State EEG Research Articles

Wearable Frontal EEG for Screening Cognitive Impairment

AbstractBackgroundAlzheimer's disease (AD) is a neurodegenerative disorder that is characterized by progressive cognitive decline. Early detection of AD is essential for timely intervention and treatment. The objective of this study is to explore the potential usefulness of virtual reality (VR) tasks combined with frontal EEG recordings for early detection of cognitive impairment in individuals with SCD (subject cognitive decline), MCI (mild cognitive impairment), and AD (Alzheimer's disease). We aimed to analyze the results and compared them with those of a control group with normal cognition.MethodResting‐state EEG was recorded in the awake‐resting state with closed eyes, and task‐induced EEG recordings were obtained during VR tasks from 108 participants. The participants included those with SCD, MCI, and AD, and they were compared with 18 subjects with normal cognition. Alpha reactivity was determined by measuring the decrease in relative alpha power over frontal electrodes.ResultThis study observed a significant decrease in alpha reactivity in the order of SCD, MCI, and AD compared to the control group with normal cognition in EEG recordings (P‐value < 0.05). Additionally, the decibel power ratio of the EEG recorded during cognitive tasks exhibited a reversed order of decrease compared to the resting state across the same diagnostic groups.ConclusionThis study shows that when performing cognitive tasks with a virtual reality (VR) device on the forehead, distinct features for Alzheimer's disease are observed compared to the healthy control group. While larger and longer‐term studies are necessary, these findings suggest that cost‐effective digital biomarkers using measurable EEG could be valuable for early Alzheimer's diagnosis.

Prediction of long‐term treatment outcome in Alzheimer's disease based on EEG connectivity

AbstractBackgroundIt is difficult to predict of long‐term treatment response of AD to medical treatment when starting medication. We explored EEG brain connectivity as a potential biomarker for long‐term medication outcomes in patients with AD.MethodsResting‐state EEG was recorded from a total of 56 AD patients (mean age = 73.23±6.39) when starting medication after diagnosis of AD. EEG was recorded in 32 channels. We divided the patients into two groups, good and poor respondent groups according to clinical evaluation after treatment and dosage changes of AD medication with MMSE score change. Starting dosage of AchE inhibitor, donepezil was 7.75±3.85mg and the mean treatment period is 104.05±57.93 weeks. The mean final medication dosages are donepezil 13.23±6.93mg and memantine 9.52±9.68mg. Initial MMSE scores of good respondent group (n=32, M=11, F=21) is 20.06±4.35 and poor respondent group (n=24, M=7, F=17) is 19.63±4.07. We analyzed brain EEG connectivity among the 246 cortical regions defined by the Brainnetome atlas.ResultsOur study found that the difference in brain EEG connectivity, especially intrahemispheric regional connectivity in gamma frequency bands, is increased at multiple regions of right hemisphere such as between Brodmann Area 44 and 39, such as DLFPC and hippocampal areas, DLFPC and caudate nucleus, middle frontal and inferior parietal areas in poor respondent group (p<0.00001). In alpha frequency band, EEG connectivity between right DLPFC(BA 9/46) and right paracentral lobule of frontal lobe (BA 4) is increased in good respondent group (p<0.0001). In theta frequency band, EEG connectivity in the areas of frontal lobe (BA 4 and 6) is decreased in good respondent group (p<0.0001).ConclusionsThis study shows increased gamma bands EEG connectivity between right anterior and posterior areas in poor respondent group. In general cortical gamma‐band activity is increased by selective attention in normal populations and related to cholinergic system. Elevated gamma band power was observed in AD when compared to MCI and control subjects was reported in a previous study. Our results suggest the potential of the gamma EEG connectivity of initial EEG when starting treatment as a biomarker to predict long‐term medical treatment for AD medication.

Brain state dynamics differ between eyes open and eyes closed rest.

The human brain exhibits spatio-temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) eyes open (EO) and eyes closed (EC) resting-state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG-defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window-based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting-state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha-BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in-depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.

Brain–Computer Interface Based on PLV-Spatial Filter and LSTM Classification for Intuitive Control of Avatars

This study researches the combination of the brain–computer interface (BCI) and virtual reality (VR) in order to improve user experience and facilitate control learning in a safe environment. In addition, it assesses the applicability of the phase-locking value spatial filtering (PLV-SF) method and the Short-Term Memory Network (LSTM) in a real-time EEG-based BCI. The PLV-SF has been shown to improve signal quality, and the LSTM exhibits more stable and accurate behavior. Ten healthy volunteers, six men and four women aged 22 to 37 years, participated in tasks inside a virtual house, using their EEG states to direct their movements and actions through a commercial, low-cost wireless EEG device together with a virtual reality system. A BCI and VR can be used effectively to enable the intuitive control of virtual environments by immersing users in real-life situations, making the experience engaging, fun, and safe. Control test times decreased significantly from 3.65 min and 7.79 min in the first and second quartiles, respectively, to 2.56 min and 4.28 min. In addition, a free route was performed for the three best volunteers who finished in an average time of 6.30 min.

Open access EEG dataset of repeated measurements from a single subject for microstate analysis

Electroencephalography (EEG) microstate analysis is a neuroimaging analytical method that has received considerable attention in recent years and is widely used for analysing EEG signals. EEG is easily influenced by internal and external factors, which can affect the repeatability and stability of EEG microstate analysis. However, there have been few reports and publicly available datasets on the repeatability of EEG microstate analysis. In the current study, a 39-year-old healthy male underwent a total of 60 simultaneous electroencephalography and electrocardiogram measurements over a period of three months. After the EEG recording was completed, magnetic resonance imaging (MRI) was also conducted. To date, this EEG dataset has the highest number of repeated measurements for one individual. The dataset can be used to assess the stability and repeatability of EEG microstates and other analytical methods, to decode resting EEG states among subjects with open eyes, and to explore the stability and repeatability of cortical spatiotemporal dynamics through source analysis with individual MRI.

EEG and acute confusional state at the emergency department

ObjectivesAcute confusional state (ACS) is a common cause of admission to the emergency department (ED). It can be related to numerous etiologies. Electroencephalography (EEG) can show specific abnormalities in cases of non-convulsive status epilepticus (NCSE), or metabolic or toxic encephalopathy. However, up to 80% of patients with a final diagnosis of NCSE have an ACS initially attributed to another cause. The exact place of EEG in the diagnostic work-up remains unclear. MethodsData of consecutive patients admitted to the ED for an ACS in a two-year period and who were referred for an EEG were collected. The initial working diagnosis was based on medical history, clinical, biological and imaging investigations allowing classification into four diagnostic categories. Comparison to the final diagnosis was performed after EEG recordings (and sometimes additional tests) were performed, which allowed the reclassification of some patients from one category to another. ResultsSeventy-five patients (mean age: 71.1 years) were included with the following suspected diagnoses: seizures for 8 (11%), encephalopathy for 14 (19%), other cause for 34 (45%) and unknown for 19 (25%). EEG was recorded after a mean of 1.5 days after symptom onset, and resulted in the reclassification of patients as follows: seizure for 15 (20%), encephalopathy for 15 (20%), other cause for 29 (39%) and unknown cause for 16 (21%). Moreover, ongoing epileptic activity (NCSE or seizure) and interictal epileptiform activity were found in eight (11%) patients initially diagnosed in another category. DiscussionIn our cohort, EEG was a key examination in the management strategy of ACS in 11% of patients admitted to the ED. It resulted in a diagnosis of epilepsy in these patients admitted with unusual confounding presentations.

Dysmaturation of sleep state and electroencephalographic activity after hypoxia-ischaemia in preterm fetal sheep

Antenatal hypoxia-ischaemia (HI) in preterm fetal sheep can trigger delayed evolution of severe, cystic white matter injury (WMI), in a similar timecourse to WMI in preterm infants. We therefore examined how severe hypoxia-ischaemia affects recovery of electroencephalographic (EEG) activity. Chronically instrumented preterm fetal sheep (0.7 gestation) received 25 min of complete umbilical cord occlusion (UCO, n = 9) or sham occlusion (controls, n = 9), and recovered for 21 days. HI was associated with a shift to lower frequency EEG activity for the first 5 days with persisting loss of EEG power in the delta and theta bands, and initial loss of power in the alpha and beta bands in the first 14 days of recovery. In the final 3 days of recovery, there was a marked rhythmic shift towards higher frequency EEG activity after UCO. The UCO group spent less time in high-voltage sleep, and in the early evening (7:02 pm ± 47 min) abruptly stopped cycling between sleep states, with a shift to a high frequency state for 2 h 48 min ± 40 min, with tonic electromyographic activity. These findings demonstrate persisting EEG and sleep state dysmaturation after severe hypoxia-ischaemia. Loss of fetal or neonatal sleep state cycling in the early evening may be a useful biomarker for evolving cystic WMI.

Recognizing the situation awareness of forklift operators based on EEG techniques in a field experiment.

Lack of situation awareness (SA) is the primary cause of human errors when operating forklifts, so determining the SA level of the forklift operator is crucial to the safety of forklift operations. An EEG recognition approach of forklift operator SA in actual settings was presented in order to address the issues with invasiveness, subjectivity, and intermittency of existing measuring methods. In this paper, we conducted a field experiment that mimicked a typical forklift operation scenario to verify the differences in EEG states of forklift operators with different SA levels and investigate the correlation of multi-band combination features of each brain region of forklift operators with SA. Based on the sensitive EEG combination indexes, Support Vector Mechanism was used to construct a forklift operator SA recognition model. The results revealed that there were differences between forklift operators with high and low SA in the θ, α, and β frequency bands in zones F, C, P, and O; combined EEG indicators θ/β, (α + θ)/(α + β), and θ/(α + β) in zones F, P, and C were significantly correlated with SA; the recognition accuracy of the model reached 88.64% in the case of combined EEG indicators of zones C & F & P as input. It could provide a reference for SA measurement, contributing to the improvement of SA.

EEG microstate quantifiers and state space descriptors during anaesthesia in patients with postoperative delirium: a descriptive analysis.

Postoperative delirium is a serious sequela of surgery and surgery-related anaesthesia. One recommended method to prevent postoperative delirium is using bi-frontal EEG recording. The single, processed index of depth of anaesthesia allows the anaesthetist to avoid episodes of suppression EEG and excessively deep anaesthesia. The study data presented here were based on multichannel (19 channels) EEG recordings during anaesthesia. This enabled the analysis of various parameters of global electrical brain activity. These parameters were used to compare microstate topographies under anaesthesia with those in healthy volunteers and to analyse changes in microstate quantifiers and EEG global state space descriptors with increasing exposure to anaesthesia. Seventy-three patients from the Surgery Depth of Anaesthesia and Cognitive Outcome study (SRCTN 36437985) received intraoperative multichannel EEG recordings. Altogether, 720 min of artefact-free EEG data, including 210 min (29.2%) of suppression EEG, were analysed. EEG microstate topographies, microstate quantifiers (duration, frequency of occurrence and global field power) and the state space descriptors sigma (overall EEG power), phi (generalized frequency) and omega (number of uncorrelated brain processes) were evaluated as a function of duration of exposure to anaesthesia, suppression EEG and subsequent development of postoperative delirium. The major analyses involved covariate-adjusted linear mixed-effects models. The older (71 ± 7 years), predominantly male (60%) patients received a median exposure of 210 (range: 75-675) min of anaesthesia. During seven postoperative days, 21 patients (29%) developed postoperative delirium. Microstate topographies under anaesthesia resembled topographies from healthy and much younger awake persons. With increasing duration of exposure to anaesthesia, single microstate quantifiers progressed differently in suppression or non-suppression EEG and in patients with or without subsequent postoperative delirium. The most pronounced changes occurred during enduring suppression EEG in patients with subsequent postoperative delirium: duration and frequency of occurrence of microstates C and D progressed in opposite directions, and the state space descriptors showed a pattern of declining uncorrelated brain processes (omega) combined with increasing EEG variance (sigma). With increasing exposure to general anaesthesia, multiple changes in the dynamics of microstates and global EEG parameters occurred. These changes varied partly between suppression and non-suppression EEG and between patients with or without subsequent postoperative delirium. Ongoing suppression EEG in patients with subsequent postoperative delirium was associated with reduced network complexity in combination with increased overall EEG power. Additionally, marked changes in quantifiers in microstate C and in microstate D occurred. These putatively adverse intraoperative trajectories in global electrical brain activity may be seen as preceding and ultimately predicting postoperative delirium.

Nonlinear and chaos features over EMD/VMD decomposition methods for ictal EEG signals detection

The detection and identification of epileptic seizures attracted considerable relevance for the neurophysiologists. In order to accomplish the detection of epileptic seizures or equivalently ictal EEG states, this paper proposes the use of nonlinear and chaos features not computed over the raw EEG signals as it was commonly experienced, but instead over intrinsic mode functions (IMFs) extracted subsequently to the application of newly time-frequency signal decomposition methods on the basis of empirical mode decomposition (EMD) and variational mode decomposition (VMD) methods. The first step within the proposed methodology is to excerpt the various components of the IMFs by EMD and VMD decomposition methods on time EEG segments. The Hjorth parameters, the Hurst exponent, the Recurrence Quantification Analysis (RQA), the detrended fluctuation analysis (DFA), the Largest Lyapunov Exponent (LLE), The Higuchi and Katz fractal dimensions (HFD and KFD), seven nonlinear and chaos features computed over the IMFs were investigated and their classification performances evaluated using the k-nearest neighbor (KNN) and the multilayer perceptron neural network (MLPNN) classifiers. Furthermore, the combination of the best nonlinear features has also been examined in terms of sensitivity, specificity and overall classification accuracy. The publicly available Bonn EEG dataset has been has been employed to validate the efficiency of the proposed method for detecting ictal EEG signals from normal or interictal EEG segments. Among the several experiments involved in the current study, the ultimate results establish that the overall classification accuracy can achieve 100%, 99.45%, 99.8%, 99.8%, 98.6% and 99.1% for six different epileptic seizure detection case problems studied, confirming the ability of the proposed methodology in helping the clinic practitioners in the epilepsy detection care units to classify seizure events with a great confidence.

A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG Analysis.

Machine learning methods have frequently been applied to electroencephalography (EEG) data. However, while supervised EEG classification is well-developed, relatively few studies have clustered EEG, which is problematic given the potential for clustering EEG to identify novel subtypes or patterns of dynamics that could improve our understanding of neuropsychiatric disorders. There are established methods for clustering EEG using manually extracted features that reduce the richness of the feature space for clustering, but only a couple studies have sought to use deep learning-based approaches with automated feature learning to cluster EEG. Those studies involve separately training an autoencoder and then performing clustering on the extracted features, and the separation of those steps can lead to poor quality clustering. In this study, we propose an explainable convolutional autoencoder-based approach that combines model training with clustering to yield high quality clusters. We apply the approach within the context of schizophrenia (SZ), identifying 8 EEG states characterized by varying levels of δ activity. We also find that individuals who spend more time outside of the dominant state tend to have increased negative symptom severity. Our approach represents a significant step forward for clustering resting-state EEG data and has the potential to lead to novel findings across a variety of neurological and neuropsychological disorders in future years.

Diversity of electroencephalographic patterns during propofol-induced burst suppression.

Burst suppression is a brain state consisting of high-amplitude electrical activity alternating with periods of quieter suppression that can be brought about by disease or by certain anesthetics. Although burst suppression has been studied for decades, few studies have investigated the diverse manifestations of this state within and between human subjects. As part of a clinical trial examining the antidepressant effects of propofol, we gathered burst suppression electroencephalographic (EEG) data from 114 propofol infusions across 21 human subjects with treatment-resistant depression. This data was examined with the objective of describing and quantifying electrical signal diversity. We observed three types of EEG burst activity: canonical broadband bursts (as frequently described in the literature), spindles (narrow-band oscillations reminiscent of sleep spindles), and a new feature that we call low-frequency bursts (LFBs), which are brief deflections of mainly sub-3-Hz power. These three features were distinct in both the time and frequency domains and their occurrence differed significantly across subjects, with some subjects showing many LFBs or spindles and others showing very few. Spectral-power makeup of each feature was also significantly different across subjects. In a subset of nine participants with high-density EEG recordings, we noted that each feature had a unique spatial pattern of amplitude and polarity when measured across the scalp. Finally, we observed that the Bispectral Index Monitor, a commonly used clinical EEG monitor, does not account for the diversity of EEG features when processing the burst suppression state. Overall, this study describes and quantifies variation in the burst suppression EEG state across subjects and repeated infusions of propofol. These findings have implications for the understanding of brain activity under anesthesia and for individualized dosing of anesthetic drugs.

Bipolar disorder traits: An electroencephalogram systematic review

Bipolar disorder (BD) is a serious mental disorder that globally affected 40 million people in 2019. According to the National Alliance on Mental Illness (NAMI), the present state of scientific knowledge only permits psychiatrists to diagnose BD using subjective and imprecise questionnaires. Therefore, developing a diagnostic tool with objective and precise biomarkers should be a major focus of research in this field. Among the potential biomarkers for BD, electroencephalogram (EEG)-based signatures of BD are considered to be the most optimal marker due to their strong links with behavioural symptoms and also their non-invasiveness. The goal of this review is to give a detailed summary of current techniques for investigating the traces of BD through EEG abnormalities. In this review, 13 studies from databases such as ScienceDirect and PubMed seeking to utilize EEG characteristics to diagnose BD were selected. The search keywords were “EEG in BD diagnosis”, “EEG microstates in BD”, and “EEG features for BD patients”. The publication date was set from 2007 to 2021. From these studies, we synthesize the effects of BD on each EEG feature, as well as detail the pros and cons when using each feature as a biomarker for BD. Results showed that EEG microstates demonstrate their potential among the seven EEG properties discussed in this article, as shown by several studies. By definition, EEG microstates are a dynamic representation of the spatial distribution of the scalp's electric potential as it varies over time. Specifically, four microstate classes recorded in different brain regions are classified into A (right-frontal left-posterior), B (left-frontal right-posterior), C (midline frontal-occipital), and D (midline frontal topographies). Greater presence of microstate class B in BD patients during task-free resting states are a distinctive characteristic of BD patients from which BD can be differentiated from other psychiatric illnesses. Besides microstates, EEG resting states are also considered to have a bright future in BD diagnosis. Specifically, by investigating brain frequency bands, researchers have discovered that BD patients exhibit abnormal delta and alpha signals as compared to healthy controls (HCs). The abnormalities of microstate B in EEG microstate characteristics would be the most promising biomarker for detecting BD. In addition, anomalies in delta and alpha signals during resting EEG states are possible BD diagnostic indicators.

Real-Time Detection and Feedback of Canonical Electroencephalogram Microstates: Validating a Neurofeedback System as a Function of Delay.

Recent neurotechnology has developed various methods for neurofeedback (NF), in which participants observe their own neural activity to be regulated in an ideal direction. EEG-microstates (EEGms) are spatially featured states that can be regulated through NF training, given that they have recently been indicated as biomarkers for some disorders. The current study was conducted to develop an EEG-NF system for detecting “canonical 4 EEGms” in real time. There are four representative EEG states, regardless of the number of channels, preprocessing procedures, or participants. Accordingly, our 10 Hz NF system was implemented to detect them (msA, B, C, and D) and audio-visually inform participants of its detection. To validate the real-time effect of this system on participants’ performance, the NF was intentionally delayed for participants to prevent their cognitive control in learning. Our results suggest that the feedback effect was observed only under the no-delay condition. The number of Hits increased significantly from the baseline period and increased from the 1- or 20-s delay conditions. In addition, when the Hits were compared among the msABCD, each cognitive or perceptual function could be characterized, though the correspondence between each microstate and psychological ability might not be that simple. For example, msD should be generally task-positive and less affected by the inserted delay, whereas msC is more delay-sensitive. In this study, we developed and validated a new EEGms-NF system as a function of delay. Although the participants were naive to the inserted delay, the real-time NF successfully increased their Hit performance, even within a single-day experiment, although target specificity remains unclear. Future research should examine long-term training effects using this NF system.

HP35: Takotsubo cardiomyopathy complicated with non-convulsive status epilepticus

Takotsubo cardiomyopathy (TTC) is a cardiac condition that causes an unexpected waning of the heart occurring during a stressful emotional event and potentially leading to acute heart failure [Ghadri J 2018]. TTC can be associated with various complications including cardiogenic shock, life-threatening arrhythmias and cardiac arrest [Templin C 2015]. Here we present a case of a young woman with TTC complicating with cardiac arrest and non-convulsive status epilepticus and a good neurological outcome Case report: A 38-years-old woman with unremarkable past medical history, after a strong emotional distress presented acute chest pain, dyspnea, and loss of consciousness with perioral cyanosis and noisy breathing. Cardiopulmonary resuscitation was immediately initiated, which continued for 50 minutes until the appearance of the sinus rhythm on the ECG. Dilated apex and mid-ventricular segment akinesis were recorded on echocardiography. Brain CT was unremarkable. Continuous video-EEG monitoring showed malignant EEG patterns, firstly, suppressed and non-reactive background, followed 24h later by frontally predominant generalized spike and spike-wave discharges. Administration of diazepam 20 mg did not modify her clinical and EEG state, while infusion of phenobarbital 1200 mg [20mg/kg] resulted in significant EEG improvement. Under combined antiepileptic medication (i.e., valproate 500 mg bid and levetiracetam 750 mg bid, her EEG and clinical status improved in few days. Conclusion: Although non-convulsive status epilepticus after cardiac arrest is associated with poor outcome, our patient survived, with a Cerebral Performance Category score of 2. Good recovery in this case might be related to young age, reversible cardiomyopathy, and timely management of complications.

Unsupervised learning of brain state dynamics during emotion imagination using high-density EEG

This study applies adaptive mixture independent component analysis (AMICA) to learn a set of ICA models, each optimized by fitting a distributional model for each identified component process while maximizing component process independence within some subsets of time points of a multi-channel EEG dataset. Here, we applied 20-model AMICA decomposition to long-duration (1–2 h), high-density (128-channel) EEG data recorded while participants used guided imagination to imagine situations stimulating the experience of 15 specified emotions. These decompositions tended to return models identifying spatiotemporal EEG patterns or states within single emotion imagination periods. Model probability transitions reflected time-courses of EEG dynamics during emotion imagination, which varied across emotions. Transitions between models accounting for imagined “grief” and “happiness” were more abrupt and better aligned with participant reports, while transitions for imagined “contentment” extended into adjoining “relaxation” periods. The spatial distributions of brain-localizable independent component processes (ICs) were more similar within participants (across emotions) than emotions (across participants). Across participants, brain regions with differences in IC spatial distributions (i.e., dipole density) between emotion imagination versus relaxation were identified in or near the left rostrolateral prefrontal, posterior cingulate cortex, right insula, bilateral sensorimotor, premotor, and associative visual cortex. No difference in dipole density was found between positive versus negative emotions. AMICA models of changes in high-density EEG dynamics may allow data-driven insights into brain dynamics during emotional experience, possibly enabling the improved performance of EEG-based emotion decoding and advancing our understanding of emotion.

Dynamical Similarity of EEG State Transitions for Scoring Performance of a Mental Arithmetic Task

Dynamical Similarity of EEG State Transitions for Scoring Performance of a Mental Arithmetic Task

In Memoriam: Joe Kamiya, 1926–2021

In Memoriam: Joe Kamiya, 1926–2021

Interactive Smart Space for Single-Person Households Using Electroencephalogram through Fusion of Digital Twin and Artificial Intelligence

The core technology for building a smart space includes the capability to analyse the space for users using various sensors. The purpose of this study was to propose a personalised interactive smart space implementation model driven by the fusion of digital twin (DT) and artificial intelligence (AI) based on electroencephalogram (EEG) data. This study utilised a handheld EEG sensor to identify a user’s emotion information and focused on the connection with the space. A smart space for single-person households that responds to EEG-based biometric information was designed for an interactive space that can improve the current emotional state of the space user. The technical characteristics of DT and AI were analysed to control spatial changes according to the user’s emotional state and to address safety-related issues. Furthermore, a fusion mechanism for DT and AI was developed for intelligent motor control to change the dimensions of the space in order to improve the EEG state of the user. In addition, using an AI model that converts EEG data into emotional state information, the user’s emotional state was analysed, and key issues related to the spatial dimensions and change of space that induce psychological stability were investigated.

Vagus Nerve Stimulation Elicits Sleep EEG Desynchronization and Network Changes in Responder Patients in Epilepsy.

Neural desynchronization was shown as a key mechanism of vagus nerve stimulation (VNS) action in epilepsy, and EEG synchronization measures are explored as possible response biomarkers. Since brain functional organization in sleep shows different synchrony and network properties compared to wakefulness, we aimed to explore the effects of acute VNS on EEG-derived measures in the two different states of vigilance. EEG epochs were retrospectively analyzed from twenty-four VNS-treated epileptic patients (11 responders, 13 non-responders) in calm wakefulness and stage N2 sleep. Weighted Phase Lag Index (wPLI) was computed as connectivity measure of synchronization, for VNS OFF and VNS ON conditions. Global efficiency (GE) was computed as a network measure of integration. Ratios OFF/ON were obtained as desynchronization/de-integration index. Values were compared between responders and non-responders, and between EEG states. ROC curve and area-under-the-curve (AUC) analysis was performed for response classification. In responders, stronger VNS-induced theta desynchronization (p < 0.05) and decreased GE (p < 0.05) were found in sleep, but not in wakefulness. Theta sleep wPLI Ratio OFF/ON yielded an AUC of 0.825, and 79% accuracy as a response biomarker if a cut-off value is set at 1.05. Considering all patients, the VNS-induced GE decrease was significantly more important in sleep compared to awake EEGstate (p < 0.01). In conclusion, stronger sleep EEG desynchronization in theta band distinguishes responders to VNS therapy from non-responders. VNS-induced reduction of network integration occurs significantly more in sleep than in wakefulness.