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Over the past decades, among all the non-invasive brain stimulation (NIBS) techniques, those aiming for neuromodulatory protocols have gained special attention. The traditional neurophysiological outcome to estimate the neuromodulatory effect is the motor evoked potential (MEP), the impact of NIBS techniques is commonly estimated as the change in MEP amplitude. This approach has several limitations: first, the use of MEP limits the evaluation of stimulation to the motor cortex excluding all the other brain areas. Second, MEP is an indirect measure of brain activity and is influenced by several factors. To overcome these limitations several studies have used new outcomes to measure brain changes after neuromodulation techniques with the concurrent use of transcranial magnetic stimulation (TMS) and electroencephalogram (EEG). In the present review, we examine studies that use TMS-EEG before and after a single session of neuromodulatory TMS. Then, we focused our literature research on the description of the different metrics derived from TMS-EEG to measure the effect of neuromodulation.

Acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) is the most common subtype of acute encephalopathy in Japan and is difficult to differentiate from prolonged febrile seizures (PFSs). This study aimed to explore the capability of machine learning to differentiate AESD from PFSs on the basis of early electroencephalogram (EEG) analyses. Sixty one children with AESD ( n  = 20) or PFS ( n  = 41) were included. Digital EEG data with bipolar montage collected within 48 h (1–48 h) after seizure onset were analyzed using absolute power spectrum (APS) and phase lag index (PLI) values in each EEG frequency band. The APS values in the theta, alpha, beta, and gamma bands were lower for AESD than those for PFS. By contrast, the mean PLI values for all frequency bands were higher for AESD than for PFS. Machine learning analysis revealed that the APS value in the beta bands provided the highest differentiation accuracy and positive predictive value for AESD (68.8%). The mean APS values across all electrodes in the beta band may be a useful tool for differentiating between early-phase AESD and PFS. This study demonstrates the potential for early automated diagnosis of AESD and PFS using EEG analysis.

This definitive work provides an innovative methodology for biomedical signal analysis. It helps bridge the gap from visual analysis of EEGs to advanced signal processing techniques, serving as the first clear guide to adaptive approximations and the matching pursuit (MP) algorithm. You find a review of signal processing essentials, written in plain English, from sampling of analog signals and the inner product, to spectral and time-frequency methods of signal analysis. This groundbreaking book introduces adaptive approximations and the basics of the matching pursuit algorithm, and explains advantages and limitations in applications relying on EEG analysis. From the parameterization of EEG transients to selective estimates of energy of relevant structures, various applications in sleep, ERD/ERS, pharmaco-EEG, and epilepsy research are explained, each spotlighting a unique feature of the matching pursuit algorithm. This comprehensive resource provides full mathematical details for all applications, including tricks necessary for efficient MP implementation, to help you modify procedures as needed or design all-new frameworks for analysis of biomedical signals. Moreover, software used in the applications can be downloaded free from the author's website.

Neural field dynamics in the cerebral cortex exhibit complex spatiotemporal patterns inadequately captured by classical integer-order diffusion models that assume exponentially decaying spatial interactions. This study establishes a stochastic fractional FitzHugh–Nagumo framework incorporating power-law spatial correlations through fractional Laplacian operators, providing explicit parameterization of non-local cortical connectivity characteristics. The inverse problem of estimating fractional orders and model parameters from electroencephalographic data is addressed through multi-objective optimization with rigorous train–test validation. Systematic sensitivity analysis across the parameter space (αu,αv)∈[1.0,2.0]×[1.0,2.0] identifies optimal subdiffusive characteristics at αu=αv=1.5, corresponding to power-law spatial kernels C(x)∼|x|−1.5 consistent with anatomical connectivity measurements. The optimized model achieves out-of-sample performance R2=0.973 on held-out test data, approaching the measurement noise ceiling. While classical FitzHugh–Nagumo models achieve comparable test accuracy, the fractional framework provides enhanced interpretability through explicit spatial interaction parameterization. The fractional orders serve as quantitative biomarkers of cortical network organization, enabling data-driven characterization across brain states and neurological conditions. The methodology establishes computational foundations for clinical applications in epilepsy monitoring, neurodegenerative disease detection, and brain–computer interfaces.

Background The fatigue that users suffer when using steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can cause a number of serious problems such as signal quality degradation and system performance deterioration, users’ discomfort and even risk of photosensitive epileptic seizures, posing heavy restrictions on the applications of SSVEP-based BCIs. Towards alleviating the fatigue, a fundamental step is to measure and evaluate it but most existing works adopt self-reported questionnaire methods which are subjective, offline and memory dependent. This paper proposes an objective and real-time approach based on electroencephalography (EEG) spectral analysis to evaluate the fatigue in SSVEP-based BCIs. Methods How the EEG indices (amplitudes in δ, θ, α and β frequency bands), the selected ratio indices (θ/α and (θ + α)/β), and SSVEP properties (amplitude and signal-to-noise ratio (SNR)) changes with the increasing fatigue level are investigated through two elaborate SSVEP-based BCI experiments, one validates mainly the effectiveness and another considers more practical situations. Meanwhile, a self-reported fatigue questionnaire is used to provide a subjective reference. ANOVA is employed to test the significance of the difference between the alert state and the fatigue state for each index. Results Consistent results are obtained in two experiments: the significant increases in α and (θ + α)/β, as well as the decrease in θ/α are found associated with the increasing fatigue level, indicating that EEG spectral analysis can provide robust objective evaluation of the fatigue in SSVEP-based BCIs. Moreover, the results show that the amplitude and SNR of the elicited SSVEP are significantly affected by users’ fatigue. Conclusions The experiment results demonstrate the feasibility and effectiveness of the proposed method as an objective and real-time evaluation of the fatigue in SSVEP-based BCIs. This method would be helpful in understanding the fatigue problem and optimizing the system design to alleviate the fatigue in SSVEP-based BCIs.

Pharmaco-electroencephalography (EEG) is a non-invasive method used to assess the effects of pharmacological compounds on the central nervous system by processing the EEG signals which directly reveal the spontaneous synchronised postsynaptic neuronal activity of the cortex with high temporal resolution. The International Pharmaco-Encephalography Society (IPEG) has recently published guidelines, which were produced by a global panel of EEG experts, with the goal to increase the standardisation of pharmaco-EEG studies in human subjects and facilitate the comparability of data across laboratories, thus enabling data-pooling and meta-analyses. The recommended standard experimental procedure is to measure EEG activity under vigilance-controlled and resting conditions. The IPEG guidelines thoroughly present the technical details and therefore constitute a robust reference. The complementary aim of the present paper is to focus on practical aspects, pitfalls and precautions to be considered when processing pharmaco-EEG data by covering the following topics: (1) investigate the stability and reliability of 5-min EEG recordings under both vigilance-controlled and resting conditions; (2) assess the spontaneous time-dependent changes in spectral activity over time, and (3) apply the data-processing strategies suggested in the pharmaco-EEG guidelines and designed to optimally capture drug effects. For this purpose, the EEG data from a randomised, double-blind, crossover trial aimed at comparing the effect of diazepam (10 mg) and placebo in 16 healthy male volunteers is used to illustrate the discussion of the processing techniques and difficulties commonly faced when analysing pharmaco-EEG data.

Background: To explore the time-frequency structure and cross-scale coupling of electroencephalography (EEG) signals during seizure in juvenile myoclonic epilepsy (JME), correlations between different leads, as well as dynamic evolution in epileptic discharge, progression and end of seizure were examined. Methods: EEG data were obtained for 10 subjects with JME and 10 normal controls and were decomposed using gauss continuous wavelet transform (CWT). The phase amplitude coupling (PAC) relationship between the 11th (4.57 Hz) and 17th (0.4 Hz) scale was investigated. Correlations were examined between the 11th and 17th scale EEG signals in different leads during seizure, using multi-scale cross correlation analysis. Results: The time-frequency structure of JME subjects showed strong rhythmic activity in the 11th and 17th scales and a close PAC was identified. Correlation analysis revealed that the ictal JME correlation first increased in the anterior head early in seizure and gradually expanded to the posterior head. Conclusion: PAC was exhibited between the 11th and 17th scales during JME seizure. The results revealed that the correlation in the anterior leads was higher than the posterior leads. In the perictal period, the 17th scale EEG signal preceded the 11th scale signal and remained for some time after a seizure. This suggests that the 17th scale signal may play an important role in JME seizure.

Sleep apnea is a potentially serious sleep disorder characterised by abnormal pauses in breathing. Electroencephalogram (EEG) signal analysis plays an important role for detecting sleep apnea events. In this research work, a method is proposed on the basis of inter-band energy ratio features obtained from multi-band EEG signals for subject-specific classification of sleep apnea and non-apnea events. The K-nearest neighbourhood classifier is used for classification purpose. Unlike conventional methods, instead of classifying apnea patient and healthy person, the objective here is to differentiate apnea and non-apnea events of an apnea patient, which makes the task very challenging. Extensive experimentation is carried out on EEG data of several subjects obtained from a publicly available database. Comprehensive experimental results reveal that the proposed method offers very satisfactory classification performance in terms of sensitivity, specificity and accuracy.

As a diagnostic monitoring approach, electroencephalogram (EEG) signals can be decoded by signal processing methodologies for various health monitoring purposes. However, EEG recordings are contaminated by other interferences, particularly facial and ocular artifacts generated by the user. This is specifically an issue during continuous EEG recording sessions, and is therefore a key step in using EEG signals for either physiological monitoring and diagnosis or brain–computer interface to identify such artifacts from useful EEG components. In this study, we aim to design a new generic framework in order to process and characterize EEG recording as a multi-component and non-stationary signal with the aim of localizing and identifying its component (e.g., artifact). In the proposed method, we gather three complementary algorithms together to enhance the efficiency of the system. Algorithms include time–frequency (TF) analysis and representation, two-dimensional multi-resolution analysis (2D MRA), and feature extraction and classification. Then, a combination of spectro-temporal and geometric features are extracted by combining key instantaneous TF space descriptors, which enables the system to characterize the non-stationarities in the EEG dynamics. We fit a curvelet transform (as a MRA method) to 2D TF representation of EEG segments to decompose the given space to various levels of resolution. Such a decomposition efficiently improves the analysis of the TF spaces with different characteristics (e.g., resolution). Our experimental results demonstrate that the combination of expansion to TF space, analysis using MRA, and extracting a set of suitable features and applying a proper predictive model is effective in enhancing the EEG artifact identification performance. We also compare the performance of the designed system with another common EEG signal processing technique—namely, 1D wavelet transform. Our experimental results reveal that the proposed method outperforms 1D wavelet.

This chapter introduces a novel strategy to classify the human memory response involved in the face recognition task by utilizing the event‐related potential (ERP) signals. It introduces the composite effect of amplitude and phase angle of the signal in common spatial pattern (CSP) formulation and is solved using Lagrange's multiplier method, taking phase information of electroencephalography (EEG) into account. The chapter aims at designing a deep LSTM network with attention mechanism for classifying the human memory response captured by an EEG device during recognition of familiar and unfamiliar faces by utilizing ERPs. It presents a novel approach to utilize the composite benefits of deep LSTM and attention mechanism to realize the classification on a structural network. The chapter presents design issues of the proposed CSP for electrode selection and LSTM network for classification. It gives experimental details with detailed analysis of results.