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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.

This article discusses the use of a brain–computer interface (BCI) to obtain emotional feedback from a human in response to the motion of humanoid robots in collaborative environments. The purpose of this study is to detect the human satisfaction level and use it as a feedback for correcting and improving the behavior of the robot to maximize human satisfaction. This article describes experiments and algorithms that use human brains activity collected through BCI in order to estimate the level of satisfaction. Users wear an electroencephalogram (EEG) headset and control the movement of the robot by mental imagination. The robots responds to the mental imagination may not be the same as human mental command and this will affect the emotional satisfaction level. The headset records brain activity from 14 locations on the scalp. Power spectral density of each EEG frequency band and four largest Lyapunov exponents of each EEG signal form the feature vector. The Mann–Whitney–Wilcoxon test is then used to rank all the features. The highest rank features are then selected to train a linear discriminant classifier (LDC) to determine the satisfaction level. Our experimental results show an accuracy of 79.2% in detecting the human satisfaction level.

The corticomuscular coupling (CMC) characterization between the motor cortex and muscles during motion control is a valid biomarker of motor system function after stroke, which can improve clinical decision-making. However, traditional CMC analysis is mainly based on the coherence method that can’t determine the coupling direction, whereas Granger Causality (GC) is limited in identifying linear cause–effect relationship. In this paper, a time-frequency domain copula-based GC (copula-GC) method is proposed to assess CMC characteristic. The 32-channel electroencephalogram (EEG) signals over brain scalp and electromyography (EMG) signals from upper limb were recorded during controlling and maintaining steady-state force output for five stroke patients and five healthy controls. Then, the time-frequency copula-GC analysis was applied to evaluate the CMC strength in both directions. Experimental results show that the CMC strength of descending direction is greater than that of ascending direction in the time domain for healthy controls. With the increase of grip strength, the bi-directional CMC strength has an increasing trend. Meanwhile, the bi-directional CMC strength of right hand is larger than that of left hand. In addition, the bi-directional CMC strength of stroke patients is lower than that of healthy controls. In the frequency domain, the strongest CMC is observed at the beta frequency band. Additionally, the CMC strength of descending direction is slightly larger than that of ascending direction in healthy controls, while the CMC strength of descending direction is lower than that of ascending direction in stroke patients. We suggest that the proposed time-frequency domain analysis approach based on copula-GC can effectively detect complex functional coupling between cortical oscillations and muscle activities, and provide a potential quantitative analysis measure for motion control and rehabilitation evaluation.

Automatic detection of the current task load of a surgeon in the theatre in real time could provide helpful information, to be used in supportive systems. For example, such information may enable the system to automatically support the surgeon when critical or stressful periods are detected, or to communicate to others when a surgeon is engaged in a complex maneuver and should not be disturbed. Passive brain–computer interfaces (BCI) infer changes in cognitive and affective state by monitoring and interpreting ongoing brain activity recorded via an electroencephalogram. The resulting information can then be used to automatically adapt a technological system to the human user. So far, passive BCI have mostly been investigated in laboratory settings, even though they are intended to be applied in real-world settings. In this study, a passive BCI was used to assess changes in task load of skilled surgeons performing both simple and complex surgical training tasks. Results indicate that the introduced methodology can reliably and continuously detect changes in task load in this realistic environment.

This study investigates the effect of haptic control strategies on a subject’s mental engagement during a fine motor handwriting rehabilitation task. The considered control strategies include an error-reduction (ER) and an error-augmentation (EA), which are tested on both dominant and nondominant hand. A noninvasive brain–computer interface is used to monitor the electroencephalogram (EEG) activities of the subjects and evaluate the subject’s mental engagement using the power of multiple frequency bands (theta, alpha, and beta). Statistical analysis of the effect of the control strategy on mental engagement revealed that the choice of the haptic control strategy has a significant effect ($p<0.001$) on mental engagement depending on the type of hand (dominant or nondominant). Among the evaluated strategies, EA is shown to be more mentally engaging when compared with the ER under the nondominant hand.

The purpose of this study is to verify that EEG is composed of frequency components with chaotic characteristics. EEG data are decomposed into frequency components, and then the chaos and fractal analyses are applied. More precisely, the running spectrum of EEG is derived and the chaos analysis is applied evaluating the Lyapunov spectrum and the correlation dimension. As a result, the existence of the chaotic phenomenon is found for most of the frequency components used as the objects of the analysis. It is also found that the fractal properties exist in most of the frequency components. The multifractal analysis is applied to the EEG data, and the q-th order generalised dimension is evaluated. It is seen as a result that the reconstructed attractors of EEG are characterized by a nonuniform fractal distributions, which are represented by a large number of scaling indices concerning with the dimension.

The Electroencephologram (EEG) experiments were designed in this paper to emulate our daily visual working memory task and demonstrate the effect of image target numbers and background textures on our brains’ visual working memory. This paper discovered that there is a possibly of implementing a neural network to predict memoryrelated brain activity in a visual working memory experiment, where participants were presented with images of different target item numbers and asked to remember as many target objects as possible. Both the Multi-Layer Perceptron (MLP) network and support vector machine (SVM) were used as training methods for the prediction. The prediction results are consistent with the actual EEG power variation observed in the experiment, which demonstrate the effect of target item number and background texture on the level of difficulty in image memorization.