Wyniki wyszukiwania - BazTech

1

Decoding human movement intentions from EEG signals using recurrent neural networks

Kleczyński Daniel, Kluska Jacek

Zeszyty Naukowe Politechniki Rzeszowskiej. Elektrotechnika

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2025

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Vol. 31

43--53

EN

The present study develops a model for recognizing movement intentions from Electroencephalography (EEG) signals using various Recurrent Neural Network (RNN) configurations, with a focus on Long Short-Term Memory (LSTM) networks. Experiments demonstrate that, with proper sample allocation and class balance, the model achieves an average accuracy of 0.9815±0.0025 and an average Receiver Operating Characteristic area (AUC) of 0.9989±0.0004 when training and test data include the same subjects. The best-performing LSTM model - augmented with a fully connected layer - was configured with a hidden layer size of 233, learning rate of 3.872 × 10−4, 3 layers, dropout of 0.3773, and sequence length of 457. However, when test subjects were completely excluded from training, the model’s accuracy did not exceed 50%, suggesting significant inter-subject variability or limitations in generalization. This work contributes to advancing Brain-Computer Interfaces for applications such as prosthetic control and provides insights into the prerequisites for effective EEG signal utilization.

2

Sztuczne inteligencje i biologiczne mózgi

Durka Piotr

Postępy Fizyki

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2025

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T. 76, z. 1

3--12

PL

Sztuczna inteligencja (ang. artificial intelligence, AI) to najgorętszy temat ostatnich lat, nie tylko w technologii. Jest wszędzie - od szczoteczek do zębów po artykuły naukowe. Pochłania setki miliardów dolarów, trzęsie giełdami, podważa wiarę w prawdziwość cyfrowych treści, halucynuje i karmi apokaliptyczne przepowiednie. Czym naprawdę jest AI? Czy zamiast Artificial Intelligence powinniśmy mówić o Alien Intelligence, jak sugeruje Yuval Noah Harari, czy raczej oczekiwać połączenia inteligencji białkowej z krzemową przez interfejsy mózg-komputer, razem z Raymondem Kurzweilem? Dlaczego wykorzystująca zdobycze nauki cywilizacja skręca nagle w stronę czarnych skrzynek i tajemniczych wyroczni? Spróbujemy określić, czym jest AI, i wyjaśnimy czym nie jest, demaskując po drodze kilka miejskich legend o podsłuchiwaniu myśli i przenoszeniu świadomości do cyberprzestrzeni. Omówimy też realne zagrożenia wynikające z faktu, że od od lat oddajemy algorytmom rząd dusz, ale nie zauważamy tego wsłuchani w opowieści o nadchodzącej „apokalipsie AI”.

EN

Artificial Intelligence (AI) is the hottest topic of recent years, and not only in technology. It is everywhere - from toothbrushes to scientific articles. It consumes hundreds of billions of dollars, shakes stock markets, undermines the credibility of digital content, hallucinates and feeds apocalyptic prophecies. What is AI really? Should we understand Artificial Intelligence as Alien Intelligence, as Yuval Noah Harari suggests, or rather expect biological intelligence to merge with silicon intelligence via brain-computer interfaces, together with Raymond Kurzweil? Why does our science-based civilization suddenly turn towards black boxes and mysterious oracles? We will try to define what AI is and explain what it is not, along the way debunking a few urban legends about eavesdropping on thoughts and transferring consciousness to cyberspace. We will also discuss the real threats resulting from the fact that for years we have been giving the reign of our souls to algorithms, but we do not notice it, listening to stories about the coming “AI apocalypse”.

3

Visuo-tactile stimulated virtual mirror therapy (ViTaS-VMT) system for enhancing motor-related brain activities: Application on two amputees

Song Minsu, Jee In-Hyeog, Kim Seungho, Lee Hansol, Lee Hyun-Joo, Chu Jun-Uk

Biocybernetics and Biomedical Engineering

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2025

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Vol. 45, iss. 2

212--228

EN

To enhance the degenerated brain signal of amputees on motor area, a visuo-tactile stimulated virtual mirror therapy system was developed. The system consists of a motion-tracking glove, a vibration motor, and a monitor-integrated table. The system can provide virtual hand illusion for body agency and combine visuo-tactile stimulation to induce body ownership on the virtual hand. The virtual hand then mimics the healthy hand like mirror therapy, and subjects perform grasping with both hands while observing the mirrored virtual hand on the amputated side. The training lasted three days, including the gradual exposure to the system to measure the difference in brain activity on the first day. We measured electroencephalogram (EEG) during training, and functional magnetic resonance imaging (fMRI) of grasping was measured before and after the training. Two amputees volunteered for this preliminary study. Both participants showed changes in motor-related brain activity, with consistent increases in event-related desynchronization (ERD) amplitude, particularly in the supplementary motor area (SMA) and primary motor cortex. These findings suggest the system’s potential to enhance motor-related neural processes. We believe that the results of this preliminary study have provided evidence that the proposed system can reproduce the learning process and that brain activation can be improved by using the system. Based on the results, a future study will expand the number of subjects and the duration of training to provide a quantitative clinical evaluation of the proposed system.

4

Toward the integration of mixed reality and brain-computer interfaces based on code-modulated visual evoked potentials

Moreno-Calderón Selene, Martínez-Cagigal Víctor, Martín-Fernández Ana, Santamaría-Vázquez Eduardo, Hornero Roberto

Biocybernetics and Biomedical Engineering

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2025

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Vol. 45, iss. 3

528--538

EN

Background and objective: Brain-computer interface (BCI) systems can assist individuals with severe motor disabilities by enabling communication through their brain signals using spellers, which allow selecting commands from a set of options. For this technology, accuracy, speed and user comfort are essential. Code-modulated visual evoked potentials (c-VEPs) have demonstrated promising performance in BCI control. Integrating BCI systems with mixed reality (MR) could provide portability and autonomy. However, to the best of our knowledge, no existing studies have explored the feasibility of combining MR with c-VEP-based BCIs. This study aims to: (1) evaluate the performance of integrating MR with c-VEP-based BCIs and (2) study the visual fatigue induced by c-VEPs compared to traditional screen. Methods: Twenty participants used a 36-character speller to select words in both MR and traditional screen conditions. Metrics like accuracy and information transfer rate (ITR) were measured. Usability and eyestrain were evaluated through questionnaires. Results: The integration of MR with c-VEPs achieved an accuracy of 96.71% and an ITR of 27.55 bits/min, compared to 95.98% accuracy and 27.10 bits/min for the conventional screen condition. The questionnaires revealed minimal levels of visual fatigue in both conditions and high usability. No significant differences were observed between conditions in terms of performance or visual fatigue. Conclusions: The c-VEP-based speller with MR-BCI technology proved feasible, achieving performance levels similar to the conventional setup, with high accuracy in both conditions. The study also found comparable visual fatigue between MR and traditional screens, supporting the practicality of MR integration in BCI systems.

5

Spatio-temporal matched filter adjustment for enhanced accuracy in brain responses classification

Piela Michal, Kotas Marian P.

Biocybernetics and Biomedical Engineering

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2025

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Vol. 45, iss. 1

34--51

EN

In this paper, we apply modified spatio-temporal matched filtering (MSTMF) to enhance electroencephalographic (EEG) signals in evoked potentials (EP) based brain-computer interfaces (BCI). Our focus is on the effective treatment of noise in the system under consideration. The applied MSTMF is a spatio-temporal extension of generalized matched filtering, which allows for optimal enhancement of weak, repeatable signals embedded in colored Gaussian noise. However, since spontaneous EEG signals are often corrupted by high-energy super-Gaussian artifacts, which deviate from this distribution, we propose rejecting these artifacts before applying MSTMF. Particularly effective have been algorithms based on independent component analysis (ICA) and empirical mode decomposition (EMD). After artifacts rejection, performed locally within time segments they occupy, without disturbing other parts of the signal, the classification of brain responses becomes more accurate. Nevertheless, the nonstationarity of the EEG signal remains a challenge that must be addressed. Therefore, we propose adjusting the MSTMF to the current noise properties to improve its performance in this demanding environment. This can be achieved by properly calculating the noise covariance matrix, which is necessary to determine the filter coefficients, using both the learning and currently processed signal segments. As a result, we have developed an enhanced method based on MSTMF for improved discrimination of evoked potentials and verified its performance on two publicly available reference databases: BCIAUT-P300 (for IFMBE Scientific Challenge) and Speller (for the BCI Competition III Challenge 2004). For these databases, we have achieved overall accuracies of 92.67% and 99.5%, surpassing the reference methods presented in the literature.

6

Reevaluating performance in c-VEP BCIs: The impact of calibration time

Martínez-Cagigal Víctor, Santamaría-Vázquez Eduardo, Pérez-Velasco Sergio, Martín-Fernández Ana, Hornero Roberto

Biocybernetics and Biomedical Engineering

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2025

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Vol. 45, iss. 4

685--696

EN

Code-modulated visual evoked potentials (c-VEP) have demonstrated high performance in non-invasive brain-computer interfaces (BCIs). Recently, research has begun to consider practical aspects such as visual comfort, where non-binary sequences and variations in the spatial frequency of stimuli play significant roles. However, calibration requirements remain underexplored in performance comparisons. This study aims to analyze a multi-variable tradeoff crucial to the practical application of c-VEP-based BCIs: decoding accuracy, decoding speed, and calibration time. Visual comfort is retrospectively evaluated using two pre-recorded datasets. Models were trained with increasing calibration cycles and tested across varying decoding times, depicting learning and decoding curves. The datasets comprised 32 healthy subjects, and featured different stimulus paradigms: plain non-binary stimuli and checkerboard-like binary stimuli with spatial frequency variations. Results showed that all conditions achieved over 97% grand-averaged accuracy with sufficient calibration. However, a clear tradeoff emerged between calibration duration and performance. Achieving 95% average accuracy within a 2 s decoding window required mean calibration durations of 28.7±19.0 s for binary stimuli, or 148.7±72.3 s for non-binary stimuli. The binary checkerboard-based condition with a spatial frequency of 1.2 c/º (C016) proved to be particularly effective, achieving over 95% accuracy within 2 s decoding window using only 7.3 s of calibration, and reporting a significant improvement in visual comfort. A minimum calibration time of 1 min was considered essential to adequately estimate the brain response, critical in template-matching paradigms. In conclusion, achieving optimal c-VEP performance requires balancing calibration duration, decoding speed and accuracy, and visual comfort.

7

A glimpse ahead: Forecasting 5.5-s human vigilance for enhanced safety in Industry 5.0

Cinquetti Ettore, Siviero Ilaria, Babiloni Fabio, Menegaz Gloria, Storti Silvia F.

Biocybernetics and Biomedical Engineering

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2025

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Vol. 45, iss. 2

258--268

EN

In the context of Industry 5.0 and human-robot interaction, ensuring the safety of operators by avoiding human errors is crucial. Monitoring vigilance decrement is an essential aspect of this effort, aimed at mitigating safety risks and enhancing productivity. A potentially promising solution to this challenge is using a passive brain-computer interface (BCI) based on electroencephalography (EEG) recordings. However, its application in industrial settings has yet to be explored in-depth. This study uses EEG data to introduce a novel experimental protocol and analysis pipeline to predict vigilance degradation in an industrial research laboratory. The dataset was gathered from ten healthy volunteers who observed a robotic arm for 23 min. The EEG power spectrum over time was computed using the continuous wavelet transform (CWT). After confirming growth in power for the α band using a linear regression model, we forecast its trend using four models. As a conventional approach, we used the vector autoregressive (VAR) model, serving as a reference for comparison with three deep learning architectures: a temporal convolutional network (TCN), a gated recurrent unit (GRU) and an encoder-decoder (ED)-GRU. The proposed ED-GRU model outperformed the others showing accurate forecasts (mean absolute error = 0.048, R2 = 0.726) up to 5.5 s. The findings suggest that monitoring vigilance degradation in Industry 5.0 is a feasible strategy to prevent human accidents and reduced performance during repetitive tasks.

8

Zastosowania sygnałów SSVEP w interfejsach mózg-komputer - wielowymiarowe podejście do interakcji człowiek-komputer

Piszcz Adrianna

Studia i Materiały Informatyki Stosowanej

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2024

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T. 16, nr 3

12--17

PL

Celem pracy jest przedstawienie różnorodnych zastosowań sygnałów SSVEP (Steady-State Visually Evoked Potentials) w interfejsach mózg-komputer (BCI). Interfejsy mózg-komputer oparte o SSVEP umożliwiają intuicyjne i wszechstronne sterowanie, otwierając nowe możliwości w takich dziedzinach jak obsługa urządzeń wspomagających, wirtualna i rozszerzona rzeczywistość, sterowanie inteligentnymi domami, gry i rozrywka. Omówione zostaną metody akwizycji i przetwarzania sygnałów EEG, algorytmy klasyfikacji oraz ich zastosowanie w różnych systemach. Wyniki badań wskazują na efektywność technologii SSVEP w dostarczaniu niezawodnych i precyzyjnych interakcji, co czyni ją kluczowym elementem w rozwoju nowoczesnych interfejsów użytkownika oraz systemów wspierających.

EN

This work aims to showcase the diverse applications of SSVEP (Steady-State Visually Evoked Potentials) signals in brain-computer interfaces (BCI). BCIs based on SSVEP allow for intuitive and versatile control, opening new possibilities in areas like assistive device control, virtual and augmented reality, smart home control, gaming, and entertainment. The paper will discuss methods for acquiring and processing EEG signals, classification algorithms, and their application in various systems. Research results demonstrate the effectiveness of SSVEP technology in providing reliable and precise interactions, making it a key element in the development of modern user interfaces and support systems.

9

Klasyfikacja sygnałów EEG w interfejsach mózg-komputer z wykorzystaniem sieci neuronowych

Galas Krzysztof

Studia i Materiały Informatyki Stosowanej

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2024

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T. 16, nr 3

18--23

PL

Celem pracy jest zaprezentowanie metod klasyfikacji sygnałów EEG w interfejsach mózg-komputer (BCI) z wykorzystaniem sieci neuronowych. Dzięki ich zdolności do modelowania złożonych zależności w danych, możliwe jest skuteczniejsze rozpoznawanie wzorców aktywności mózgowej, co przyczynia się do poprawy dokładności i szybkości działania systemów BCI. W pracy omówiono architektury sieci neuronowych wykorzystywane do analizy sygnałów EEG, takie jak sieci konwolucyjne (CNN) czy rekurencyjne (RNN). Badania pokazują, że te metody mają ogromny potencjał w zastosowaniach takich jak sterowanie urządzeniami wspomagającymi, komunikacja oraz rozrywka.

EN

The aim of this paper is to present methods for classifying EEG signals in brain-computer interfaces (BCIs) using neural networks. Thanks to their ability to model complex relationships in the data, it is possible to recognise patterns of brain activity more effectively, which contributes to improving the accuracy and speed of BCI systems. This paper discusses neural network architectures used to analyse EEG signals, such as convolutional networks (CNNs) or recurrent networks (RNNs). The research shows that these methods have immense potentialin applications such as assistive device control, communication,and entertainment.

10

Entropy-based feature extraction for classification of EEG signal using Lifting Wavelet Transform

Ananthi A., Subathra M.S.P., George S. Thomas, Sairamya N.J.

Przegląd Elektrotechniczny

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2024

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R. 100, nr 9

146--150

EN

In the realm of Brain-Computer Interface (BCI), a crucial hurdle lies in effectively classifying Motor Imagery (MI) signals. Numerous techniques have been developed for Electroencephalogram (EEG) signal-based MI classification. The proposed system transforms EEG signals into various representations through Lifting Wavelet Transform (LWT). Long Short Term Memory (LSTM) is employed for classifying the extracted feature vectors in each line. The performance of this method is evaluated on the PhysioNet database, specifically for distinguishing between right and left hand imagery move. The strategy,resulting in 100% accuracy in 19 out of 72 wavelet families of LWT. This combination proves to be a highly efficient tool for BCI-based EEG analysis, showcasing its potential as a resourceful solution in this domain.

PL

W obszarze interfejsu mózg-komputer (BCI) kluczową przeszkodą jest skuteczna klasyfikacja sygnałów obrazowania motorycznego (MI). Opracowano liczne techniki klasyfikacji MI na podstawie sygnału elektroencefalogramu (EEG). Proponowany system przekształca sygnały EEG na różne reprezentacje za pomocą transformacji falkowej Lifting Wavelet Transform (LWT). Pamięć długoterminowa Long Short Term Memory (LSTM) jest wykorzystywana do klasyfikowania wyodrębnionych wektorów cech w każdej linii. Wydajność tej metody jest oceniana w bazie danych PhysioNet, w szczególności w celu rozróżnienia ruchu obrazowania prawej i lewej ręki. Strategia ta zapewnia 100% dokładność w 19 z 72 rodzin falek LWT. Ta kombinacja okazuje się wysoce wydajnym narzędziem do analizy EEG opartej na BCI, pokazując swój potencjał jako zasobnego rozwiązania w tej dziedzinie.

11

A linear-attention-combined convolutional neural network for EEG-based visual stimulus recognition

Huang Junjie, Chen Wanzhong, Zhang Tao

Biocybernetics and Biomedical Engineering

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2024

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Vol. 44, no. 2

369--379

EN

The recognition task of visual stimuli based on EEG (Electroencephalogram) has become a major and important topic in the field of Brain-Computer Interfaces (BCI) research. Although the underlying spatial features of EEG can effectively represent visual stimulus information, it still remains a highly challenging task to explore the local-global information of the underlying EEG to achieve better decoding performance. Therefore, in this paper we propose a deep learning architecture called Linear-Attention-combined Convolutional Neural Network (LACNN) for visual stimuli EEG-based classification task. The proposed architecture combines the modules of Convolutional Neural Networks (CNN) and Linear Attention, effectively extracting local and global features of EEG for decoding while maintaining low computational complexity and model parameters. We conducted extensive experiments on a public EEG dataset from the Stanford Digital Repository. The experimental results demonstrate that LACNN achieves an average decoding accuracy of 54.13% and 29.83% in 6-category and 72-exemplar classification tasks respectively, outperforming the state-of-the-art methods, which indicates that our method can effectively decode visual stimuli from EEG. Further analysis of LACNN shows that the Linear Attention module improves the separability between different category features and localizes key brain region information that aligns with the paradigm principles.

12

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

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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.

13

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

14

The quantitative application of channel importance in movement intention decoding

Wang Linlin, Li Mingai

Biocybernetics and Biomedical Engineering

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2022

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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.

15

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

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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.

16

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

|

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.

17

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

Piszcz Adrianna

Studia i Materiały Informatyki Stosowanej

|

2021

|

T. 13, 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.

18

Ocular artifact elimination from electroencephalography signals: A systematic review

Ranjan Rakesh, Chandra Sahana Bikash, Kumar Bhandari Ashish

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 3

960--996

EN

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

19

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.

20

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.