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    Wyświetlanie 1-8 z 8
    Tytuł:
    Resting state EEG rhythm characteristics associated with readiness for divergent thinking
    Cechy rytmu EEG w spoczynku związane z gotowością do myślenia rozbieżnego
    Autorzy:
    Kozachuk, N.
    Kachynska, T.
    Zhuravlyov, O.
    Zhuravlyova, O.
    Powiązania:
    https://bibliotekanauki.pl/articles/2048046.pdf
    Data publikacji:
    2021
    Wydawca:
    Akademia Bialska Nauk Stosowanych im. Jana Pawła II w Białej Podlaskiej
    Tematy:
    alpha rhythm
    electroencephalogram
    brain
    Opis:
    Background. Local synchronization of the electrical activity of the cerebral cortex at rest with eyes open in persons with different levels of divergent thinking were studied. Material and methods. 95 men and 98 women aged 18-21 with different levels of divergent thinking were studied. The power of the EEG at rest with eyes closed and open was analyzed. Results. There were established differences in the extent of the depression depth, as well as in the activation of the EEG alpha rhythm, which is related to gender and level of productivity. Women have a greater depth of alpha rhythm depression than men. In subjects with high and medium levels of divergent thinking, alpha-rhythm depression was of a generalized nature, and in subjects with low levels of divergent thinking – local and topographically non-specific. Conclusions. These results indicate that the EEG response of readiness to perceive stimuli, which provides either very high or very low productivity of divergent thinking, is manifested in the dynamics of the biopotential power in the EEG alpha range.
    Źródło:
    Health Problems of Civilization; 2021, 15, 3; 234-241
    2353-6942
    2354-0265
    Pojawia się w:
    Health Problems of Civilization
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Features of the cortical activity of men having a high or low alpha-frequency background of the eeg while performing alternate finger movements
    Autorzy:
    Morenko, A.
    Tsos, A.
    Kotsan, I.
    Powiązania:
    https://bibliotekanauki.pl/articles/2052222.pdf
    Data publikacji:
    2014
    Wydawca:
    Akademia Bialska Nauk Stosowanych im. Jana Pawła II w Białej Podlaskiej
    Tematy:
    power
    coherency
    electroencephalogram
    individual alpha-frequency
    alternate movements by fingers
    Opis:
    The purpose of this paper is to identify the cerebral electrical activity features in men with a high or low α-frequency background while performing the alternate finger movements. A test group consisting of 104 healthy men from the ages of 19 to 21 was divided into two groups according to the magnitude of their individual α- frequency (ІАF) median –groups with high (n = 53, IAF ≥10,04 Hz) and low (n = 51, IAF ≤10,03 Hz) levels of ІАF. Changes in power and the coherence of the EEG oscillations during the alternate finger movements as well as intergroup differences were evaluated. Men with high a IAF are distinguished by higher rates of speed and accuracy in terms of their sensory-motor response. The role of inhibitory neural processes increases in the case of men whose frequencies are low. The implementation of alternating finger movements in male groups is accompanied by a decrease in the coherence of θ-, α1-, α3- EEG oscillations in the cortex in general, β- and γ-activity - in the rear temporal and occipital areas. In the frontal and central lobes of α2-, β- and γ- ranges an increase in EEG coherence fuctuations was observed. The power of θ-, α- and β1- waves, especially in the posterior cortex areas, decreases. A larger degree of low-frequency fuctuations in EEG power can be observed in the frontal area. Thus, more economical brain processes providing the processing of any sensory or motor information in men with a high IAF determine higher levels of the speed and accuracy of their sensorimotor responses. Men with a low IAF have lower ductility but a higher voltage of brain processes correlated with a decrease in the sensorimotor response of speed capabilities increasing the role of inhibitory effects.
    Źródło:
    Health Problems of Civilization; 2014, 08, 1; 24-31
    2353-6942
    2354-0265
    Pojawia się w:
    Health Problems of Civilization
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The Influence of a Low-Frequency Musical Fragment on the Neural Oscillations
    Autorzy:
    Drozdenko, Kateryna
    Naida, Sergey
    Drozdenko, Oleksandr
    Damarad, Anastasiia
    Pareniuk, Dmytro
    Vakulenko, Liudmyla
    Adaricheva, Zhanna
    Powiązania:
    https://bibliotekanauki.pl/articles/2141638.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    electroencephalogram
    brain rhythms
    music therapy
    acoustic influences
    bioelectrical activity
    spectral analysis
    Opis:
    Study of musical-acoustic influences, which are used to improve the functional state of a person, as well as her/his neurophysiological or psychological rehabilitation, is very relevant nowadays. It is related with a large number of conflict situations, significant psychological and informational overloads of modern human, permanent stress due to the pandemic, economic crisis, natural and man-made disasters. This work examines the effect of listening to low-frequency music on the percentage of alpha, beta, delta, and theta waves in the total spectral power of the electroencephalogram in the frequency band 0.5–30 Hz. To obtain rhythms of the brain the spectral analysis of filtered native electroencephalogram was used. For statistical analysis of neural oscillations the Student’s t-test and the sign test were implemented with usage of the Lilliefors normality criterion and the Shapiro-Wilk test. Statistically significant differences were identified in alpha, theta and delta oscillations. For the beta rhythm presented music did not play any significant role. An increase in the activity of the alpha rhythm in the temporal (for 2.20 percentage point), central (for 1.51 percentage point), parietal (for 2.70 percentage point), occipital (for 2.22 percentage point) leads of the right hemisphere and the parietal (for 1.74 percentage point) and occipital (for 2.46 percentage point) leads of the left hemisphere and also of the theta rhythm in the temporal leads of the left hemisphere (for 1.13 percentage point) were observed. The downfall of delta rhythm in the frontal lead of the left hemisphere (for 1.51 percentage point) and occipital in both hemispheres (for 1.64 and 1.33 percentage points respectively in the left and right hemispheres) was detected. These may indicate that listening to low-frequency compositions helps to restore the brain in physiological conditions at different functional overload levels, decrease the level of emotional tone, and promote relaxation.
    Źródło:
    Archives of Acoustics; 2022, 47, 2; 169-179
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    PREDICTING THE MENTAL STRESS LEVEL OF DRIVERS IN A BRAKING CAR PROCESS USING ARTIFICIAL INTELLIGENCE
    Autorzy:
    Sugiono, Sugiono
    Prasetya, Renaldi P
    Fanani, Angga A
    Cahyawati, Amanda N
    Powiązania:
    https://bibliotekanauki.pl/articles/2138164.pdf
    Data publikacji:
    2022-02-23
    Wydawca:
    Fundacja Edukacji Medycznej, Promocji Zdrowia, Sztuki i Kultury Ars Medica
    Tematy:
    Artificial intelligence (AI)
    Electroencephalogram (EEG)
    safety distance
    transportation safety
    mental stress
    Opis:
    Reducing the physical and mental weariness of drivers is significant in improving healthy and safe driving. This paper is aim to predict the stress level of drivers while braking in various conditions of the track. By discovering the drivers’ mental stress level, we are able to safely and comfortably adjust the distance in relation to the vehicle ahead. The initial step used was a study related to Artificial Intelligence (AI), Electroencephalogram (EEG), safe distance in braking, and the theory of mental stress. The data was collected by doing a direct measurement of drivers’stress levels using the EEG tool. The respondents were 5 parties around 30-50 years old who had experience in driving for> 5 years. The research asembled 400 pieces of data about braking including the data of the velocity before braking, track varieties (cityroad, rural road, residential road, and toll road), braking distance, stress level (EEG), and focus (EEG). The database constructed was used to input the machine learning (AI) – Back Propagation Neural Network (BPNN) in order to predict the drivers’ mental stress level. Referring to the data collection, each road type gave a different value of metal stress and focus. City road drivers used an average velocity of 23.24 Km/h with an average braking distance of 11.17 m which generated an average stress level of 53.44 and a focus value of 45.76.Under other conditions, city road drivers generated a 52.11 stress level, the rural road = 48.65, and 50.23 for the toll road. BPNN Training with 1 hidden layer, neuron = 17, ground transfer function, sigmoid linear, and optimation using Genetic Algorithm (GA) obtained the Mean Square Error (MSE) value = 0.00537. The road infrastructure, driving behavior, and emerging hazards in driving took part in increasing the stress level and concentration needs of the drivers. The conclusion may be drawn that the available data and the chosen BPNN structure were appropriate to be used in training and be utilized to predict drivers’ focus and mental stress level. This AI module is beneficial in inputting the data to the braking car safety system by considering those mental factors completing the existing technical factor considerations.
    Źródło:
    Acta Neuropsychologica; 2022, 20(1); 1-15
    1730-7503
    2084-4298
    Pojawia się w:
    Acta Neuropsychologica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Metody eliminacji artefaktów w sygnałach EEG
    Methods of EEG artifacts elimination
    Autorzy:
    Plechawska-Wójcik, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/408247.pdf
    Data publikacji:
    2015
    Wydawca:
    Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
    Tematy:
    elektroencefalogram
    pomiar elektroencefalograficzny
    pomiar szumu
    artefakt EEG
    electroencephalogram
    electroencephalography measurement
    noise measurement
    EEG artifacts
    Opis:
    Rejestracja sygnałów elektroencefalograficznych (EEG) jest niemal zawsze związana z zapisem różnego rodzaju artefaktów, które zaszumianą odczyt i utrudniają analizę zebranych danych. Artefakty te mogą być zauważalne w pojedynczych kanałach, ale bardzo często muszą być korygowane na przestrzeni kilku kanałów jednocześnie. Ich pochodzenie może być różnorodne. Wyróżnia się artefakty sieciowe, sprzętowe jak również kilka rodzajów artefaktów mięśniowych, pochodzących od badanej osoby. W ostatnich latach obserwuje się wzrost zainteresowania badaniami EEG nie tylko w zastosowaniach ambulatoryjnych i klinicznych, ale także w analizach psychologicznych oraz w budowie nowoczesnych interfejsów człowiekmaszyna. Artykuł przedstawia studium przypadku zastosowania analiz klasyfikacyjnych w zagadnieniach korekcji artefaktów sygnału EEG.
    Registration of electroencephalography signals (EEG) is almost always associated with recording different kinds of artifacts that makes it difficult to read and analyze collected data. These artifacts may be noticeable in the individual channels, but very often they have to be adjusted over several channels simultaneously. Their origin can be varied. Among the most typical are network and hardware artifacts as well as several types of muscle artifacts, derived from the tested person. In recent years increased interest in EEG studies might be noticed. EEG signals are applied not only in the outpatient and clinical applications, but also in psychological analyses and in construction of modern human-machine interfaces. This article presents a case study of classification analysis application in EEG artifact correction tasks.
    Źródło:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2015, 2; 39-46
    2083-0157
    2391-6761
    Pojawia się w:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Personal identification based on brain networks of EEG signals
    Autorzy:
    Kong, W.
    Jiang, B.
    Fan, Q.
    Zhu, L.
    Wei, X.
    Powiązania:
    https://bibliotekanauki.pl/articles/329856.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    electroencephalogram signal
    personal identification
    brain network
    phase synchronization
    elektroencefalogram
    identyfikacja osobowa
    sieć mózgowa
    synchronizacja fazy
    Opis:
    Personal identification is particularly important in information security. There are numerous advantages of using electroencephalogram (EEG) signals for personal identification, such as uniqueness and anti-deceptiveness. Currently, many researchers focus on single-dataset personal identification, instead of the cross-dataset. In this paper, we propose a method for cross-dataset personal identification based on a brain network of EEG signals. First, brain functional networks are constructed from the phase synchronization values between EEG channels. Then, some attributes of the brain networks including the degree of a node, the clustering coefficient and global efficiency are computed to form a new feature vector. Lastly, we utilize linear discriminant analysis (LDA) to classify the extracted features for personal identification. The performance of the method is quantitatively evaluated on four datasets involving different cognitive tasks: (i) a four-class motor imagery task dataset in BCI Competition IV (2008), (ii) a two-class motor imagery dataset in the BNCI Horizon 2020 project, (iii) a neuromarketing dataset recorded by our laboratory, (iv) a fatigue driving dataset recorded by our laboratory. Empirical results of this paper show that the average identification accuracy of each data set was higher than 0.95 and the best one achieved was 0.99, indicating a promising application in personal identification.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2018, 28, 4; 745-757
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Zastosowanie QEEG w psychiatrii z uwzględnieniem populacji rozwojowej
    Application of QEEG in psychiatry taking into account the population of children and adolescents
    Autorzy:
    Wiśniewska, Martyna
    Gmitrowicz, Agnieszka
    Pawełczyk, Nina
    Powiązania:
    https://bibliotekanauki.pl/articles/941117.pdf
    Data publikacji:
    2016
    Wydawca:
    Medical Communications
    Tematy:
    child and adolescent psychiatry
    neurofeedback
    quantitative analysis of electroencephalogram
    ilościowa analiza eeg
    psychiatria dzieci i młodzieży
    Opis:
    The aim of the study is to discuss QEEG method in the context of its usefulness for confirming neurodevelopmental disorders, and evaluating the effectiveness of psychiatric and/or psychological interventions, based on a review of available literature. It attempts to determine the applicability of this method in child and adolescent psychiatry. QEEG is quantitative analysis of EEG record using statistical processing of the signal. This method is commonly used to prepare therapeutic recommendations for neurofeedback training. Attempts at implementation of QEEG in diagnosis of various diseases have been increasingly often described in foreign studies. This also applies to psychiatry. Most reports still tackle attention deficit hyperactivity disorder (comparison of the effects of neurofeedback therapy and pharmacological interactions, distinguishing subtypes of the disease). Other analyses are concerned with diagnosing mental illnesses, differentiating their subtypes, predicting effects of pharmacological therapy, comparing the effectiveness of different treatments. Studies of patients with depression and schizophrenia are also becoming popular. QEEG has proved useful in determining the efficacy of pharmacological treatment of depression. According to the researchers, this method enables prediction of schizophrenia, differentiation of its subtypes, and determining the effectiveness of its treatment. There are studies devoted to the analysis of changes in EEG characteristic of methamphetamine addiction or eating disorders. There is, however, little research regarding the use of this method in child and adolescent psychiatry, even though there obviously exist many studies related to the use of QEEG in neurofeedback therapy in the youngest patients with attention deficit hyperactivity disorder, or studies on the effectiveness of various types of medications used in the treatment of this disease. One study discussing EEG biofeedback training in autistic children has also been published. It seems necessary to expand research on the use of quantitative QEEG in work with children and adolescents suffering from psychiatric entities.
    Celem pracy jest omówienie metody QEEG w kontekście jej przydatności w stawianiu diagnozy psychiatrycznej oraz monitorowaniu skutków oddziaływań psychologicznych i/lub psychofarmakologicznych na podstawie przeglądu dostępnego piśmiennictwa. Autorki starały się określić zastosowania omawianej metody w psychiatrii dzieci i młodzieży. QEEG polega na ilościowej analizie zapisu EEG za pomocą obróbki statystycznej sygnału. Metoda ta jest powszechnie wykorzystywana w celu przygotowania zaleceń terapeutycznych do treningu neurofeedback. Za granicą coraz częściej pisze się o próbach wdrażania QEEG w diagnostyce różnych chorób – także psychicznych. Większość doniesień nadal dotyczy zagadnień nadpobudliwości psychoruchowej z deficytem uwagi (porównanie skutków neurofeedbacku i oddziaływań farmakologicznych, odróżnianie podtypów choroby). Inne analizy odnoszą się do diagnozowania chorób psychicznych, różnicowania ich podtypów, przewidywania skutków leczenia farmakologicznego, porównania skuteczności poszczególnych metod leczenia. Popularne stają się badania nad pacjentami z depresją i schizofrenią. QEEG okazało się przydatne w ocenie skuteczności farmakoterapii depresji, według badaczy umożliwia też przewidywanie zachorowania na schizofrenię, różnicowanie jej podtypów i określanie skuteczności leczenia. Istnieją badania poświęcone analizie zmian w zapisie QEEG charakterystycznych dla uzależnienia od metamfetaminy i dla zaburzeń odżywiania. Mało jest natomiast analiz na temat użycia tej metody w psychiatrii dzieci i młodzieży – z wyjątkiem badań, które odnoszą się do roli QEEG w terapii neurofeedback pacjentów z zespołem nadpobudliwości psychoruchowej z deficytem uwagi czy skuteczności typów leków używanych w terapii tej choroby. Znaleziono jedną pracę poruszającą problematykę dzieci z autyzmem (w odniesieniu do treningu EEG biofeedback). Konieczne wydaje się poszerzenie badań o zastosowanie ilościowego QEEG w pracy z dziećmi i adolescentami cierpiącymi na choroby psychiczne.
    Źródło:
    Psychiatria i Psychologia Kliniczna; 2016, 16, 3; 188-193
    1644-6313
    2451-0645
    Pojawia się w:
    Psychiatria i Psychologia Kliniczna
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modified Block Sparse Bayesian Learning-Based Compressive Sensing Scheme For EEG Signals
    Autorzy:
    Upadhyaya, Vivek
    Salim, Mohammad
    Powiązania:
    https://bibliotekanauki.pl/articles/1844532.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    compressive sensing
    CS
    mean square error
    MSE
    structural similarity index measure
    SSIM
    electroencephalogram
    EEG
    digital signal processing
    DSP
    block sparse Bayesian learning
    BSBL
    Opis:
    Advancement in medical technology creates some issues related to data transmission as well as storage. In real-time processing, it is too tedious to limit the flow of data as it may reduce the meaningful information too. So, an efficient technique is required to compress the data. This problem arises in Magnetic Resonance Imaging (MRI), Electrocardiogram (ECG), Electroencephalogram (EEG), and other medical signal processing domains. In this paper, we demonstrate Block Sparse Bayesian Learning (BSBL) based compressive sensing technique on an Electroencephalogram (EEG) signal. The efficiency of the algorithm is described using the Mean Square Error (MSE) and Structural Similarity Index Measure (SSIM) value. Apart from this analysis we also use different combinations of sensing matrices too, to demonstrate the effect of sensing matrices on MSE and SSIM value. And here we got that the exponential and chi-square random matrices as a sensing matrix are showing a significant change in the value of MSE and SSIM. So, in real-time body sensor networks, this scheme will contribute a significant reduction in power requirement due to its data compression ability as well as it will reduce the cost and the size of the device used for real-time monitoring.
    Źródło:
    International Journal of Electronics and Telecommunications; 2021, 67, 3; 331-336
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-8 z 8

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