Clinical EEG and Neuroscience最新文献

{"title":"Temporal and Spectral Properties of the Auditory Mismatch Negativity and P3a Responses in Schizophrenia.","authors":"Nancy B Lundin,&nbsp;Leah P Burroughs,&nbsp;Paul D Kieffaber,&nbsp;Jaime J Morales,&nbsp;Brian F O'Donnell,&nbsp;William P Hetrick","doi":"10.1177/15500594221089367","DOIUrl":"https://doi.org/10.1177/15500594221089367","url":null,"abstract":"<p><p>The mismatch negativity (MMN) event-related potential (ERP) indexes relatively automatic detection of changes in sensory stimuli and is typically attenuated in individuals with schizophrenia. However, contributions of different frequencies of electroencephalographic (EEG) activity to the MMN and the later P3a attentional orienting response in schizophrenia are poorly understood and were the focus of the present study. Participants with a schizophrenia-spectrum disorder (<i>n</i> = 85) and non-psychiatric control participants (<i>n</i> = 74) completed a passive auditory oddball task containing 10% 50 ms \"deviant\" tones and 90% 100 ms \"standard\" tones. EEG data were analyzed using spatial principal component analysis (PCA) applied to wavelet-based time-frequency analysis and MMN and P3a ERPs. The schizophrenia group compared to the control group had smaller MMN amplitudes and lower deviant-minus-standard theta but not alpha event-related spectral perturbation (ERSP) after accounting for participant age and sex. Larger MMN and P3a amplitudes but not latencies were correlated with greater theta and alpha time-frequency activity. Multiple linear regression analyses revealed that control participants showed robust relationships between larger MMN amplitudes and greater deviant-minus-standard theta inter-trial coherence (ITC) and between larger P3a amplitudes and greater deviant-minus-standard theta ERSP, whereas these dynamic neural processes were less tightly coupled in participants with a schizophrenia-spectrum disorder. Study results help clarify frequency-based contributions of time-domain (ie, ERP) responses and indicate a potential disturbance in the neural dynamics of detecting change in sensory stimuli in schizophrenia. Overall, findings add to the growing body of evidence that psychotic illness is associated with widespread neural dysfunction in the theta frequency band.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9696187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Relationship Between Cannabinoids and Neural Oscillations: How Cannabis Disrupts Sensation, Perception, and Cognition.","authors":"Shariful A Syed,&nbsp;Ashley M Schnakenberg Martin,&nbsp;Jose A Cortes-Briones,&nbsp;Patrick D Skosnik","doi":"10.1177/15500594221138280","DOIUrl":"https://doi.org/10.1177/15500594221138280","url":null,"abstract":"<p><p>Disruptions in neural oscillations are believed to be one critical mechanism by which cannabinoids, such as delta-9-tetrahyrdrocannabinol (THC; the primary psychoactive constituent of cannabis), perturbs brain function. Here we briefly review the role of synchronized neural activity, particularly in the gamma (30-80 Hz) and theta (4-7 Hz) frequency range, in sensation, perception, and cognition. This is followed by a review of clinical studies utilizing electroencephalography (EEG) which have demonstrated that both chronic and acute cannabinoid exposure disrupts neural oscillations in humans. We also offer a hypothetical framework through which endocannabinoids modulate neural synchrony at the network level. This also includes speculation on how both chronic and acute cannabinoids disrupt functionally relevant neural oscillations by altering the fine tuning of oscillations and the inhibitory/excitatory balance of neural circuits. Finally, we highlight important clinical implications of such oscillatory disruptions, such as the potential relationship between cannabis use, altered neural synchrony, and disruptions in sensation, perception, and cognition, which are perturbed in disorders such as schizophrenia.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9688626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special Issue: Update on Neural Oscillations in Neuropsychiatric Disorders.","authors":"Bahar Güntekin,&nbsp;Brian F O'Donnell","doi":"10.1177/15500594231181523","DOIUrl":"https://doi.org/10.1177/15500594231181523","url":null,"abstract":"The present issue highlights recent progress in the analysis of oscillatory activity for the assessment, understanding and remediation of psychiatric and neurologic disorders. Contemporary interest in neural synchrony and oscillations in neuropsychiatric disorders has been motivated by investigations of cellular and network oscillations, modeling of neural networks and advances in signal analysis. Hebb proposed that synchronous activation between two neurons strengthened connectivity between them; and that a reverberatory neural circuit could maintain a representation of a stimulus in memory. More recent findings implicate neural synchrony and oscillations in feature binding, attentional selection, arousal, memory operations and consciousness. Consequently, disturbances of oscillations within and between neural ensembles may contribute to sensory, behavioral and cognitive deficits in neuropsychiatric disorders. Because human EEG and MEG activity are generated by synchronized inhibitory and excitatory postsynaptic potentials within large ensembles of neurons, they are well suited for the detection of neural synchrony and oscillations with millisecond temporal resolution. In the present issue, investigators describe alterations of spontaneous or evoked oscillatory activity in a wide range of conditions, including Parkinson’s disease (Bayraktaroglu et al, 2023), Alzheimer’s disease (Fide et al, 2023), neurodegenerative disorders (Keller et al, 2023) depression (Riddle et al, 2023), schizophrenia (Basar Eroglu et al, 2023; Lundin et al, 2023; Peterson et al, 2023; Roach et al, 2023) and cannabis use disorders (Syed et al, 2023). These studies are broadly consistent with the hypothesis that disorders that affect signaling or connectivity among neurons will be associated with aberrant oscillatory activity. Moreover, these data indicate the potential of oscillatory measures for probing neuropathological mechanisms, identifying biomarkers, tracking course and predicting outcomes. Advances in signal analysis have been critical for the characterization of oscillatory activity in neuropsychiatric populations. At a single channel, time frequency analysis yields measures of frequency, amplitude and phase. Time-frequency analysis allows the assessment of the temporal dynamics of event-related oscillations before and after an event of interest (Delorme and Makeig, 2004; Keil et al, 2022). The papers in this issue draw on a rich array of methods, including resting power spectrum, event-related phase locking, phase delay, event-related power spectrum, coherence, and resting or event-related cross-frequency coupling. Lundin et al (2023) investigated the event-related power spectrum in patients with schizophrenia and Fide et al (2023) examined patients with Alzheimer’s disease patients. Event-related phase locking, also termed inter-trial coherence, is a measure of phase consistency across trials relative to a time locking event for a specific frequency band. In th","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9696319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transcranial Alternating Current Stimulation Alters Auditory Steady-State Oscillatory Rhythms and Their Cross-Frequency Couplings.","authors":"S. de la Salle, J. Choueiry, Mark Payumo, Matt Devlin, Chelsea Noel, A. Abozmal, M. Hyde, Renée Baysarowich, B. Duncan, V. Knott","doi":"10.2139/ssrn.4081702","DOIUrl":"https://doi.org/10.2139/ssrn.4081702","url":null,"abstract":"Auditory cortical plasticity deficits in schizophrenia are evidenced with electroencephalographic (EEG)-derived biomarkers, including the 40-Hz auditory steady-state response (ASSR). Aiming to understand the underlying oscillatory mechanisms contributing to the 40-Hz ASSR, we examined its response to transcranial alternating current stimulation (tACS) applied bilaterally to the temporal lobe of 23 healthy participants. Although not responding to gamma tACS, the 40-Hz ASSR was modulated by theta tACS (vs sham tACS), with reductions in gamma power and phase locking being accompanied by increases in theta-gamma phase-amplitude cross-frequency coupling. Results reveal that oscillatory changes induced by frequency-tuned tACS may be one approach for targeting and modulating auditory plasticity in normal and diseased brains.","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44036347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ikelos-RWA. Validation of an Automatic Tool to Quantify REM Sleep Without Atonia.","authors":"Alexandra Papakonstantinou, Jannis Klemming, Martin Haberecht, Dieter Kunz, Frederik Bes","doi":"10.1177/15500594231175320","DOIUrl":"10.1177/15500594231175320","url":null,"abstract":"<p><p><b><i>Study Objectives.</i></b> To present and evaluate an automatic scoring algorithm for quantification of REM-sleep without atonia (RWA) in patients with REM-sleep behaviour disorder (RBD) based on a generally accepted, well-validated visual scoring method, (\"Montreal\" phasic and tonic) and a recently developed, concise scoring method (Ikelos-RWA). <b><i>Methods.</i></b> Video-polysomnographies of 20 RBD patients (68.2 ± 7.2 years) and 20 control patients with periodic limb movement disorder (65.9 ± 6.7 years) were retrospectively analysed. RWA was estimated from chin electromyogram during REM-sleep. Visual and automated RWA scorings were correlated, and agreement (<i>a</i>) and Cohen's Kappa (<i>k</i>) calculated for 1735 minutes of REM-sleep of the RBD patients. Discrimination performance was evaluated with receiver operating characteristic (ROC) analysis. The algorithm was then applied on the polysomnographies of a cohort of 232 RBD patients (total analysed REM-sleep: 17,219 minutes) and evaluated, while correlating the different output parameters. <b><i>Results.</i></b> Visual and computer-derived RWA scorings correlated significantly (tonic Montreal: <i>r</i>_TM = 0.77; phasic Montreal: r_PM = 0.78; Ikelos-RWA: r_I = 0.97; all <i>p</i> < 0.001) and showed good to excellent Kappa coefficients (<i>k</i>_TM = 0.71; <i>k</i>_PM = 0.79; <i>k</i>_I = 0.77). The ROC analysis showed high sensitivities (95%-100%) and specificities (84%-95%) at the optimal operation points, with area under the curve (AUC) of 0.98, indicating high discriminating capacity. The automatic RWA scorings of 232 patients correlated significantly (<i>r</i>_TM{I} = 0.95; <i>r</i>_PM{I} = 0.91, <i>p</i> < 0.0001). <b><i>Conclusions.</i></b> The presented algorithm is an easy-to-use and valid tool for automatic RWA scoring in patients with RBD and may prove effective for general use being publicly available.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9481335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental Validation of the Cumulative MDRM in theP300 Speller Machine.","authors":"Fodil Zerrouki,&nbsp;Salah Haddab","doi":"10.1177/15500594221078166","DOIUrl":"https://doi.org/10.1177/15500594221078166","url":null,"abstract":"<p><p>The P300 speller Machine is among the leading applications of the electroencephalography (EEG)-based brain computer interfaces (BCIs), it is still a benchmark and a persistent challenge for the BCI Community. EEG signal classification represents the key piece of a BCI chain. The minimum distance to Riemannian mean (MDRM) belongs to these classification methods emerging in different BCI applications such as text spelling by thought. Based on a binary classification of each covariance matrix separately, character prediction is done according to the highest score across the whole set of all repetitions. Minimum cumulative distance to Riemannian mean (MCDRM) is a Cumulative variant of the MDRM, perfectly adapted to the P300 Speller Machine. The power of this variant is that prediction takes a more global proceeding involving the <i>n</i> repetitions together. Indeed, thanks to cumulative distances selected row and column are those having the covariance matrices both closer to the Target barycenter and farther from the non-Target one. This variant overcomes the main MDRM limitations as it improves inter-sessional generalization, allows optimal use of all repetitions and reduces considerably the risk of conflict appearing during the selection of rows and columns leading to character prediction. We applied this variant to the raw signals of Data set II-b of Berlin BCI and compared to the published results the MCDRM offers significantly higher results: 97.5% of correct predictions compared to the 96.5% of the competition winner. The MCDRM fits best with the P300 Speller machine, especially when dealing with noisy signals that requires intelligent and optimal usage of the <i>n</i> repetitions.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9315368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring the Awake and Anesthetized Unconscious States Using Bispectral Index and Electroencephalographic Connectivity Measures.","authors":"Marianne Cecilie Johansen Nævra,&nbsp;Luis Romundstad,&nbsp;Anders Aasheim,&nbsp;Pål Gunnar Larsson","doi":"10.1177/15500594221131680","DOIUrl":"https://doi.org/10.1177/15500594221131680","url":null,"abstract":"<p><p><i>Objective.</i> Our objective was to compare three electroencephalography (EEG)-based methods with anesthesiologist clinical judgment of the awake and anesthetized unconscious states. <i>Methods.</i> EEG recorded from 25 channels and from four channel bilateral Bispectral index (BIS) electrodes were collected from 20 patients undergoing surgery with general anesthesia. To measure connectivity we applied Directed Transfer Function (DTF) in eight channels of the EEG, and extracted data from BIS over the same time segments. Shannon's entropy was applied to assess the complexity of the EEG signal. Discriminant analysis was used to evaluate the data in relation to clinical judgment. <i>Results.</i> Assessing anesthetic state relative clinical judgment, the bilateral BIS gave the highest accuracy (ACC) (95.4%) and lowest false positive discovery rate (FDR) (0.5%) . Equivalent DTF gave 94.5% for ACC and 2.6% for FDR. Combining all methods gave ACC = 94.9% and FDR = 1%. Generally, entropy scored lower on ACC and higher on FDR than the other methods (ACC 90.87% and FDR 4.6%). BIS showed at least a one minute delay in 18 of the 20 patients. <i>Conclusions.</i> Our results show that BIS and DTF both have a high ACC and low FDR. Because of time delays in BIS values, we recommend combining the two methods.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/4c/58/10.1177_15500594221131680.PMC10084521.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9284116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Altered Dynamic Brain Connectivity in Individuals With Sickle Cell Disease and Chronic Pain Secondary to Hip Osteonecrosis.","authors":"Jamille Evelyn R S Santana,&nbsp;Abrahão F Baptista,&nbsp;Rita Lucena,&nbsp;Tiago da S Lopes,&nbsp;Raphael S do Rosário,&nbsp;Marjorie R Xavier,&nbsp;André Fonseca,&nbsp;José Garcia V Miranda","doi":"10.1177/15500594211054297","DOIUrl":"https://doi.org/10.1177/15500594211054297","url":null,"abstract":"<p><p>Individuals with sickle cell disease (SCD) exhibit changes in static brain connectivity in rest. However, little known as chronic pain associated with hip osteonecrosis affects dynamic brain connectivity during rest and the motor imagery task. The aim of this study was to investigate the characteristics of the dynamic functional brain connectivity of individuals with SCD and chronic pain secondary to hip osteonecrosis. This is a cross-sectional study comparing the dynamic brain connectivity of healthy individuals (n = 18) with the dynamic brain connectivity of individuals with SCD and chronic pain (n = 22). Individuals with SCD and chronic pain were stratified into high- or low-intensity pain groups based on pain intensity at the time of assessment. Dynamic brain connectivity was assessed through electroencephalography in 3 stages, resting state with eyes closed, and during hip (painful for the SCD individuals) and hand (control, nonpainful) motor imagery. Average weight of the edges and full synchronization time (FST)-time required for 95% of the possible edges to appear over time during a given task-were evaluated. Regarding the average weight of the edges, individuals with SCD and high-intensity pain presented higher edge weight during hip motor imagery. The average weight of the edges correlated positively with pain intensity and depression symptoms. Individuals with SCD and chronic pain complete the cerebral network at rest more quickly (lower FST). Individuals with SCD and chronic pain/hip osteonecrosis have impaired dynamic brain network with shorter FST in rest network and more pronounced diffuse connectivity in individuals with high-intensity pain. The dynamic brain network evaluated by time-varying graphs and motif synchronization was able to identify differences between groups.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9266859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early Neuroprognostication Using Frontal Spectrograms in Moderately Sedated Cardiac Arrest Patients.","authors":"Jae Hoon Lee","doi":"10.1177/15500594221074888","DOIUrl":"https://doi.org/10.1177/15500594221074888","url":null,"abstract":"<p><p><i>Introduction.</i> The integrated suppression ratio throughout all electroencephalography (EEG) patterns has rarely been studied. The aim of this study was to evaluate the clinical utility of the suppression ratio and hyperactivity of EEG on spectrograms. <i>Methods.</i> This prospective observational study included 73 cardiac arrest patients. Hardwired frontal EEG monitoring with spectrograms (color density spectral arrays, CDSA) was used to predict neurological outcomes. The mean suppression ratio (MSR) and hyperactivity in the high-frequency band (HHF) in the spectrogram were investigated in moderately sedated patients. Sedative doses were considered to estimate the MSR, which was automatically measured. <i>Results.</i> Using propofol 30 to 40 µg/kg/min and remifentanil 0.1 to 0.15 µg/kg/min, all the patients with an MSR >30% died. At day 2, the MSR in patients with a good outcome was 0%. The cut off values were different as an MSR >30% at day 1 (AUC 0.815) and an MSR >1% at day 2 (AUC 0.891). Of the patients with an MSR ≤30%, HHF was the greatest predictor of a poor outcome (OR 12.858, <i>P</i> = .006). The best predictors of a poor outcome using the spectrogram were suppression ratio (SR) >30% or HHF at day 1 (AUC 0.88) and SR >1% or HHF at day 2 (AUC 0.909). <i>Conclusions.</i> The use of MSR and HHF in frontal spectrograms is convenient and may be successfully employed for early neuroprognostication in moderately sedated cardiac arrest patients. However, spectrograms should be used with electroencephalogram considering the effects of sedatives because of the imperfect detection of electrographic seizures and artifacts.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9267351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Borderline and Depression: A Thin EEG Line.","authors":"Jakša Vukojević,&nbsp;Damir Mulc,&nbsp;Ivana Kinder,&nbsp;Eda Jovičić,&nbsp;Krešimir Friganović,&nbsp;Aleksandar Savić,&nbsp;Mario Cifrek,&nbsp;Domagoj Vidović","doi":"10.1177/15500594211060830","DOIUrl":"https://doi.org/10.1177/15500594211060830","url":null,"abstract":"<p><p>In everyday clinical practice, there is an ongoing debate about the nature of major depressive disorder (MDD) in patients with borderline personality disorder (BPD). The underlying research does not give us a clear distinction between those 2 entities, although depression is among the most frequent comorbid diagnosis in borderline personality patients. The notion that depression can be a distinct disorder but also a symptom in other psychopathologies led our team to try and delineate those 2 entities using 146 EEG recordings and machine learning. The utilized algorithms, developed solely for this purpose, could not differentiate those 2 entities, meaning that patients suffering from MDD did not have significantly different EEG in terms of patients diagnosed with MDD and BPD respecting the given data and methods used. By increasing the data set and the spatiotemporal specificity, one could have a more sensitive diagnostic approach when using EEG recordings. To our knowledge, this is the first study that used EEG recordings and advanced machine learning techniques and further confirmed the close interrelationship between those 2 entities.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9266861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}