Himanshu Kumar

Objective: Surgical decision‐making in temporal lobe epilepsy (TLE) faces a critical challenge in determining whether the hippocampus can be safely spared during anterior temporal resection, particularly when surgery involves the language‐dominant hemisphere. We investigated whether presurgical network control metrics derived from magnetoencephalography (MEG) can differentiate patients who achieved seizure freedom with hippocampal resection (HR) from those who achieved seizure freedom with hippocampal sparing (HS). Methods: We analyzed presurgical spike‐free interictal MEG in 25 TLE patients with seizure freedom after anterior temporal resection (19 with hippocampal resection, 6 with sparing). Functional networks constructed from MEG data were partitioned into communities using a Louvain‐based consensus clustering algorithm. Within identified hippocampal‐related communities from each patient, we applied control centrality analysis to quantify the influence of each node on community synchronizability. Group differences were assessed using Fisher’s exact tests with false discovery rate (FDR) correction applied within each frequency band and hemisphere. Leave‐one‐subject‐out (LOSO) sensitivity analysis assessed the robustness of findings to individual subjects. Results: After FDR correction, synchronization of the contralateral anterior superior temporal sulcus (STS) in the HS group at beta band emerged as the primary finding ( p = .001, q = .02). This finding showed 100% stability in LOSO and persisted in a subgroup analysis excluding patients with hippocampal seizure onset ( p = .004). At p < .05, additional region‐frequency differences formed a coherent anatomic pattern: the HR group showed higher synchronization fractions within ipsilateral limbic structures, whereas the HS group showed higher synchronization in the ipsilateral posterior hippocampus and contralateral temporal regions with 100% LOSO stability. Significance: Presurgical MEG‐based network control analysis identified contralateral anterior STS synchronization as a candidate biomarker for hippocampal sparing eligibility in TLE. This proof‐of‐concept finding warrants prospective validation as potential tools to support individualized surgical planning beyond localization‐based approaches.

Subject-independent emotion recognition from electroencephalography (EEG) is constrained by nonlinear neural dynamics and inter-subject variability. This study characterises nine bispectral quadratic phase coupling (QPC) descriptors extracted from frontal EEG rhythms of the DEAP dataset, selects a compact subset via a genetic algorithm under nested leave-one-subject-out (LOSO) cross-validation, and evaluates classification with an explainable boosting machine (EBM). Under matched preprocessing, the bispectral features outperform power spectral density and differential entropy baselines by 2.7–3.8 points F 1 for arousal and 2.0–2.8 points F 1 for valence (paired Wilcoxon, p < 0.05 after Bonferroni correction), supporting their utility as compact nonlinear descriptors. The pipeline achieves 70.39% accuracy for arousal (19 features) and 69.98% for valence (25 features) on DEAP, statistically comparable to GRU-Conv and BiDCNN under matched LOSO evaluation, while using approximately an order-of-magnitude faster inference and a substantially smaller model footprint. Application of the identical pipeline to the independent DREAMER dataset yields comparable within-dataset performance, while direct DEAP → DREAMER cross-dataset transfer remains challenging. The additive structure of the EBM provides per-feature shape functions and pairwise interactions that are consistent with established frontal asymmetry and cross-frequency coupling literature. Bispectral QPC descriptors under an inherently interpretable modelling framework thus offer a compact, transparent alternative to higher-dimensional black-box approaches for subject-independent EEG emotion classification.

Electroencephalography (EEG)-based emotional state assessment is widely preferred due to its noninvasiveness and nonradiation approach. However, these signals are highly nonstationary and multicomponent, demonstrating large intrasubject variability. Extracting time and frequency information simultaneously from EEG addresses these challenges to effectively recognise the valence emotional states. Traditional time–frequency (TF) approaches optimise either temporal or frequency resolution, resulting in failure to identify fast transient oscillatory emotional events. In this letter, an attempt has been made to recognize emotional valence using super-resolution-based superlet transform (SLT). For this, the preprocessed EEG signals during emotion-evoking audio–visual stimuli from publicly available database is considered. The EEG signals are decomposed into theta, alpha, beta, and gamma frequency bands and are subjected to SLT. The TF skewness and kurtosis are extracted from the SLT. The statistical significance of features is evaluated, and the features are applied to three machine learning algorithms: random forest, Adaboost, and k-nearest neighbor. The results show that the SLT-based TF spectrum is able to provide variations of frequency components associated with emotional valence. Both the features exhibit statistically significant $(p < 0.05)$ difference in the high-frequency gamma bands to characterize emotional valence. Among the classifiers, AdaBoost stands out as the most robust performer (F1 = 70.16%). Feature importance analysis highlights that SLT features from the fronto-central and parieto-occipital brain regions play a crucial role in valence detection. It appears that this method could be useful in analyzing various mental well-being conditions in clinical settings.

Emotion influences human life and impacts daily life activities. During emotional processes, physiological signals interact with each other instead of functioning separately. Although unimodal and multimodal approaches have been explored for emotion classification, there is a lack of inclusion of central and peripheral nervous system signal interaction-based approaches. In this study, an attempt has been made to characterize valance emotional states using Electroencephalogram (EEG)- Electrodermal activity (EDA) based coupling. For this, multimodal signals are obtained from the publicly available DEAP database (n=32 subjects). The EEG signals are decomposed into θ, α, β, and bands and EDA signals are decomposed into phasic and tonic components. Then two EEG, three EDA, and two EEG-EDA coupling-based features are extracted and applied to three classifiers namely Random Forest (RF), Linear discriminant analysis, and Adaptive boosting. In addition, SHAP analysis is performed to explain classifiers’ performance with respect to features. The result shows that the proposed approach is able to classify valence emotional states. The feature combination of EEG, EDA, and EEG-EDA coupling-based features with an RF classifier performs best with an F1-score of 68.21%. SHAP analysis in frontal electrodes with γ band obtained better discrimination among different valance states. This study underscores the significance of the coupling studies of EEG with EDA in classifying emotion. Therefore, the proposed approach can be extended to emotional state assessment in clinical settings.

Emotion influences human life and impacts daily life activities. The central nervous system (CNS) and peripheral nervous system (PNS) interact intricately to produce emotional experiences. There are various studies related to the interaction of the CNS-PNS during psychophysiological states. However, emotion-oriented, CNS-PNS phase synchronisation studies are limited. In this work, an attempt is made to investigate the interaction between the CNS and PNS using electroencephalogram (EEG) and photoplethysmogram (PPG) during emotional elicitation evoked by audio–visual stimuli. For this, EEG and PPG signals from the publicly accessible DEAP database are utilized. EEG signals are segmented into four frequency bands: theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–45 Hz). Subsequently, the instantaneous phases of EEG and PPG are computed and employed to determine the phase synchronization index (PSI). Finally, the Wilcoxon Rank-sum test is performed to estimate the statistical significance of the PSI in low verus high levels of arousal and valence emotional states. Results show that the theta band of the EEG has the highest phase synchronization with the PPG signals. In low arousal, there's higher phase synchronization, whereas high valence exhibits higher phase synchronization. The FC5, C3, PO3, and F4 electrodes are found to be statistically significant ( p < 0.01) for arousal. For valence PO4 ( p < 0.01) is found to be statistically significant in discriminating valence levels. It is also evident that PSI based on the theta and alpha bands can distinguish levels of arousal and valence. Hence, the proposed approach can be used to better understand the CNS-PNS interaction in various emotional states under clinical conditions.

Automated emotion recognition is crucial in identifying and monitoring psychological disorders. Although several electroencephalography (EEG)-based methods have been explored for emotion recognition, capturing the subtle oscillations within EEG signals associated with distinct emotional states remains a persistent challenge. Nonlinear phase-based functional connectivity (FC) can capture the intricate time-varying patterns of brain activity during the processing of emotions. In this work, an attempt has been made to characterize the EEG-based emotional states using nonlinear phase-based FC techniques. For this, the EEG signals are obtained from the publicly available DEAP database and decomposed into four frequency bands: Theta (4–7[ ]Hz), alpha (8–12[ ]Hz), beta (13–30[ ]Hz) and gamma (30–45[ ]Hz). Three nonlinear phase-based FC measures, namely phase lag index (PLI), phase locking value (PLV) and Rho index, are extracted from individual frequency bands. Two types of features, namely network features and FC indices, are fed to three classifiers, namely random forest (RF), extreme gradient boosting (XGB) and K-Nearest Neighbors (KNN). The results reveal that the proposed approach can capture EEG dynamics to characterize emotional states. The gamma band-based Rho index demonstrated prominence in discriminating arousal and valence emotional states. The utilization of the Rho index-based FC feature effectively reveals interactions among cortical brain regions in response to audio-visual stimuli. Thus, the proposed approach could be extended to classifying various emotional states in clinical settings.

The interaction between the central nervous system (CNS) and peripheral nervous system (PNS) governs various physiological functions, influences cognitive processes and emotional states. It is necessary to unravel the mechanisms governing the interaction between the brain and the body, enhancing our understanding of physical and mental well-being. Neuro-ergonomics-based human-computer interaction can be improved by comprehending the intricate interrelation between the CNS and PNS. Various studies have been explored using diverse methodologies to study CNS-PNS interaction in specific psychophysiological states, such as emotion, stress, or cognitive tasks. However, there is a need for a thorough, extensive, and systematic review covering diverse interaction forms, applications, and assessments. In this work, an attempt has been made to perform a systematic review that examines the interaction between the CNS and PNS across diverse psychophysiological states, focusing on varied physiological signals. For this, scientific repositories, namely Scopus, PubMed, Association for Computing Machinery, and Web of Science, are accessed. In total, 61 articles have been identified within the period of January 2008 to April 2023 for systematic review. The selected research articles are analyzed based on factors, namely subject information, stimulation modality, types of interactions between the brain and other organs, feature extraction techniques, classification methods, and statistical approaches. The evaluation of the existing literature indicates a scarcity of publicly available databases for CNS-PNS interaction and limited application of machine learning and deep learning-based advanced tools. Furthermore, this review underscores the urgent need for enhancements in several key areas including the development of a more refined psycho-physiological model, improved analysis techniques, and better electrode-surface interface technology. Additionally, there is a need for more research involving daily life activities, female-oriented studies, and privacy considerations. This review contributes to standardizing protocols, improves the diagnostic relevance of various instruments, and extracts more reliable biomarkers. The novelty of this study lies in guiding researchers to point out various issues and potential solutions for future research in the field of bio-signal-based CNS-PNS interaction.

Recognition of dichotomous emotional states such as happy and sad play important roles in many aspects of human life. Existing literature has recorded diverse attempts in extracting physiological and non-physiological traits to record these emotional states. Selection of the right instrumental approach for measuring these traits plays a critical role in emotion recognition. Moreover, various stimuli have been used to induce emotions. Therefore, there is a current need to perform a comprehensive overview of instrumental approaches and their outcomes for the new generation of researchers. In this direction, this study surveys the instrumental approaches in discriminating happy and sad emotional states that are elicited using audio-visual stimuli. A comprehensive literature review is performed using PubMed, Scopus, and ACM digital library repositories. The reviewed articles are classified with respect to the i) stimulation modality, ii) acquisition protocol, iii) instrumentation approaches, iv) feature extraction, and v) classification methods. In total, 39 research articles were published on the selected topic of instrumental approaches in differentiating dichotomous emotional states using audio-visual stimuli between January 2011 and April 2021. The majority of the papers used physiological traits, namely electrocardiogram, electrodermal activity, heart rate variability, photoplethysmogram, and electroencephalogram based instrumental approaches for recognizing the emotional states. The results show that only a few articles have focused on audio-visual stimuli for the elicitation of happy and sad emotional states. This review is expected to seed research in the areas of standardization of protocols, enhancing the diagnostic relevance of these instruments, and extraction of more reliable biomarkers.