293 research outputs found
Оценка состояния оператора в условиях электромагнитного шумового излучения
The purpose of the work, the results of which are presented within the the article, was to study changes in the nonlinear EEG parameters represented by sample entropy, correlation dimension, fractal dimension, Lempel-Ziv complexity while the operator is irradiated with electromagnetic noise. Apart from the above nonlinear parameters, we studied the change in the power spectral density of delta-, theta-, alpha-, and beta-rhythms. A change in the spectral power density of beta- and theta-rhythms, fractal dimension, and sample entropy during irradiation was associated with a change in the above parameters during depression. A change in the spectral power density of delta-, theta-, alpha-, and beta-rhythms, the correlation dimension, and Lempel-Ziv complexity during irradiation was associated with a change in the above parameters in stress. A change in the spectral power density of the theta rhythm, sample entropy and Lempel-Ziv complexity during irradiation was associated with a change in the above parameters during mental fatigue. The power of the electromagnetic noise generator was 30 mW, the spectral range was 5 GHz, and the generator itself was a generator of electromagnetic noise radiation on transistors. The mathematical description of the calculation of nonlinear parameters represented by sample entropy, correlation dimension, fractal dimension and Lempel-Ziv complexity was studied. The registration of electroencephalograms was carried out according to the “10/20” scheme using the MBN electroencephalograph. The results of the work showed the presence of a depressive and stressful state, as well as the absence of mental fatigue when exposed to electromagnetic noise radiation, if we are guided by the change in sample entropy, correlation dimension, fractal Цель работы, результаты которой представлены в рамках статьи, заключалась в исследовании закономерностей изменений нелинейных параметров ЭЭГ, представленных выборочной энтропией, корреляционной размерностью, фрактальной размерностью, сложностью Лемпеля-Зива при облучении оператора электромагнитным шумовым излучением. Вместе с вышеуказанными нелинейными параметрами исследовалось изменение спектральной плотности мощности дельта-, тета-, альфа- и бета-ритмов. Изменение спектральной плотности мощности бетаи тета-ритмов, фрактальной размерности и выборочной энтропии при облучении было связано с изменением вышеуказанных параметров при депрессии. Изменение спектральной плотности мощности дельта-, тета-, альфа- и бета-ритмов, корреляционной размерности и сложности Лемпеля-Зива при облучении было связано с изменением вышеуказанных параметров при стрессе. Изменение спектральной плотности мощности тета-ритма, выборочной энтропии и сложности Лемпеля-Зива при облучении было связано с изменением вышеуказанных параметров при умственной усталости. Мощность генератора электромагнитного шума составляла 30мВт, спектральный диапазон составлял 5ГГц, а сам генератор представлял собой генератор электромагнитного шумового излучения на транзисторах. Было изучено математическое описание расчета нелинейных параметров, представленных выборочной энтропией, корреляционной размерностью, фрактальной размерностью и сложностью Лемпеля-Зива. Регистрация электроэнцефалограмм осуществлялась по схеме “10/20” с использованием электроэнцефалографа “Нейрокартограф” фирмы МБН. Результаты работы показали наличие депрессивного и стрессового состояния, а также отсутствие умственной усталости при воздействии электромагнитным шумовым излучением, если руководствоваться изменением выборочной энтропии, корреляционной размерности, фрактальной размерности, сложности Лемпеля-Зива и спектральной плотности мощности
Real-time EMG based pattern recognition control for hand prostheses : a review on existing methods, challenges and future implementation
Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations
Thoughts on Neurophysiological Signal Analysis and Classification
Neurophysiological signal is crucial intermediary, through which brain activity can be quantitatively measured and brain mechanisms are able to be revealed. In particular, those non-invasive neurophysiological signals, such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), are welcome and frequently utilised in a variety of studies because those signals can be non-invasively recorded without harms to the human brain while they are conveying abundant information pertaining to brain activity. The recorded neurophysiological signals are analysed to mine meaningful information for the understanding of brain mechanisms or are classified to distinguish different patterns (e.g., different cognitive states, brain diseases versus healthy controls). To date, remarkable progress has been made in both the analysis and classification of neurophysiological signal, but scholars are not feeling complacent. Consistent effort ought to be paid to advance the research of analysis and classification based on neurophysiological signal. In this paper, I express my thoughts about promising future directions in neurophysiological signal analysis and classification based on the current developments and achievements. I will elucidate the thoughts after brief summaries of relevant backgrounds, achievements, and tendencies. According to my personal selection and preference, I mainly focus on brain connectivity, multidimensional array (tensor), multi-modality, multiple task classification, deep learning, big data, and naturalistic experiment. Hopefully, my thoughts could give a little help to inspire new ideas and contribute to the research of analysis and classification of neurophysiological signal in some way
Analysis of Signal Decomposition and Stain Separation methods for biomedical applications
Nowadays, the biomedical signal processing and classification and medical image interpretation play an essential role in the detection and diagnosis of several human diseases. The problem of high variability and heterogeneity of information, which is extracted from digital data, can be addressed with signal decomposition and stain separation techniques which can be useful approaches to highlight hidden patterns or rhythms in biological signals and specific cellular structures in histological color images, respectively. This thesis work can be divided into two macro-sections. In the first part (Part I), a novel cascaded RNN model based on long short-term memory (LSTM) blocks is presented with the aim to classify sleep stages automatically. A general workflow based on single-channel EEG signals is developed to enhance the low performance in staging N1 sleep without reducing the performances in the other sleep stages (i.e. Wake, N2, N3 and REM). In the same context, several signal decomposition techniques and time-frequency representations are deployed for the analysis of EEG signals. All extracted features are analyzed by using a novel correlation-based timestep feature selection and finally the selected features are fed to a bidirectional RNN model. In the second part (Part II), a fully automated method named SCAN (Stain Color Adaptive Normalization) is proposed for the separation and normalization of staining in digital pathology. This normalization system allows to standardize digitally, automatically and in a few seconds, the color intensity of a tissue slide with respect to that of a target image, in order to improve the pathologist’s diagnosis and increase the accuracy of computer-assisted diagnosis (CAD) systems. Multiscale evaluation and multi-tissue comparison are performed for assessing the robustness of the proposed method. In addition, a stain normalization based on a novel mathematical technique, named ICD (Inverse Color Deconvolution) is developed for immunohistochemical (IHC) staining in histopathological images. In conclusion, the proposed techniques achieve satisfactory results compared to state-of-the-art methods in the same research field. The workflow proposed in this thesis work and the developed algorithms can be employed for the analysis and interpretation of other biomedical signals and for digital medical image analysis
Effect of task failure on intermuscular coherence measures in synergistic muscles
The term "task failure" describes the point when a person is not able to maintain the level of force required by a task. As task failure approaches, the corticospinal command to the muscles increases to maintain the required level of force in the face of a decreased mechanical efficacy. Nevertheless, most motor tasks require the synergistic recruitment of several muscles. How this recruitment is affected by approaching task failure is still not clear. The increase in the corticospinal drive could be due to an increase in synergistic recruitment or to overlapping commands sent to the muscles individually. Herein, we investigated these possibilities by combining intermuscular coherence and synergy analysis on signals recorded from three muscles of the quadriceps during dynamic leg extension tasks. We employed muscle synergy analysis to investigate changes in the coactivation of the muscles. Three different measures of coherence were used. Pooled coherence was used to estimate the command synchronous to all three muscles, pairwise coherence the command shared across muscle pairs and residual coherence the command peculiar to each couple of muscles. Our analysis highlights an overall decrease in synergistic command at task failure and an intensification of the contribution of the nonsynergistic shared command
Smart Bagged Tree-based Classifier optimized by Random Forests (SBT-RF) to Classify Brain- Machine Interface Data
Brain-Computer Interface (BCI) is a new technology that uses electrodes and sensors to connect machines and computers with the human brain to improve a person\u27s mental performance. Also, human intentions and thoughts are analyzed and recognized using BCI, which is then translated into Electroencephalogram (EEG) signals. However, certain brain signals may contain redundant information, making classification ineffective. Therefore, relevant characteristics are essential for enhancing classification performance. . Thus, feature selection has been employed to eliminate redundant data before sorting to reduce computation time. BCI Competition III Dataset Iva was used to investigate the efficacy of the proposed system. A Smart Bagged Tree-based Classifier (SBT-RF) technique is presented to determine the importance of the features for selecting and classifying the data. As a result, SBT-RF is better at improving the mean accuracy of the dataset. It also decreases computation cost and training time and increases prediction speed. Furthermore, fewer features mean fewer electrodes, thus lowering the risk of damage to the brain. The proposed algorithm has the greatest average accuracy of ~98% compared to other relevant algorithms in the literature. SBT-RF is compared to state-of-the-art algorithms based on the following performance metrics: Confusion Matrix, ROC-AUC, F1-Score, Training Time, Prediction speed, and Accuracy
The Role of Alpha Oscillations among the Main Neuropsychiatric Disorders in the Adult and Developing Human Brain: Evidence from the Last 10 Years of Research
Alpha oscillations (7–13 Hz) are the dominant rhythm in both the resting and active brain.
Accordingly, translational research has provided evidence for the involvement of aberrant alpha activ-
ity in the onset of symptomatological features underlying syndromes such as autism, schizophrenia,
major depression, and Attention Deficit and Hyperactivity Disorder (ADHD). However, findings on
the matter are difficult to reconcile due to the variety of paradigms, analyses, and clinical phenotypes
at play, not to mention recent technical and methodological advances in this domain. Herein, we seek
to address this issue by reviewing the literature gathered on this topic over the last ten years. For each
neuropsychiatric disorder, a dedicated section will be provided, containing a concise account of the
current models proposing characteristic alterations of alpha rhythms as a core mechanism to trigger
the associated symptomatology, as well as a summary of the most relevant studies and scientific con-
tributions issued throughout the last decade. We conclude with some advice and recommendations
that might improve future inquiries within this field
Angular gyrus connectivity at alpha and beta oscillations is reduced during tonic pain - Differential effect of eye state
The angular gyrus (AG) is a common hub in the pain networks. The role of the AG during pain perception, however, is still unclear. This crossover study examined the effect of tonic pain on resting state functional connectivity (rsFC) of the AG under eyes closed (EC) and eyes open (EO). It included two sessions (placebo/pain) separated by 24 hours. Pain was induced using topical capsaicin (or placebo as control) on the right forearm. Electroencephalographic rsFC assessed by Granger causality was acquired from 28 healthy participants (14 women) before (baseline) and 1-hour following the application of placebo/capsaicin. Subjects were randomly assigned and balanced to groups of recording sequence (EC-EO, EO-EC). Decreased rsFC at alpha-1 and beta, but not alpha-2, oscillations was found during pain compared to baseline during EC only. For alpha-1, EC-EO group showed a pain-induced decrease only among connections between the right AG and each of the posterior cingulate cortex (PCC, P = 0.002), medial prefrontal cortex (mPFC, P = 0.005), and the left AG (P = 0.023). For beta rsFC, the EC-EO group showed a bilateral decrease in rsFC spanning the connections between the right AG and mPFC (P = 0.015) and between the left AG and each of PCC (P = 0.004) and mPFC (P = 0.026). In contrast, the EO-EC group showed an increase in beta rsFC only among connections between the left AG and each of PCC (P = 0.012) and mPFC (P = 0.036). No significant change in the AG rsFC was found during EO. These results provide insight into the involvement of the AG in pain perception and reveal methodological considerations when assessing rsFC during EO and EC
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