Wyniki wyszukiwania - BazTech

1

Using high-tech tools for consumer buying decisions of FMCG

Wiścicka-Fernando Małgorzata, Biercewicz Konrad

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2023

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z. 174

387--402

EN

Purpose: The main aim of this paper is to explore consumer decisions and emotions during shopping at the self-service store with fast-moving consumer goods (FMCG). Design/methodology/approach: The subject of the study is to assess the impact of emotions during the choice-making process on consumers' buying decisions. The respondents are citizens of the West Pomeranian region, Poland. The survey was conducted using state-of-the-art data acquisition technologies, i.e., Virtual Reality and EEG. An interview was also used as a complementary form. The research was both qualitative and quantitative, with a research sample of 34 respondents and took place in the virtual world. The researchers used primary data. The results presented here are part of a broader research project that used a triangulation of research methods to allow a deeper analysis of the conscious and unconscious aspects of the subjects. Findings: The research provided independent data on consumer emotions. The authors identified 4 groups of emotions that appeared during the selection of a product and were highly differentiated and strongly dependent on such characteristics as consumer type and gender. It has also been noticed that the longer a product is held, the lower emotional “sleepiness’. Research limitations/implications: One of the main limitations is the data collection process, which is relatively expensive, so the sample size is limited. The results obtained can be a signpost for a researcher who would like to use this new technology for further research. Practical implications: The results obtained can be used by shop managers in planning the sales activities or shop space to help the customer decide. Originality/value: In the research was used an innovative combination of virtual reality (VR) equipment and an electroencephalogram (EEG). To the best of the authors' knowledge, the results of a study from the FMCG industry using both devices simultaneously have never been published.

2

Patient specific channel optimization using entropy and CNN deep learning for epileptic seizure prediction

Cherouati Brahim, Senouci Mohamed

Przegląd Elektrotechniczny

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2023

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R. 99, nr 7

106--110

EN

Predicting epileptic seizures in advance improves greatly the life of epileptic patients. In this paper we present a new approach based on patient specific channel optimization using four different features namely entropy, variance, kurtosis and skewness. After selecting three best channels for each method, we then use Convolutional Neural Network (CNN) to classify raw EEG signal in order to discriminate between interictal and preictal state. With entropy, our method achieves a good degree of prediction in terms of accuracy 97.09%, sensitivity 97.67% and specificity 96.51% for patient 01 using channels 4, 8 and 20.

PL

Przewidywanie napadów padaczkowych z wyprzedzeniem znacznie poprawia życie chorych na padaczkę. W tym artykule prezentujemy nowe podejście oparte na optymalizacji kanałów specyficznych dla pacjenta przy użyciu czterech różnych metod, a mianowicie entropii, wariancji, kurtozy i skośności. Po wybraniu trzech najlepszych kanałów dla każdej z metod, wykorzystujemy Neuronową Sieć Konwolucyjną (CNN) do klasyfikacji surowego sygnału EEG w celu rozróżnienia pomiędzy stanem międzynapadowym i przednapadowym. Dzięki entropii nasza metoda osiąga dobry stopień predykcji w zakresie dokładności 97,09%, czułości 97,67% i specyficzności 96,51% dla pacjenta 01 przy użyciu kanałów 4, 8 i 20.

3

Outlier detection in EEG signals

Duraj Agnieszka, Chomątek Łukasz

Przegląd Elektrotechniczny

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2023

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R. 99, nr 1

237--240

EN

In this paper, the topic of detection of outliers in EEG signals was discussed, which facilitates making decisions about the diagnosis of a patient based on this study. We used two methods to detect outliers: the support vector machine and the k nearest neighbors method. The experiments were performed on a publicly available dataset containing EEG test results for 500 patients. The obtained results showed that the methods we used allow for the outlier detection efficiency at the level of 93%.

PL

W niniejszej pracy podjęto temat detekcji wyjątków w sygnałach EEG, co pozwala na ułatwienie podejmowania decyzji co do diagnozy pacjenta na podstawie tego badania. Do detekcji wyjątków wykorzystaliśmy dwie metody: maszynę wektorów nośnych i metodę k najblizszych sąsiadów. Eksperymenty zostały przeprowadzone na ogólnodostępnym zbiorze danych zawieraj ącym wyniki badania EEG dla 500 pacjentów. Uzyskane wyniki pokazały, że u żyte przez nas metody pozwalają na uzyskanie skuteczności detekcji wyjątków na poziomie 93%.

4

An improved performance of support vector machine to classify EEG motor imagery based on differential asymmetry

Putranto Yulianto Tejo, Putra Oddy Virgantara, Hafidz Isa, Sardjono Arief, Hariadi Mochamad, Putranto Yulianto Tejo

Przegląd Elektrotechniczny

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2023

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R. 99, nr 6

196--203

EN

One challenge in EEG motor imaging is th e low signal-to-noise ratio of brain signals. Its emergence in the accurate rendition of brain signals varies significantly from person to person. Here, we propose a framework to classify tasks based on fusion features using a Support Vector Machine. Our features are acquired from Discrete Wavelet Transform and Empirical Mode Decomposition. Subsequently, the disparity between measurements of left and right brain signals was calculated. Our proposed work significantly improves accuracy from 83.29 % to 93.16 % compared to previous work.

PL

Jednym z wyzwań w obrazowaniu motorycznym EEG jest niski stosunek sygnału do szumu sygnałów mózgowych. Jego pojawienie się w dokładnym przekazywaniu sygnałów mózgowych różni się znacznie w zależności od osoby. Tutaj proponujemy ramy do klasyfikowania zadań w oparciu o funkcje fuzji przy użyciu maszyny wektorów nośnych. Nasze funkcje są uzyskiwane z dyskretnej transformacji falkowej i dekompozycji trybu empirycznego. Następnie obliczono rozbieżność między pomiarami sygnałów lewego i prawego mózgu. Nasza proponowana praca znacznie poprawia dokładność z 83,29% do 93,16% w porównaniu z poprzednią pracą.

5

EEG signal analysis for monitoring concentration of operators

Rykała Łukasz

Journal of Automation Mobile Robotics and Intelligent Systems

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2023

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Vol. 17, No. 1

30--39

EN

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

6

Detection of epileptic seizures in EEG by using machine learning techniques

AL-Huseiny Muayed S., Sajit Ahmed S.

Diagnostyka

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2023

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Vol. 24, No. 1

art. no. 2023108

EN

In this research a public dataset of recordings of EEG signals of healthy subjects and epileptic patients was used to build three simple classifiers with low time complexity, these are decision tree, random forest and AdaBoost algorithm. The data was initially preprocessed to extract short waves of electrical signals representing brain activity. The signals are then used for the selected models. Experimental results showed that random forest achieved the best accuracy of detection of the presence/absence of epileptic seizure in the EEG signals at 97.23% followed by decision tree with accuracy of 96.93%. The least performing algorithm was the AdaBoost scoring accuracy of 87.23%. Further, the AUC scores were 99% for decision tree, 99.9% for random forest and 95.6% for AdaBoost. These results are comparable to state-of-the-art classifiers which have higher time complexity.

7

Hybrid implementation of the fastICA algorithm for high-density EEG using the capabilities of the intel architecture and CUDA programming

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

Computer Science

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2023

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T. 24 (4)

455--472

EN

High-density electroencephalographic (EEG) systems are utilized in the study of the human brain and its underlying behaviors. However, working with EEG data requires a well-cleaned signal, which is often achieved through the use of independent component analysis (ICA) methods. The calculation time for these types of algorithms is the longer the more data we have. This article presents a hybrid implementation of the fastICA algorithm that uses parallel programming techniques (libraries and extensions of the Intel processors and CUDA programming), which results in a significant acceleration of execution time on selected architectures.

8

Identifying epileptic EEGs and congestive heart failure ECGs under unified framework of wavelet scattering transform, bidirectional weighted (2D)2PCA and KELM

Zhang Tao, Chen Wanzhong, Chen Xiaojuan

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 1

279--297

EN

In order to achieve the accurate identifications of various electroencephalograms (EEGs) and electrocardiograms (ECGs), a unified framework of wavelet scattering transform (WST), bidirectional weighted two-directional two-dimensional principal component analysis (BW(2D)2PCA) and grey wolf optimization based kernel extreme learning machine (KELM) was put forward in this study. To extract more discriminating features in the WST domain, the BW(2D)2PCA was proposed based on original two-directional two-dimensional principal component analysis, by considering both the contribution of eigenvalue and the variation of two adjacent eigenvalues. Totally fifteen classification tasks of classifying normal vs interictal vs ictal EEGs, non-seizure vs seizure EEGs and normal vs congestive heart failure (CHF) ECGs were investigated. Applying patient non-specific strategy, the proposed scheme reported ACCs of no less than 99.300 ± 0.121 % for all the thirteen classification cases of Bonn dataset in classifying normal vs interictal vs ictal EEGs, MCC of 90.947 ± 0.128 % in distinguishing non-seizure vs seizure EEGs of CHB-MIT dataset, and MCC of 99.994 ± 0.001 % in identifying normal vs CHF ECGs of BBIH dataset. Experimental results indicate BW(2D)2PCA based framework outperforms (2D)2PCA based scheme, the high-performance results manifest the effectiveness of the proposed framework and our proposal is superior to most existing approaches.

9

Effects of sampling rate on multiscale entropy of electroencephalogram time series

Zheng Jinlin, Li Yan, Zhai Yawen, Zhang Nan, Yu Haoyang, Tang Chi, Yan Zheng, Luo Erping, Xie Kangning

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 1

233--245

EN

A physiological system encompasses numerous components that function at various time scales. To characterize the scale-dependent feature, the multiscale entropy (MSE) analysis has been proposed to describe the complex processes on multiple time scales. However, MSE analysis uses the relative scale factors to reveal the time-related dynamics, which may cause in-comparability of results from diverse studies with inconsistent sampling rates. In this study, in addition to the conventional MSE with relative scale factors, we also expressed MSE with absolute time scales (MaSE). We compared the effects of sampling rates on MSE and MaSE of simulated and real EEG time series. The results show that the previously found phenomenon (down-sampling can increase sample entropy) is just the projection of the compressing effect of down-sampling on MSE. And we have also shown the compressing effect of down-sampling on MSE does not change MaSE’s profile, despite some minor right-sliding. In addition, by analyzing a public EEG dataset of emotional states, we have demonstrated improved classification rate after choosing appropriate sampling rate. We have finally proposed a working strategy to choose an appropriate sampling rate, and suggested using MaSE to avoid confusion caused by sampling rate inconsistency. This novel study may apply to a broad range of studies that would traditionally utilize sample entropy and MSE to analyze the complexity of an underlying dynamic process.

10

Neurosciences in architecture. Applied research and its potential in architectural design

Krauze Weronika, Motak Maciej

Teka Komisji Urbanistyki i Architektury Oddział PAN w Krakowie

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2022

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T. 50

331--356

EN

The aim of this paper is to review and introduce neuroscience research whose results offer the possibility or potential possibility for use in the discipline of architecture. This study is a proposal for a substantive introduction to systematics and a detailed description of the use of particular research methods at each stage of the design process. The article discusses necessary definitions and a historical outline of the interdiscipline, which was formed by combining architecture and neuroscience (neuroarchitecture). The most important information concerning the use of particular neuroscience research in architecture are also discussed, such as: observational and experimental methods from the field of environmental psychology, fMRI (functional magnetic resonance imaging), eye tracking, VR (virtual reality) and the EDA wristbands.

PL

Celem artykułu jest przegląd i przybliżenie badań z zakresu neuronauk, których wyniki wskazują na możliwość lub potencjał zastosowania ich w architekturze. Niniejsze opracowanie jest propozycją merytorycznego wprowadzenia do systematyki i szczegółowego opisu wykorzystania poszczególnych metod badawczych na każdym etapie procesu projektowego. W artykule omówiono kluczowe definicje i rys historyczny neuroarchitektury - interdyscypliny, która ukształtowała się dzięki połączeniu architektury i neuronauk. Przytoczono również najważniejsze informacje dotyczące wykorzystania poszczególnych metod i narzędzi właściwych neuronaukom w architekturze, takich jak: metody obserwacyjne i eksperymentalne z zakresu psychologii środowiskowej, fMRI (funkcjonalny rezonans magnetyczny), okulografia (eye tracking), VR (virtual reality) oraz opaski EDA.

11

Transition of emotions from the negatively excited state to positive unexcited state: an ERP perspective

Singh Moon Inder, Singh Mandeep

Metrology and Measurement Systems

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2022

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Vol. 29, nr 2

405--423

EN

The cognitive aspects like perception, problem-solving, thinking, task performance, etc., are immensely influenced by emotions making it necessary to study emotions. The best state of emotion is the positive unexcited state, also known as the HighValence LowArousal (HVLA) state of the emotion. The psychologists endeavour to bring the subjects from a negatively excited state of emotion (Low Valence High Arousal state) to a positive unexcited state of emotion (High Valence Low Arousal state). In the first part of this study, a four-class subject independent emotion classifier was developed with an SVM polynomial classifier using average Event Related Potential (ERP) and differential average ERP attributes. The visually evoked Electroencephalogram (EEG) signals were acquired from 24 subjects. The four-class classification accuracy was 83% using average ERP attributes and 77% using differential average ERP attributes. In the second part of the study, the meditative intervention was applied to 20 subjects who declared themselves negatively excited (in Low Valence High Arousal state of emotion). The EEG signals were acquired before and after the meditative intervention. The four-class subject independent emotion classifier developed in Study 1 correctly classified these 20 subjects to be in a negatively excited state of emotion. After the intervention, 16 subjects self-assessed themselves to be in a positive unexcited (HVLA) state of emotion (which shows the intervention accuracy of 80%). Testing a four-class subject independent emotion classifier on the EEG data acquired after the meditative intervention validated 13 of 16 subjects in a positive unexcited state, yielding an accuracy of 81.3%.

12

Concept of using the brain-computer interface to control hand prosthesis

Żaba Julia, Paszkiel Szczepan

Journal of Automation Mobile Robotics and Intelligent Systems

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2022

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Vol. 16, No. 4

3--12

EN

This study examines the possibility of implementing intelligent artificial limbs for patients after injuries or amputations. Brain-computer technology allows signals to be acquired and sent between the brain and an external device. Upper limb prostheses, however, are quite a complicated tool, because the hand itself has a very complex structure and consists of several joints. The most complicated joint is undoubtedly the saddle joint, which is located at the base of the thumb. You need to demonstrate adequate anatomical knowledge to construct a prosthesis that will be easy to use and resemble a human hand as much as possible. It is also important to create the right control system with the right software that will easily work together with the brain-computer interface. Therefore, the proposed solution in this work consists of three parts, which are: the Emotiv EPOC + Neuroheadsets, a control system made of a servo and an Arduino UNO board (with dedicated software), and a hand prosthesis model made in the three-dimensional graphic program Blender and printed using a 3D printer. Such a hand prosthesis controlled by a signal from the brain could help people with disabilities after amputations and people who have damaged innervation at the stump site.

13

Effects of Bikram Yoga Clothes on EEG Beta Spectrum

Lee Hyojeong, Lee Yejin

Fibres & Textiles in Eastern Europe

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2022

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Vol. 30, no. 6

45--54

EN

This study analyzes how the beta index, which is closely related to alertness, caution, concentration, anxiety, and tension in brain activity, varies before and after practicing yoga. Electroencephalogram (EEG) and subjective evaluations were conducted on healthy female yoga trainers with over three years of experience; participants wore yoga clothes with differing characteristics in a hot environment. Repeated ANOVA measurements were conducted on the data by deriving the difference between the corresponding sample t-test pre- and post-yoga . After yoga, concentration increased, while alertness, anxiety, and excitement decreased depending on the yoga clothes. The clothing combination that offered higher pressure and greater absorption, and enhanced concentration while lowering excitation and anxiety increased beta waves the most. The design characteristics of yoga clothes influence beta power for concentration and arousal after yoga practice. Through EEG measurements, it was possible to explore the mental states resulting from wearing clothes suitable for yoga.

14

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

Wang Huiyang, Yu Hua, Wang Haixian

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 3

1023--1040

EN

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

15

EEG based cortical investigation for the limit of stability analysis in transfemoral amputees: A comparison with able-bodied individuals

Khajuria Aayushi, Joshi Deepak

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

362--374

EN

Background: The evidences for demonstrating the contributions of the cerebral cortex in human postural control is increasing. However, there remain little insights about the cortical correlates of balance control in lower-limb amputees. The present study aimed to investigate the cortical activity and balance performance of transfemoral amputees in comparison to healthy individuals during a continuous balance task (CBT). Methods: The postural stability of the participants was defined with limit of stability parameter. Electroencephalography (EEG) data were recorded in synchronization with the center of pressure (CoP) data from eighteen individuals (including eight unilateral transfemoral amputees). We anticipated that, due to the limb loss, the postural demand of transfemoral amputees increases which significantly modulates the spectral power of intrinsic cortical oscillations. Findings: Using the independent components from the sensorimotor areas and supplementary motor area (SMA), our results present a well-pronounced drop of alpha spectral power at sensorimotor area contralateral to sound limb of amputees in comparison to SMA and the sensorimotor area contralateral to prosthetic limb. Following this, we found significantly higher (p < 0.05) limit of stability (LOS) at their sound limb than at the prosthetic limb. Healthy individuals have similar contribution from both the limbs and the EEG alpha spectral power was similar across the three regions of the cortex during the balance control task as expected. Overall, a decent correlation was found between the LOS and alpha spectral power in both amputee and healthy individuals (Pearson’s correlation coefficient > 0.5). Interpretation: By externally stimulating the highlighted cortical regions, neuroplasticity might be promoted which helps to reduce the training time for the efficient rehabilitation of amputees. Additionally, this new knowledge might benefit in the designing and development of innovative interventions to prevent falls due to lower limb amputation.

16

Badania wpływu zaawansowanej technologicznie odzieży ochronnej na stan psychofizyczny człowieka

Dąbrowska Anna, Bartkowiak Grażyna, Krzemińska Sylwia, Greszta Agnieszka, Kobus Monika

Bezpieczeństwo Pracy : nauka i praktyka

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2022

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nr 9

20-25

PL

Integracja mikroelektroniki i elektroniki noszonej ze środkami ochrony indywidualnej, w tym z odzieżą ochronną, z jednej strony sprawia, że można uzyskać zupełnie nowe funkcje tych środków, jednak z drugiej strony niewłaściwe ich zaprojektowanie może być potencjalnym źródłem zagrożenia dla użytkownika, np. przez spowodowanie nad miernego obciążenia psychofizycznego. Z tego względu niezbędne jest badanie odzieży ochronnej wyposażonej w elektronikę noszoną pod kątem jej wpływu na obciążenie psychofizyczne człowieka. W Centralnym Instytucie Ochrony Pracy - Państwowym Instytucie Badawczym podjęto działania w kierunku opracowania nowej metodyki badań, która pozwoliłaby ocenić wpływ aktywnej odzieży ochronnej, w tym z wbudowanymi czujnikami i modułami mikro elektronicznymi, na obciążenie psychofizyczne jej użytkownika w symulowanych warunkach przewidywanego stosowania.

EN

The integration of microelectronics and wearable electronics with personal protective equipment (PPE) means that, on the one hand, completely new functions can be achieved, but on the other hand, their inappropriate design may be a potential source of danger for the user, e.g. by causing excessive psychophysical burden. For this reason, it is necessary to test protective clothing equipped with wearable electronics in terms of its impact on the psychophysical load of a human being. At the Central Institute for Labour Protection - National Research Institute, efforts were made to develop a new research methodology that would allow to fully investigate and evaluate the impact of smart protective clothing, including those with built-in sensors and microelectronic modules, on the psychophysical load of its user under simulated conditions of intended use.

17

Effect of Psychoacoustic Annoyance on EEG Signals of Tractor Drivers

Lashgari Majid, Arab Mohammad Reza, Nadjafi Mohsen, Rafiee Mojtaba

Archives of Acoustics

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2022

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Vol. 47, No. 4

469--477

EN

The purpose of this study was to evaluate the psychoacoustic annoyance (PA) that the tractor drivers are exposed to, and investigate its effects on their brain signals during their work activities. To this aim, the sound of a garden tractor was recorded. Each driver’s electroencephalogram (EEG) was then recorded at five different engine speeds. The Higuchi method was used to calculate the fractal dimension of the brain signals. To evaluate the amount of acoustic annoyance that the tractor drivers were exposed to, a psychoacoustic annoyance (PA) model was used. The results showed that as the engine speed increased, the values of PA increased as well. The results also indicated that an increase in the Higuchi’s fractal dimension (HFD) of alpha and beta bands was due to the increase of the engine speed. The regression results also revealed that there was a high correlation between the HFD of fast wave activities and PA, in that, the coefficients of determination were 0.92 and 0.91 for alpha and beta bands, respectively. Hence, a good correlation between the EEG signals and PA can be used to develop a mathematical model which quantifies the human brain response to the external stimuli.

18

Determining the degree of player engagement in a computer game with elements of a social campaign using cognitive neuroscience techniques

Biercewicz Konrad, Borawski Mariusz, Borawska Anna, Duda Jarosław

Applied Computer Science

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2022

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Vol. 18, no 4

28--52

EN

Due to the popularity of video games in various applications, including both commercial and social marketing, there is a need to assess their content in terms of player satisfaction, already at the production stage. For this purpose, the indices used in EEG tests can be used. In this publication, a formula has been created based on the player's commitment to determining which elements in the game should be improved and for which graphic emblems connected with social campaigns were more memorable and whether this was related to commitment. The survey was conducted using a 2D platform game created in Unity based on observations of 28 recipients. To evaluate the elements occurring in the game at which we obtain a higher memory for graphic characters, a corresponding pattern was created based on player involvement. The optimal Index for moving and static objects and the Index for destruction were then selected based on the feedback. Referring to the issue of graphic emblems depicting social campaigns should be placed in a place where other activities such as fighting will not be distracted, everyone will be able to reach the level where the recently placed advertisement is. This study present the developed method to determine the degree of player's engagement in particular elements in the game using the EEG and to explore the relationship between the visibility of social advertising and engagement in a 2D platform game where the player has to collect three keys and defeat the ultimate opponent.

19

An EEG mobile device as a game controller

Dawid Aleksander, Buchwald Paweł

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

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2021

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Vol. 25, No 4

397--407

EN

In this work the real-time control of computer games was explored by a single elec-trode mobile electroencephalography (EEG) device with a Bluetooth interface. The amplitudevariation in the two frequency bands of 4-12 Hz and 60-200 Hz was selected as the real-timecontrol parameter. The frequency-domain of a raw EEG signal was calculated using the discreteFourier transform. The time-dependent signal samples equal to 512, 1024, and 2048 time pointsin size were used in our research. The well-known classic Pong game was used to try out ourcontroller. The developed software handles communication with the device and real-time gamerendering. The .NET Framework with the C# programming language was used as a develop-ment tool. 50 gameplay trials were made for each controller setup. The obtained results arepromising for the possible use of the device in real-time communication with computer devicesfor people with hand disabilities.

20

Efektywność klasyfikacji mrugnięcia z wykorzystaniem wybranych sieci neuronowych

Galas Krzysztof

Studia i Materiały Informatyki Stosowanej

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2021

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nr 1

11--16

PL

Głównym celem badania było porównanie i wykazanie, która z przedstawionych typów sieci neuronowych najlepiej sklasyfikuje pobierany sygnał EEG mierzony przez headset Emotiv EPOC. Przedstawione sieci neuronowe są stosowane w szerokim zakresie przetwarzania danych. Została wybrana sieć splotowa oraz sieć Kohonena. Parametry sieci, takie jak ilość przejść danych uczących w jednej sesji uczącej zostały modyfikowane. Badanie uwzględnia stopień błędu klasyfikacji sygnału przez sieć oraz ilość czasu potrzebna do trening modelu. Wartością porównywalną jest stosunek czasu treningu do stopnia dokładności klasyfikacji. Otrzymane wyniki zostały przedstawione jako wykresy zależności w/w wartości do parametrów dotyczących uczenia modelu sieci.

EN

The main objective of this study was to compare and demonstrate which of the presented neural network types will best classify the extracted EEG signal measured by the Emotiv EPOC headset. The presented neural networks are used in a wide range of data processing. A convolutional network and a Kohonen network have been selected. The network parameters such as number of learning data transitions in one learning session have been modified. The study considers the degree of signal classification error by the network and the amount of time required to train the model. The comparative value is the ratio of training time to classification accuracy. The obtained results are presented as plots of the relation of the above-mentioned values to the parameters concerning the learning of the network model.