finally, this is followed by parallel linear classifications on the statistical manifold to detect if the signals exhibit healthy or abnormal brain activity in each spectral band. The good performance of the proposed method is demonstrated with an application to paediatric neurology using 39 EEG recordings from the Children's Hospital Boston database, where it achieves an average sensitivity of 98%, specificity of 88%, and detection latency of 4 s, performing similarly to the best approaches from the literature.
EN
Quantification of abnormality in brain signals may reveal brain conditions and pathologies. In this study, we investigate different electroencephalography (EEG) feature extraction and classification techniques to assist in the diagnosis of both epilepsy and autism spectrum disorder (ASD). First, the EEG signal is pre-processed to remove major artifacts before being decomposed into several EEG sub-bands using a discrete-wavelet-transform (DWT). Two nonlinear methods were studied, namely, Shannon entropy and largest Lyapunov exponent, which measure complexity and chaoticity in the EEG recording, in addition to the two conventional methods (namely, standard deviation and band power). We also study the use of a cross-correlation approach to measure synchronization between EEG channels, which may reveal abnormality in communication between brain regions. The extracted features are then classified using several classification methods. Different EEG datasets are used to verify the proposed design exploration techniques: the University of Bonn dataset, the MIT dataset, the King Abdulaziz University dataset, and our own EEG recordings (46 subjects). The combination of DWT, Shannon entropy, and k-nearest neighbor (KNN) techniques produces the most promising classification result, with an overall accuracy of up to 94.6% for the three-class (multi-channel) classification problem. The proposed method obtained better classification accuracy compared to the existing methods and tested using larger and more comprehensive EEG dataset. The proposed method could potentially be used to assist epilepsy and ASD diagnosis therefore improving the speed and the accuracy.
EN
The measurement of evoked potentials has become a standard tool to test new hardware and software for electroencephalography (EEG). In this study, we investigate where to detect and how to improve visually, auditory and somatosensory evoked potentials with a reduced number of electrodes. We measured a total of 50 evoked potentials in healthy subjects, and we were able to detect visually, auditory and somatosensory evoked potentials with just three electrodes. We also investigated where to measure a combination of visually, auditory and somatosensory evoked potentials and found the best positions to be Oz, O1, O2, TP9 and TP10. In the second part of this study, we analyzed how the evoked potentials depend on the segmentation frequency selected to superpose EEG responses. We found that the detection of visually evoked potentials requires the segmentation frequency to match the stimulus frequency with an accuracy of at least 99.92 percent. The detection of auditory evoked potentials and somatosensory evoked potentials requires a matching of at least 99.95 percent. Therefore, a correct matching of the segmentation frequency with the stimulation frequency is the primary key to improving the quality of evoked potentials.
PL
W pracy przedstawiono charakterystykę systemu do wspomagania komunikacji w procesie neurorehabilitacji osób w stanie ograniczonej świadomości. Przygotowana aplikacja komputerowa wykorzystuje metodę śledzenia wzroku wspomaganą analizą sygnału EEG. W pracy podano genezę powstania systemu, scharakteryzowano zaimplementowane ćwiczenia oraz pozostałe funkcjonalności, a także zamieszczono wyniki wstępnych badań dokonanych w kilku polskich ośrodkach terapeutycznych.
EN
The paper presents the characteristics of a system dedicated to communication support in the process of neurorehabilitation of persons in a state of limited consciousness. The prepared computer application uses eye tracking method supported by the EEG signal analysis. The paper presents the origin of the system, the implemented exercises and other system functionalities, as well as the results of the preliminary research carried out in several therapeutic centers.
PL
W artykule przedstawiono system zwiększający bezpieczeństwo pracy operatora maszyny i urządzeń w oparciu o pomiar poziomu koncentracji uwagi. Powstał on w odpowiedzi na wyniki analiz stanu bezpieczeństwa pracy WUG wskazujące, że najczęstszą przyczyną wypadków jest „niedostateczna koncentracja przy wykonywaniu pracy”. W artykule przedstawiono analizę rozwiązań rynkowych związanych z podjętą tematyką oraz opis proponowanego rozwiązania w postaci koncepcji systemu.
EN
The system increasing safety of machine and equipment operators by measuring the level of operator's attention is presented. The system has been developed in response to the analyses of occupational safety conditions conducted by WUG, which indicate that insufficient concentration at the workplace is the most common cause of accidents. Description of the suggested solution in a form of the system measuring concentration of attention as well as analysis of the market solutions related to the subject matter is presented.
EN
The article presents the analysis of various kinds of montage applied in the analysis of electroencephalographic data. The present study covers analysis of data obtained as a result of EEG examination of several persons with the age of 23–24 years. Every person has undergone an examination with appliance of 21 electrodes in accordance with classic EEG record method applying the 10–20 system. The examined persons were asked to perform the following activities: sitting steadily with opened eyes, sitting steadily with closed eyes as well as typical cognitive activity i.e. silent reading of given text. Every fragment has been subjected to initial data analysis (filtering, artifact correction) and used for spectral analysis afterwards. The whole analysis has been realized on the basis of four EEG montage examples (two bipolar and two monopolar). For each of them comparative analysis concerning the following points has been carried out: EEG spectra as well as other measures, such as activity maps, diffraction histograms for separate waves and spectrum charts for the selected electrodes.
PL
Artykuł przedstawia analizę zastosowania różnego rodzaju montaży w analizie danych elektroencefalograficznych (EEG).Zaprezentowane studium przypadku obejmuje analizę danych z badania EEG kilku osób w wieku 23–24 lat. Każda osoba poddana została badaniu z wykorzystaniem 21 elektrod w klasycznym zapisie EEG z wykorzystaniem systemu 10–20. Osoby badane poproszone zostały o następujące aktywności: spokojne siedzenie z oczami otwartymi, zamkniętymi a także typową aktywność poznawczą – czytanie zadanego tekstu w myślach. Każdy fragment został poddany wstępnej analizie danych (filtracja, korekcja artefaktów) a następnie wykorzystany do analizy widmowej. Cała analiza została zrealizowana w oparciu o cztery przykładowe montaże EEG (dwa bipolarne i dwa monopolarne). Dla każdego z nich zrealizowano analizę porównawczą w odniesieniu do widm EEG a także innych miar, takich jak mapy aktywności, histogramy rozkładu poszczególnych fal oraz wykresy widmowe dla wybranych elektrod.
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