Wyniki wyszukiwania - BazTech

1

Decoding motor imagery based on dipole feature imaging and a hybrid CNN with embedded squeeze-and-excitation block

Wang Linlin, Li Mingai

Biocybernetics and Biomedical Engineering

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2023

|

Vol. 43, no. 4

751--762

EN

Motor imagery (MI) decoding is the core of an intelligent rehabilitation system in brain computer interface, and it has a potential advantage by using source signals, which have higher spatial resolution and the same time resolution compared to scalp electroencephalography (EEG). However, how to delve and utilize the personalized frequency characteristic of dipoles for improving decoding performance has not been paid sufficient attention. In this paper, a novel dipole feature imaging (DFI) and a hybrid convolutional neural network (HCNN) with an embedded squeeze-and-excitation block (SEB), denoted as DFI-HCNN, are proposed for decoding MI tasks. EEG source imaging technique is used for brain source estimation, and each sub-band spectrum powers of all dipoles are calculated through frequency analysis and band division. Then, the 3D space information of dipoles is retrieved, and by using azimuthal equidistant projection algorithm it is transformed to a 2D plane, which is combined with nearest neighbor interpolation to generate multi sub-band dipole feature images. Furthermore, a HCNN is designed and applied to the ensemble of sub-band dipole feature images, from which the importance of sub-bands is acquired to adjust the corresponding attentions adaptively by SEB. Ten-fold cross-validation experiments on two public datasets achieve the comparatively higher decoding accuracies of 84.23% and 92.62%, respectively. The experiment results show that DFI is an effective feature representation, and HCNN with an embedded SEB can enhance the useful frequency information of dipoles for improving MI decoding.

2

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

3

The quantitative application of channel importance in movement intention decoding

Wang Linlin, Li Mingai

Biocybernetics and Biomedical Engineering

|

2022

|

Vol. 42, no. 2

630--645

EN

The complex brain network consists of multiple collaborative regions, which can be activated to varying degrees by motor imagery (MI) and the induced electroencephalogram (EEG) recorded by an array of scalp electrodes is usually decoded for driving rehabilitation system. Either all channels or partially selected channels are equally applied to recognize movement intention, which may be incompatible with the individual differences of channels from different locations. In this paper, a channel importance based imaging method is proposed, denoted as CIBI. For each electrode of MI-EEG, the power over 8–30 Hz band is calculated from discrete Fourier spectrum and input to random forest algorithm (RF) to quantify its contribution, namely channel importance (CI); Then, CI is used for weighting the powers of α and β rhythms, which are interpolated to a 32 x 32 grid by using Clough-Tocher method respectively, generating two main band images with time-frequency-space information. In addition, a dual branch fusion convolutional neural network (DBFCNN) is developed to match with the characteristic of two MI images, realizing the extraction, fusion and classification of comprehensive features. Extensive experiments are conducted based on two public datasets with four classes of MI-EEG, the relatively higher average accuracies are obtained, and the improvements achieve 23:95% and 25:14% respectively when using channel importance, their statistical analysis are also performed by Kappa value, confusion matrix and receiver operating characteristic. Experiment results show that the personalized channel importance is helpful to enhance inter-class separability as well as the proposed method has the outstanding decoding ability for multiple MI tasks.

4

Non-linear optimized spatial filter for single-trial identification of movement related cortical potential

Mascolini Alessio, Niazi Imran Khan, Mesin Luca

Biocybernetics and Biomedical Engineering

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2022

|

Vol. 42, no. 1

426--436

EN

To investigate the optimal filter settings for pre-processing of Movement Related Cortical Potentials (MRCP) for the detection through EEG in single trial, we have proposed a novel Non-Linear Optimized Spatial Filter (NL-SF) and compared it to the Optimized Spatial Filtering (OSF) used in literature. MRCPs from EEG recordings are emphasized, calculating the optimal non-linear combination of channels which isolates the signal of interest. The method is applied to EEG data recorded from 16 healthy patients either executing or imagining 50 self-paced upper limb movements (palmar grasp). MRCPs have been identified from the outputs of the two filters by matching with a template built by averaging responses to movement intentions in the training set. NL-SF had a median accuracy on the overall dataset of 84.6%, which is significantly better than that of OSF (i.e., 76.9%). Being a filter and feasible for self-paced applications, it could be of interest in online BCI system design.

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

|

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

BCI w VR: imersja sposobem na sprawniejsze wykorzystywanie interfejsu mózg-komputer

Piszcz Adrianna

Studia i Materiały Informatyki Stosowanej

|

2021

|

nr 1

5--10

PL

Celem eksperymentów było zbadanie czy rzeczywistość wirtualna usprawnia korzystanie z interfejsu mózg-komputer. Do badania wykorzystano autorski system informatyczny, który umożliwia rysowanie kształtów na ekranie komputera. Przygotowane stanowisko badawcze składa się z komputera z niezbędnym oprogramowaniem, z mobilnych gogli wirtualnej rzeczywistości Esperanza EMV300 ze smartfonem Samsung Galaxy A40 oraz interfejsu mózg-komputer Emotiv Epoc. Wykazano, że imersja pozwala zwiększyć poziom koncentracji i sprawniej korzystać z interfejsu mózg-komputer. Taki rodzaj zanurzenia w rzeczywistość wirtualną może zapoczątkować całą serię aplikacji obsługiwanych w sposób intuicyjny, za pomocą komend myślowych, w wykreowanym wirtualnym świecie.

EN

The purpose of the experiments was to investigate whether virtual reality improves the use of the brain-computer interface. The study used a custom computer system that allows drawing shapes on the computer screen. The prepared test stand consists of a computer with the necessary software, Esperanza EMV300 mobile virtual reality goggles with a Samsung Galaxy A40 smartphone and Emotiv Epoc braincomputer interface. It was shown that immersion allows to increase the level of concentration and use the brain-computer interface more efficiently. This kind of immersion in virtual reality could initiate a whole series of applications operated intuitively, via thought commands, in a created virtual world.

7

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

|

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.

8

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.

9

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

|

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.

10

Detection of eye closing/opening from EOG and its application in robotic arm control

Sharma Kamal, Jain Neeraj, Pal Prabir K.

Biocybernetics and Biomedical Engineering

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2020

|

Vol. 40, no. 1

173--186

EN

Detection of eye closing/opening from alpha-blocking in the EEG of occipital region has been used to build human-machine interfaces. This paper presents an alternative method for detection of eye closing/opening from EOG signals in an online setting. The accuracies for correct detection of eye closing and opening operations with the proposed techniques were found to be 95.6% and 91.9% respectively for 8 healthy subjects. These techniques were then combined with the detection of eye blinks, the accuracy of which turned out to be 96.9%. This was then used to build an interface for robotic arm control for a pick and place task. The same task was also carried out using a haptic device as a master. The speed and accuracy for these two methods were then compared to assess quantitatively the ease of using this interface. It appears that the proposed interface will be very useful for persons with neurodegenerative disorders who can perform eye closing/opening and eye blinks.

11

Robotic orthosis compared to virtual hand for Brain–Computer Interface feedback

Cantillo-Negrete Jessica, Carino-Escobar Ruben I., Carrillo-Mora Paul, Barraza-Madrigal José A., Arias-Carrión Oscar

Biocybernetics and Biomedical Engineering

|

2019

|

Vol. 39, no. 2

263--272

EN

Brain–Computer Interfaces (BCI) allow the control of external devices by decoding the users' intentions from their central nervous system. Feedback, one of the main elements of a closed- loop BCI, is used to enhance the user's performance. The present work aimed to compare the effect of two different feedback sources; congruent anatomical visual hand representation and passive hand movement on BCI performance and cortical activations. Electroencephalography of 12 healthy right-handed subjects was recorded to set a BCI activated by right-hand motor imagery. Afterward, the subjects were asked to control the system by imagining the movement. The system provided either visual feedback, shown on a computer screen or kinesthetic feedback, provided by a robotic hand orthosis. Differences in performance and cortical activations were assessed, using classification accuracy and event-related desynchronization/synchronization in μ and β bands, respectively. Performance was significantly better with kinesthetic feedback as it allowed for higher correct classification of motor imagery. Cortical activations in the ipsilateral central channel in μ were different between the two feedback modalities. Our results imply that healthy subjects can achieve a greater degree of control using a motor imagery-based BCI with kinesthetic feedback than with anatomically congruent visual feedback. Furthermore, cortical activation differences show that kinesthetic feedback seems to elicit higher recruitment of sensorimotor cortex brain cells, which probably reflects enhanced local information modulation related to fine motor processing. Therefore, kinesthetic feedback provided by a robotic orthosis could be a more suitable feedback strategy for BCI systems designed for neuromodulation and neurorehabilitation.

12

P300 based character recognition using sparse autoencoder with ensemble of SVMs

Kundu Sourav, Ari Samit

Biocybernetics and Biomedical Engineering

|

2019

|

Vol. 39, no. 4

956--966

EN

In this study, a brain–computer interface (BCI) system known as P300 speller is used to spell the word or character without any muscle activity. For P300 signal classification, feature extraction is an important step. In this work, deep feature learning techniques based on sparse autoencoder (SAE) and stacked sparse autoencoder (SSAE) are proposed for feature extraction. Deep feature provides the abstract information about the signal. This work proposes fusion of deep features with the temporal features, which provides abstract and temporal information about the EEG signal. These deep feature and temporal feature are partially complement of each other to represent the EEG signal. For classification of the EEG signal, an ensemble of support vector machines (ESVM) is adopted as it helps to reduce the classifiers variability. In classifier ensemble system, the score of individual classifier is not at the same level. To transform these scores into a common level, min–max normalization is proposed prior to combining them. Min-max normalization scales the classifiers' score between 0 and 1. The experiments are conducted on three standard public datasets, dataset IIb of BCI Competition II, dataset II of the BCI Competition III and BNCI Horizon dataset. The experimental results show that the proposed method yields better or comparable performance compared to earlier reported techniques.

13

Extracting multiple commands from a single SSVEP flicker using eye-accommodation

Sharma Kamal, Kar Soumitra

Biocybernetics and Biomedical Engineering

|

2019

|

Vol. 39, no. 3

914--922

EN

The steady-state visually evoked potential (SSVEP) based brain-computer interfaces (BCIs) generally deploy flickering stimuli with different frequencies in order to generate different commands. This paper presents a setup that can be used to generate multiple commands from a single flickering stimulus using magnitude modulation of SSVEP through eye-accommodation. In this setup, a flickering stimulus was shown on the computer screen and a passive fixation target was placed between the screen and the subject. The eye-accommodation mechanism to focus on the target between the screen and the subject, caused the flickering stimulus to become blurred which reduced the magnitude of the evoked SSVEP response. The reduced magnitude SSVEP response can be used to generate another command over the command generated when the subject focuses directly on the stimulus. The fixation target was placed at 3 different positions that can provide up to 4 commands from the single flicker stimulus. Fifteen healthy human subjects participated in the experiments. The mean offline accuracies obtained for 2-class, 3-class, and 4-class extraction were 100%, 94.2 ± 6.1%, and 80.9 ± 9.7% respectively for a 4-seconds time window.

14

Sterowanie modelem pojazdu za pomocą interfejsu mózg-komputer

Derdziński Marek, Mikołajewski Dariusz, Łukowski Janusz

Studia i Materiały Informatyki Stosowanej

|

2018

|

nr 2

17--23

PL

Celem pracy było zbudowanie układu sterowania prostym modelem pojazdu za pomocą interfejsu mózg-komputer (ang. brain computer interface - BCI). Omówiono zasadę działania BCI oraz wykorzystanie BCI w mechatronice, w tym na potrzeby interdyscyplinarnych badań kognitywistycznych (nauk o poznaniu). W dalszej części pracy Autorzy skupili się na opisie modelu, który posłużył do przeprowadzenia badania, ze szczególny uwzględnieniem współdziałania BCI oraz Arduino. Czwarta część pracy dotyczy badania działania zbudowanego rozwiązania technicznego przeprowadzonego na grupie osób w wieku 8-54 lat.

EN

This artilce aims at consctruction of the brain-computer interface (BCI) - based control system of the car model. Article decribes BCI's rules of operation and BCI applications in mechatronics, including interdisciplinary cognitive sciences. Further part of the article is focused on description of the model used in the research, particularly on BCI-Arduino cooperation. The last part of the article shows research on subjects aged 8-54 years concerning BCI use to control car model..

15

Interfejsy mózg-komputer w sterowaniu urządzeniami i systemami mechatronicznymi

Mikołajewski Dariusz, Tomaszewska Ewa, Karczmarek Mariusz

Studia i Materiały Informatyki Stosowanej

|

2018

|

nr 2

4--9

PL

Interfejsy mózg-komputer ustanowiły przełom w rozwoju współczesnych neuronauk i neurorehabilitacji. Niniejszy artykuł stanowi przegląd części technologii interfejsów mózg-komputer ukierunkowanej na sterowanie urządzeniami i systemami mechatronicznymi. Opisane zostały zarówno podstawowe rozwiązania z obszaru samych interfejsów, jak i przedyskutowane technologie mogące zapewnić sygnały sterujące dla urządzeń mechatronicznych. Pomimo ciągłego rozwoju problematyki wiele kwestii jest nierozwiązanych w zakresie udoskonalenia samych interfejsów oraz sklasyfikowania sygnałów sterujących

EN

Brain-computer interfaces (BCIs) have begun to constitute the another breakthrough in contemporary neuroscience and neurorehabilitation. This paper provides an overview of brain-computer interfaces (BCIs) technology that aims to address the priorities for control of mechatronic devices and systems. We describe basic solutions in the area of BCIs and discuss technologies that may provide command signals for mechatronic devices. Despite continuous development of the topic there still remains room for improvement, including future interfaces and control signal classification enhancements.

16

Interfejs mózg-komputer jako moduł mechatroniczny

Tomaszewska Ewa, Karczmarek Mariusz, Mikołajewski Dariusz

Studia i Materiały Informatyki Stosowanej

|

2018

|

nr 2

10--16

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.

17

The use of electroencephalography as a future-oriented brain-computer interface : current concepts, solutions, technologies that allow communication through thoughts

Sosnowski K.

Computer Science and Mathematical Modelling

|

2018

|

No. 8

47--52

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.

18

Development of a practical high frequency brain–computer interface based on steady-state visual evoked potentials using a single channel of EEG

Ajami S., Mahnam A., Abootalebi V.

Biocybernetics and Biomedical Engineering

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2018

|

Vol. 38, no. 1

106--114

EN

Brain–computer interfaces based on steady-state visual evoked potentials have recently gained increasing attention due to high performance and minimal user training. Stimulus frequencies in the range of 4–60 Hz have been used in these systems. However, eye fatigue when looking at low-frequency flickering lights, higher risk of induced epileptic seizure for medium-frequency flickers, and low signal amplitude for high-frequency flickers complicate appropriate selection of flickering frequencies. Here, different flicker frequencies were evaluated for development of a brain–computer interface speller that ensures user's comfort as well as the system's efficiency. A frequency detection algorithm was also proposed based on Least Absolute Shrinkage and Selection Operator estimate that provides excellent accuracy using only a single channel of EEG. After evaluation of the SSVEP responses in the range of 6–60 Hz, three stimulus frequency sets of 30–35, 35–40 and 40–45 Hz were adopted and the system's performance and corresponding eye fatigue were compared. While the accuracy of the asynchronous speller for all three stimulus frequency sets was close to the maximum (average 97.6%), repeated measures ANOVA demonstrated that the typing speed for 30–35 Hz (8.09 char/min) and 35–40 Hz (8.33 char/min) are not significantly different, but are significantly higher than for 40–45 Hz (6.28 char/min). On the other hand, the average eye fatigue scale for 35–40 Hz (80%) is comparable to that for 40–45 Hz (85%), but very higher than for 30–35 Hz (60%). Therefore, 35–40 Hz range was proposed for the system which resulted in 99.2% accuracy and 67.1 bit/min information transfer rate.

19

Sposoby generowania stymulacji wywołujących SSVEP z zastosowaniem monitorów LCD

Zawiślak T.

Zeszyty Naukowe Wydziału Elektroniki i Informatyki Politechniki Koszalińskiej

|

2017

|

Nr 11

123--131

PL

Potencjał stanu ustalonego (SSVEP - ang. Steady State Visually Evoked Potential) to odpowiedź mózgu na obserwowane stymulacje świetlne pojawiające się ze stałą częstotliwością. Podczas tego zjawiska w sygnale EEG (Elektroencefalogram) odbieranym z powierzchni czaszki w okolicach kory wzrokowej następuje znaczny wzrost mocy sygnału w częstotliwości z jaką pojawia się bodziec świetlny. W eksperymentach badających to zjawisko oraz interfejsach mózg-komputer (ang. BCI - Brain Computer Interface) bazujących na nim, stosuje się różne rozwiązania do wysyłania stymulacji. Wiodącymi metodami jest zastosowanie układów ze źródłem światła wykorzystującym diody elektroluminescencyjne (LED) lub wykorzystanie ekranów monitorów komputerowych (CRT, LCD). Niniejszy artykuł zawiera opis problemu oraz przegląd metod wykorzystywanych do wywoływania stymulacji na ekranie monitora.

EN

The Steady State Visually Evoked Potential (SSVEP) is the brain's response to the observed light stimulation occurring at a constant frequency. During this phenomenon in the EEG (Electroencephalogram) signal received from the skull surface near the visual cortex there is a significant increase in signal strength in the frequency with which the light stimulus appears. In experiments investigating this phenomenon as well as in Brain Computer Interfaces (BCI) based on it, various solutions are used to send stimulation. The leading methods are the use of systems with a light source using electroluminescent diodes (LED) or the use of computer screens (CRT, LCD). This article contains a description of the problem and an overview of the methods used to stimulate the monitor screen.

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A low-budget stimulation system for evoking SSVEP built on Arduino/Genuino platform

Rejer I., Zawiślak T.

Przegląd Elektrotechniczny

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2017

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R. 93, nr 1

112--116

EN

Steady State Visual Evoke Potentials (SSVEPs) are responses of a human brain to outside periodical stimulations. Their particular feature is the fact that the frequency of brain response is the same as the stimulation frequency. This does not mean that SSVEP appears with any stimulation frequency. First of all, the stimulation frequencies evoking SSVEPs are subject-depended, and hence the same stimulation frequency can evoke a prominent SSVEP for one subject, and nothing at all for another one. Second, to evoke the brain response, the stimulus has to be strong enough and has to be delivered with a steady frequency. With brain-computer interfaces (BCIs), using SSVEPs as control signals, often the problem is how to provide a set of stimuli capable of evoking a large number of brain responses. In this paper a proposition of a low cost stimulation system delivering light stimuli is presented. The paper presents both, the structure of the proposed platform and the test results obtained with a real subject. 85 stimulation frequencies from 5 to 31.25Hz were tested during the experiment and for 47 of them the prominent SSVEPs were obtained.

PL

Wywołany potencjał wzrokowy stanu ustalonego (SSVEP) to odpowiedź ludzkiego mózgu na zewnętrzną okresowo pojawiającą się stymulację. Szczególną cechą tego rodzaju potencjałów jest fakt, że częstotliwość odpowiedzi jest taka sama jak częstotliwość bodźca. To nie oznacza jednak, że potencjał SSVEP wystąpi przy każdej częstotliwości bodźca. Po pierwsze, częstotliwości wywołujące SSVEP są zależne od indywidualnych cech badanego podmiotu Po drugie, aby wywołać odpowiedź mózgu, bodźce muszą być odpowiednio silne i muszą być dostarczane ze stałą częstotliwością. Jednym z problemów, który można napotkać w trakcie realizacji interfejsów mózg-komputer wykorzystujących SSVEP jako sygnały sterujące jest właśnie problem dokładnego generowania bodźców w jak największym zakresie częstotliwości. Niniejszy artykuł przedstawia propozycję nisko budżetowego systemu do generowania stymulacji świetlnych, który może zostać zastosowany w interfejsie mózgkomputer. W artykule przedstawiono zarówno sposób budowy systemu, jak i wyniki otrzymane w eksperymencie z rzeczywistym podmiotem. W trakcie eksperymentu wygenerowano 85 sekwencji bodźców o różnej częstotliwości stymulacji (w zakresie od 5 do 31.25 Hz). Dla 47 sekwencji bodźców uzyskano prawidłową odpowiedź mózgu (SSVEP).