Wyniki wyszukiwania - BazTech

1

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.

2

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.

3

Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm

Karkosz Stanisław, Jukiewicz Marcin

Bio-Algorithms and Med-Systems

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2020

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Vol. 16, no. 2

art. no. 20200013

EN

Objectives: Optimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy. Methods: System of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI). Results: The designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects. Conclusions: It is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.

4

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.

5

Method for Clustering of Brain Activity Data Derived from EEG Signals

Kurowski Adam, Mrozik Katarzyna, Kostek Bozena, Czyzewski Andrzej

Fundamenta Informaticae

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2019

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Vol. 168, nr 2-4

249--268

EN

A method for assessing separability of EEG signals associated with three classes of brain activity is proposed. The EEG signals are acquired from 23 subjects, gathered from a headset consisting of 14 electrodes. Data are processed by applying Discrete Wavelet Transform (DWT) for the signal analysis and an autoencoder neural network for the brain activity separation. Processing involves 74 wavelets from 3 DWT families: Coiflets, Daubechies and Symlets. Euclidean distance between clusters normalized with respect to the standard deviation of the whole set of data are used to separate each task performed by participants. The results of this stage allow for an assessment of separability between subsets of data associated with each activity performed by experiment participants. The speed of convergence of the training process employing deep learning-based clustering is also measured.

6

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

Binias B., Palus H.

Measurement Automation Monitoring

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2015

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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.

7

Połączenie mózg-komputer jako metoda komunikacji z niereagującymi pacjentami - przegląd literatury

Kucharski P., Rybicki A., Kopaczyńska M.

Acta Bio-Optica et Informatica Medica. Inżynieria Biomedyczna

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2015

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Vol. 21, nr 3

148--157

PL

Artykuł dotyczy nowatorskiej i szybko rozwijającej się techniki, jaką są interfejsy mózg komputer (ang. Brain-Computer Interfaces, BCI). BCI umożliwiają zmianę sygnału bioelektrycznego mózgu na sygnał cyfrowy, który wysyłany jest do różnego rodzaju urządzeń, pozwalających sterować aplikacjami komputerowymi oraz sprzętem elektronicznym bez udziału mięśni. W pewnym sensie urządzenia te „odgadują” intencje użytkownika, a tym samym zwalniają go z konieczności wyrażania swych zamierzeń za pomocą gestów i ruchów. W pracy opisano zastosowania BCI w komunikacji z pacjentami znajdującymi się w stanie neurologicznymi, uniemożliwiającym kontaktowanie się ze światem zewnętrznym. Opisano inwazyjne i nieinwazyjne techniki obrazowania mózgu, takie jak: funkcjonalny rezonans magnetyczny (ang. Functional Magnetic Resonance Imaging, fMRI), funkcjonalna spektroskopia bliskiej podczerwieni (ang. Functional Near-Infrared Spectroscopy, fNIRS), elektroencefalografia (ang. Electroencephalography, EEG), elektro-kortykografia (ang. Electrocorticography, ECoG) i inne, które są stosowane obecnie w BCI. Pokazano wady i zalety opisywanych technik obrazowania medycznego. Przedstawiono także propozycje rozwiązań zwiększających efektywność wymienionych metod obrazowania w systemach BCI. Opisano przyszłe kierunki rozwoju systemów BCI. Mimo wielu zalet, interfejsów mózg-komputer, trzeba również brać pod uwagę aspekty bioetyczne.

EN

This paper describes an innovative technology known as Brain-Computer Interfaces (BCI). This method uses converts brain bioelectrical signals into digital forms these signals are used to interact with computer applications or devices like e.g. wheelchairs. Systems used for communication with patients whose neurological condition does not allow communication with the outside world, are described. Published results of studies, experiments and reports dealing with brain imaging techniques: either invasive such as Electrocorticography (ECoG) or noninvasive like Functional Magnetic Resonance (fMRI), Functional Near-Infrared Spectroscopy (fNIRS), Electroencephalography (EEG), are discussed. Advantages and disadvantages of described imaging techniques are presented along with solutions for better imaging. The future trends in the development of BCI are pointed out. The controversial aspects of BCI in respect to bioethical issues are discussed as well.

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Applying algorithms for inverse solutions in classifying EEG signals

Jagodzińska U.

Elektronika : konstrukcje, technologie, zastosowania

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2013

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Vol. 54, nr 9

144-147

EN

The article presents selected algorithms in inverse solutions in the EEG signal. When undertaking the calculations, it was assumed that the data obtained from electrodes on the surface of the head were preprocessed. As a result of using these algorithms it is possible to specify both the areas of the brain that the signals come from and the current density of the signals read by means of electrodes placed on the surface of the head. On the basis of knowing the solution to the inverse problem, an attempt was made to select the features of the signals. Then t-statistics was used to differentiate and order them.

PL

W artykule przedstawiono wybrane algorytmy rozwiązywania zagadnień odwrotnych w sygnale EEG. Przystępując do obliczeń założono, że dane uzyskane z elektrod rozmieszczonych na powierzchni głowy zostały wstępnie przetworzone. Wynikiem działania tych algorytmów jest lokalizacja obszarów mózgu, z których pochodzą sygnały oraz natężenia tych sygnałów odczytywanych za pomocą elektrod rozmieszczonych na powierzchni głowy. Znając rozwiązania zagadnienia odwrotnego podjęto też próbę selekcji cech. Wykorzystano t-statystykę do ich zróżnicowania i uszeregowania.

9

From cochlear implants to Brain-Computer Interfaces

Tadeusiewicz R., Rotter P.

Bio-Algorithms and Med-Systems

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2012

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Vol. 8, no. 3

267--286

EN

In this article two groups of technologies based on connecting a medical device to the human brain are presented. The first group exploits the existing nerves, like the cochlear implant where ear prosthesis is connected to the auditory nerve. Another group is based on a direct connection between an electronic device and the human brain and it is called Brain-Computer Interfaces. The article contains the description of these technologies, points out their current capabilities and limitations and the main barriers to further development. The authors indicate possible directions of future expansion of the discussed technologies.