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Seizures have a serious impact on the physical function and daily life of epileptic patients. The automated detection of seizures can assist clinicians in taking preventive measures for patients during the diagnosis process. The combination of deep learning (DL) model with convolutional neural network (CNN) and transformer network can effectively extract both local and global features, resulting in improved seizure detection performance. In this study, an enhanced transformer network named Inresformer is proposed for seizure detection, which is combined with Inception and Residual network extracting different scale features of electroencephalography (EEG) signals to enrich the feature representation. In addition, the improved transformer network replaces the existing Feedforward layers with two half-step Feedforward layers to enhance the nonlinear representation of the model. The proposed architecture utilizes discrete wavelet transform (DWT) to decompose the original EEG signals, and the three sub-bands are selected for signal reconstruction. Then, the Co-MixUp method is adopted to solve the problem of data imbalance, and the processed signals are sent to the Inresformer network for seizure information capture and recognition. Finally, discriminant fusion is performed on the results of three-scale EEG sub-signals to achieve final seizure recognition. The proposed network achieves the best accuracy of 100% on Bonn dataset and the average accuracy of 98.03%, sensitivity of 95.65%, and specificity of 98.57% on the long-term CHB-MIT dataset. Compared to the existing DL networks, the proposed method holds significant potential for clinical research and diagnosis applications with competitive performance.

On nine unanesthetized male rabbits, the frequency spectra of hypothalamic electrogram (EEG) were studied during low intensity (10 mW/cm2) millimeter wave (55–75 GHz) exposure to various acupuncture points (zone): auricular, cranial and corporal. The chances of occurrence of significant (p < 0.05) changes in the EEG spectra during irradiation versus. sham experiments were equal to 31, 21 and 5%, respectively. Exposure to auricular zone reduced the EEG power in narrow bands with central frequencies of 5.3, 15.9 Hz and increased ones of 2.6, 3.2, 6.9, 7.9, 11.5 and 25.6 Hz. The main effect of exposure to cranial zone was similar — changes at 15.9 and 25.6 Hz only. The data obtained demonstrate that the responsiveness of the central nervous system to low intensity millimeter wave radiation may depend on the location of the exposed acupuncture zone.

Brain Respiration (BR)-training is a unique form of breathing exercise that develops potential ability by facilitating brain function. It is recognized as an effective method of improving the scholastic aptitude and emotional stability of children. The present study was designed to investigate the characteristics of the EEG during this training. Spectral analysis was used to examine the relative power in the EEG of 12 children while they practiced BR-training, and these were compared to those of 12 matched controls. BR-trainees showed a lower θ rhythm than the controls before the training session began and lower β₂ power before, during and after the session. In contrast, the BR subjects showed greater relative α₁ power than the controls in the left frontal region during BR-training, which persisted throughout the BR-training schedule. There is evidence that decreased θ and β waves may be correlated with emotional maturation, whilst increased α waves are associated with educational achievement. These findings enhance our understanding of the neurophysiological basis of the effects of BR-training upon emotion and maturation.

According to the experimental results and practitioners' subjective experience, we report some hypotheses that may account for meditative phenomena during the practice of Zen-Buddhism. Orthodox Zen-Buddhist practitioners, aiming to prove the most original true-self, discover and uncover the inner energy or light on the way towards their goal. Perception of the inner light can be comprehended as resonance. Uncovering the inner energy optimizes physiological and mental health. In the meditation experiment, a significant correlation was observed between perception of the inner light and electroencephalographic (EEG) alpha blockage. We further examined this phenomenon by recording the EEG from subjects during a blessing that the subjects did not know being given. During the blessing period, significant alpha blocking was observed in experimental subjects who had been practicing meditation for years in preparation for being in resonance with the inner light. This report provides a new insight into the debate that meditation benefits our health.

Reflexology is a 4000-year-old art of healing practiced in ancient India, China and Egypt. In the beginning of the 20th century, it spread to the Western world. Reflexologic clinics and massage centers can be found all around the world. In spite of the widespread popularity, to the best of our knowledge, no serious research work has been done in this area, although much scientific research work has been carried out in other Eastern techniques like meditation and yoga. This is why a humble attempt is done in this work to quantitatively assess the effect of reflexological stimulation from a systems point of view. In this work, nonlinear techniques have been used to assess the complexity of EEG with and without reflexological stimulation. We prefer the nonlinear approach, as we believe that the effects are taking place in a subtle way, since there is no direct correlation between reflexological points and modern neuroanatomy.

In this study, the EEG, ECG and blood-pressure-pulse recorder were employed to evaluate heart rate variability, pulse rate variability, and EEG of 10 adults after scalp (experimental test I) at Sishencong scalp acupoint and auricular (experimental test II) acupuncture at Shenmen auricular acupoint for about 10 min. Comparison of the results between the experimental tests and a control with no stimulation test showed that both the heart rate and pulse rate were decreased, and the blood pressure fell. The high and low frequency power of FFT analysis of heart rate was increased and decreased, respectively; indicating that the parasympathetic nerves were activated and the sympathetic nerves were inhibited. The analysis of the power spectrum of EEG showed that the number of low frequency waves was increased after acupuncture stimulation. Therefore, acupuncture on either Sishencong or Shenmen might calm the mind, slow down the heart rate, and activate the parasympathetic nerves.

This paper investigates how acupuncture at ST 36 modulates the brain functional network. 20 channel EEG signals from 15 healthy subjects are respectively recorded before, during and after acupuncture. The correlation between two EEG channels is calculated by using Pearson’s coefficient. A data-driven approach is applied to determine the threshold, which is performed by considering the connected set, connected edge and network connectivity. Based on such thresholding approach, the functional network in each acupuncture period is built with graph theory, and the associated functional connectivity is determined. We show that acupuncturing at ST 36 increases the connectivity of the EEG-based functional network, especially for the long distance ones between two hemispheres. The properties of the functional network in five EEG sub-bands are also characterized. It is found that the delta and gamma bands are affected more obviously by acupuncture than the other sub-bands. These findings highlight the modulatory effects of acupuncture on the EEG-based functional connectivity, which is helpful for us to understand how it participates in the cortical or subcortical activities. Further, the data-driven threshold provides an alternative approach to infer the functional connectivity under other physiological conditions.

In this paper, we investigate signatures of variation in the behavior of correlated time series by analyzing changes in the topological properties of the corresponding visibility graph. Variations in six different network measures: assortativity, average path length, clustering, transitivity, density, and the average of the mean link length, are explored. We construct visibility graphs from the original and the magnitude and sign of its increment series. Both the horizontal and the natural visibility graphs are studied. Through extensive numerical studies on the time series of fractional Brownian motion (fBm), we first identify network measures that can reflect the changes in correlations in the time series. The efficacy of these markers is examined to identify the transitions in two systems, a two-dimensional (2D) Ising spin system and EEG data with seizures. While all the identified network measures capture the change in the thermal equilibrium correlations for the Ising spin system, they have limited success in the case of the time-dependent fluctuations in the EEG data. We identify some markers relevant to detecting seizures in the EEG data set.

Epilepsy is a disorder of cortical excitability and still an important medical problem. The correct diagnosis of a patient's epilepsy syndrome clarifies the choice of drug treatment and also allows an accurate assessment of prognosis in many cases. The aim of this study is to evaluate epileptic patients and classify subgroups of partial epilepsy by Multilayer Perceptron Neural Networks (MLPNNs). This is the first study to classify the partial epilepsy groups using the neural network according to EEG signals. 418 patients with epilepsy diagnoses according to International League against Epilepsy (ILAE, 1981) were included in this study. The epilepsy outpatients at the Neurology Department Clinic of Cukurova University Medical School between the years of 2002–2005 were examined and included in the study. The MLPNNs were trained by the parameters obtained from the EEG signals and clinical findings of the patients. Test results show that the MLPNN model is able to classify partial epilepsy with an accuracy of 91.5%. Moreover, new MLPNNs were constructed for determining significant variables on classification. The loss of consciousness in the course of seizure time variable caused the largest decrease in the classification accuracy when it was left out. In conclusion, we think that the classification performance of MLPNN model for partial epilepsy is satisfactory and this model may be used in clinical studies as a decision support tool to determine the partial epilepsy classification of the patients.

EEG has been proved to be an effective tool for researchers’ cognition and mental workload by detecting the changes of brain activity potential. The mental workload of pilots in aviation flight is closely related to the characteristics of flight tasks. The previous methods have problems such as lack of objectivity, low EEG analysis ability and lack of real-time analysis ability. In order to solve these problems, this paper proposes a multi-dimensional data fusion brain workload calculation method based on flight effect evaluation, which integrates vision, operational behavior and visual gaze, and classifies and analyzes them in combination with EEG data. This method evaluates the mental workload of pilots from three aspects: visual gaze behavior, control behavior and flight effect in the simulated flight experimental environment, and realizes a more objective mental workload analysis. Then, the synchronously collected EEG data are segmented and sampled to form a dataset, and an LSTM neural network model integrating attention mechanism is established, in which the attention mechanism is used to improve the feature processing ability of the network model for the classification of complex EEG data. After machine learning training, the final model can achieve 94% detection accuracy for 2-s EEG data, and has the ability of real-time analysis in the application environment. Compared with the previous similar LSTM model, the accuracy is improved by 6%, which also shows the effectiveness of the model.

Recently, deep learning has been widely used in the classification of EEG signals and achieved satisfactory results. However, the correlation between EEG electrodes is rarely considered, which has been proved that there are indeed connections between different brain regions. After considering the connections between EEG electrodes, the graph convolutional neural network is applied to detect human motor intents from EEG signals, where EEG data are transformed into graph data through phase lag index, time-domain and frequency-domain features with different signal bands. Meanwhile, a multi-scale attention mechanism is proposed to the network to improve the accuracy of classification. By using the multi-scale attention-based graph convolutional neural network, the accuracy of 93.22% is achieved with 10-fold cross-validation, which is higher than the compared methods which ignore the spatial correlations of EEG signals.

This paper investigates the correlation between a pilot’s brain electroencephalographic (EEG) activity and his driving posture, addressing the intricate relationship between cognition and behavior in aviation. We designed and implemented a simulated experiment that recorded the fly pilot’s attitude and collected EEG information as experimental data. The experiment is only based on EEG data to predict flight posture (pull, down, left, right). We propose a flight-driving attitude prediction model (CA-FAP) based on CEBRA and self-attention mechanism, with a prediction accuracy of 0.83. This model is better than common spatial pattern (CSP) and uniform manifold approximation and projection (UMAP) dimension reduction methods in the experiment. Moreover, a better effect can be obtained in the larger attitude radian dataset (accuracy is 0.85), and the effect is not obvious in the dataset of closing the six-axis motion platform, indicating that the model prediction of flight attitude is closely related to the pilot position transformation. By comparing the prediction effect of each category, the pull-up and drop are better than the steering prediction result. The study can help pilots adjust their posture and decisions, and serve as a basis for studying flight pilot cognitive load, mental load, mood changes, and flight performance.

This paper presents a cognitive state estimation system focused on some effective feature extraction based on temporal and spectral analysis of electroencephalogram (EEG) signal and the proper channel selection of the BIOPAC automated EEG analysis system. In the proposed approach, different frequency components (i) real value; (ii) imaginary value; (iii) magnitude; (iv) phase angle and (v) power spectral density of the EEG data samples during different mental task performed to assess seven types of human cognitive states — relax, mental task, memory related task, motor action, pleasant, fear and enjoying music on the three channels of BIOPAC EEG data acquisition system — EEG, Alpha and Alpha RMS signal. Also the time and time-frequency-based features were extracted to compare the performance of the system. After feature extraction, the channel efficacy is evaluated by support vector machine (SVM) based on the classification rate in different cognitive states. From the experimental results and classification accuracy, it is determined that the overall accuracy for alpha channel shows much improved result for power spectral density than the other frequency based features and other channels. The classification rate is 69.17% for alpha channel whereas for EEG and alpha RMS channel it is found 47.22% and 32.21%, respectively. For statistical analysis standard deviation shows better result for alpha channel and it is found 65.4%. The time-frequency analysis shows much improved result for alpha channel also. For the mean value of DWT coefficients the accuracy is highest and it is 81.3%. Besides the classification accuracy, SVM shows better performance in compare with kNN classifier.

This paper presents a method to characterize, identify and classify some pathological Electroencephalogram (EEG) signals. We use some Time Frequency Distributions (TFDs) to analyze its nonstationarity. The analysis is conducted by the spectrogram (SP), the Choi–Williams Distribution (CWD) and the Smoothed Pseudo Wigner Ville Distribution (SPWVD). The studies are carried on some real EEG signals collected from a known database. The estimation of the best value of parameters for each distribution is achieved using the Rényi entropy (RE). The time-frequency results have permitted to characterize some pathological EEG signals. In addition, the Rényi Marginal Entropy (RME) is used for the purpose of detecting the peak seizures and discriminates between normal and pathological EEG signals. The frequency bands are evaluated using the Marginal Frequency (MF). The EEG signal classification of two sets A and E containing normal and pathologic EEG signals, respectively, is performed using our proposed method based on energy extraction of signals from time-frequency plane. Also, the Moving Average (MA) is used as a tool to obtain better classification results. The results conducted on real-life EEG signals illustrate the effectiveness of the proposed method.

This paper presents a fully integrated front-end, low noise amplifier (LNA), dedicated to the processing of various types of bio-medical signals, such as Electrocardiogram (ECG), Electroencephalography (EEG), Axon Action Potential (AAP). A novel noise reduction technique, for an operational transconductance amplifier (OTA), has been proposed. This adds a current steering branch parallel to the differential pair, with a view to reducing the noise contribution by the cascode current sources. Hence, this reduces the overall input-referred noise of the LNA, without adding any additional power. The proposed technique implemented in 65nm CMOS technology achieves 30 dB closed-loop voltage gain, 0.05Hz lower cut-off frequency and 100 MHz 3-dB bandwidth. It operates at 1.2V power supply and draws 1$\mu$A static current. The prototype described in this paper occupies 3300$\mu$m² silicon area.

One of the important parameters in the brain–computer interface (BCI) system is speed. Therefore, it is always desirable to design a high-speed system that has an acceptable performance, simultaneously. The main idea of this paper is the use of evolutionary algorithms (EAs) to select the optimal features for epilepsy diagnosis by processing the electroencephalogram (EEG) signals. The lesser the number of features is, the higher will be the usefulness of accuracy of the system to us. Therefore, here, using EAs, some of the features that are redundant in the data and do not contain a lot of information and only increase the complexity of the system are eliminated and the best features are chosen. We select this choice by EAs. Running the feature selection step is after the feature extraction step. In fact, the features were extracted using the common spatial pattern (CSP) algorithm, and then the optimal features were selected from the extracted feature set. This can save a lot of system complexity and reduce system execution time considerably. Finally, at the diagnostic stage, these selected features are given to a simple neural network (NN). The results showed that when the combination of EA and CSP is used, the precision of the system is much higher than when the CSP method is only used, although it contributes significantly to the complexity of the system.

Inverse algorithms are used to assess EEG source parameters. This involves identifying unknown voxels in hundreds of different regions, giving an incomplete picture of the brain. There are no uniform solutions since the same sensor output may come from many source configurations. To overcome the lack of uniqueness, one must consider previous information and parameters inherent in the source. Our goal is to predict the location of brain sources from the recorded EEG signal without any prior knowledge of sources. We applied a particle filter to locate the brain sources in this article. The degeneracy of particle weights limits the particle filter’s performance. Various resampling techniques are suggested to address this problem. The performance of the branching resampling approach is compared to a systematic resampling method for brain source localization. To perform assessment and comparison studies, both simulated and real EEG data are used.

EEG-based brain–computer interface systems have demonstrated impressive capability in the context of the broad use of deep learning. However, serious security flaws have been shown by their vulnerability to adversarial sample attacks. It is not possible to directly apply traditional attack strategies from the image domain to EEG signals because of their unique visualization and dynamic features. A unique multi-target smooth universal adversarial perturbation (MS-UAP) approach designed specifically for EEG data is presented in this paper to address this difficulty. We define a loss function and optimize parameters based on UAP for both target and nontarget class samples, respectively, to address the temporal nature of the EEG signal and the attack’s flexibility. Additionally, this research generates a smoothed adversarial sample by convolution operations, yielding a more disorienting effect, to avoid the introduction of physiologically untrustworthy square wave distortions caused by typical visual attack approaches. Three public EEG datasets were used for extensive testing and assessments, and the results show that MS-UAP successfully targets convolutional neural network (CNN) classifiers and launches simultaneous attacks on several classes, exhibiting strong model transferability. To ensure accurate attacks that go undetected, MS-UAP creates a single disturbance that is targeted at target classes and has the least negative effect on other classes. In order to facilitate effective real-time attacks, MS-UAP, an offline-generated universal perturbation template, can be smoothly incorporated into EEG data. The study is significant since it emphasizes how important it is to improve BCI system security and promote the creation of stronger defenses.

A general method for the creation of strange nonchaotic attractor is proposed. As an example, the strange nonchaotic attractor in a high-dimensional globally coupled neural system is studied numerically. For such an attractor, the time interval of continuously positive finite-time Lyapunov exponent must be smaller than the period of the driving stimulus, although it has a long-time positive tail. The intermittency between laminar and burst behavior is a characteristic dynamic of the strange nonchaotic attractors. Simulation results show that the chaotic phase occurs only within a small region around the origin in the parameter space. More than half of the large nonchaotic region is the strange nonchaotic phase. The chaotic phase is typically surrounded by strange nonchaotic attractors. This result also suggests that some biological signals that have a strange structure may be nonchaotic rather than chaotic.

The present report describes the dynamic foundations of long-standing experimental work in the field of oscillatory dynamics in the human and animal brain. It aims to show the role of multiple oscillations in the integrative brain function, memory, and complex perception by a recently introduced conceptional framework: the super-synergy in the whole brain. Results of recent experiments related to the percept of the grandmother-face support our concept of super-synergy in the whole brain in order to explain manifestation of Gestalts and Memory-Stages. This report may also provide new research avenues in macrodynamics of the brain.