EEG Bands Research Articles

Circadian Rhythms in Fractal Features of EEG Signals.

Time-of-day modulations affect both performance on a wide range of cognitive tasks and electrical activity of the brain, as recorded by electroencephalography (EEG). The aim of this work was to identify fluctuations of fractal properties of EEG time series due to circadian rhythms. In twenty-one healthy volunteers (all males, age between 20 and 30 years, chronotype: neutral type) high density EEG recordings at rest in open and closed eyes conditions were acquired in 4 times of the day (8.00 a.m., 11.30 a.m., 2.30 p.m., 7.00 p.m.). A vigilance task (Psychomotor Vigilance Test, PVT) was also performed. Detrended fluctuation Analysis (DFA) of envelope of alpha, beta and theta rhythms was performed, as well as Highuchi fractal dimension (HFD) of the whole band EEG. Our results evidenced circadian fluctuations of fractal features of EEG at rest in both eyes closed and eyes open conditions. Lower values of DFA exponent were found in the time T1 in closed eyes condition, likely effect of the sleep inertia. An alpha DFA exponent reduction was found also in central sensory-motor areas at time T3, the day time in which the sleepiness can be present. In eyes open condition, HFD lowered during the day. In eyes closed condition, an HFD increase was observed in central and frontal regions at time T2, the time in which alertness reaches its maximum and homeostatic sleep pressure is low. Complexity and the persistence of temporal correlations of brain rhythms change during daytime, parallel to changes in alertness and performance.

An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299.

Texas Instruments ADS1299 is an attractive choice for low cost electroencephalography (EEG) devices owing to its low power consumption and low input referred noise. To date, there have been no rigorous evaluations of its performance. In this EEG experimental study we evaluated the performance of the ADS1299 against a high quality laboratory-based system. Two self-paced lower limb motor tasks were performed by 22 healthy participants. Recorded power across delta, theta, alpha, and beta EEG bands, the power ratio across the motor tasks, pre-movement noise, and signal-to-noise ratio were obtained for evaluation. The amplitude and time of the negative peak in the movement-related cortical potentials (MRCPs) extracted from the EEG data were also obtained. Using linear mixed models, no statistically significant differences (p > 0.05) were found in any of these measures across the two systems. These findings were further supported by evaluation of cosine similarity, waveform differences, and topographic maps. There were statistically significant differences in MRCPs across the motor tasks in both systems. We conclude that the performance of the ADS1299 in combination with wet Ag/AgCl electrodes is analogous to that of a laboratory-based system in a low frequency (<40 Hz) EEG recording.

Relevance of electroencephalogram assessment in amyloid and tau pathology in rat

In order to contribute to a better knowledge on the relationship between amyloid and tau pathology, and electroencephalography (EEG) disturbances, the aim of this study was to evaluate the effects of injection of beta amyloid Abeta(1–42) peptide, tau (a recombinant AAV (Adeno-Associated Virus) containing the human transgene tau with the P301 L mutation on rats and the combination of both, on the power of brain’s rhythm (delta, theta, alpha, beta and gamma waves) during the different sleep/wake states of animals by EEG recording. Currently, no preclinical studies explore the effect of the tau pathology on EEG.The experimentations were performed 3 weeks and 3 months post injections. Beta amyloid deposits and hyperphosphorylated Tau are observed by immunohistofluorescence, only in the hippocampus. Furthermore, using a radial arm water maze, the main effect was observed on working memory which was significantly impaired in Abeta-Tau group only 3 months post injections.However, on EEG, as early as the 3rd week, an overall decrease of the EEG bands power was observed in the treated groups, particularly the theta waves during the rapid eye movement (REM) sleep. Beta amyloid was mainly involved in these perturbations. Obviously, EEG seems to be an interesting tool in the early diagnostic of amyloid and tau pathologies, with a good sensitivity and the possibility to perform a follow up during a large period.

Elevated Resting State Gamma Oscillatory Activities in Electroencephalogram of Patients With Post-herpetic Neuralgia.

In acute and ongoing pain, the spontaneous oscillatory activity of electroencephalogram (EEG) has been characterized by suppression of alpha band oscillations and enhancement of gamma band oscillations. In pathological chronic pain which is more severe and common in clinic practice, it is of great interest to investigate the oscillatory activity especially at the broad gamma frequency bands. Our present study explored the resting state oscillatory activities of EEG in patients with post-herpetic neuralgia (PHN) over 3 months which is a typical neuropathic pain model in clinical researches. It was found that the PHN patients showed anxiety and depression revealed by Beck Anxiety Inventory (BAI) and Beck Depression Inventory (BDI) examinations. Power spectrum analysis revealed that the power at gamma frequency band (from 40 to 70 Hz) of EEG was significantly higher in the PHN patients, and positively correlated with pain intensity, anxiety, and depression indexes. Further, increased gamma activity derived from the prefrontal cortex and the cerebellum were revealed by cluster-based sensor level and the beamforming source level analyses. These results suggest the enhanced gamma oscillatory activity in the prefrontal cortex and cerebellum is a characteristic marker in chronic neuropathic pain patients.

Can cECG be an unobtrusive surrogate to determine cognitive state of driver?

Electroencephalography (EEG) is a good measure of cognitive behavior. In this study, we have used a non-contact capacitive coupled ECG (cECG) technology as a surrogate of EEG to estimated driver fatigue. Thirty five male volunteers participated in this study. HRV components such as low frequency (LF), high frequency (HF), and LF/HF ratio were calculated using cECG system. EEG bands (beta, alpha and theta) and ratio index (α+θ)/β were also calculated using EEG system along with HRV components. To find out whether there is any interaction between EEG and cECG, we have estimated coherency, gain and phase shift between high frequency of RR interval and all bands of EEG frequency. Decreasing trends in LF and LF/HF ratio whereas increasing trend in HF with significant difference (p < 0.05) were observed during the course of 120 min of simulated driving. EEG results showed significant (p < 0.05) decrease in beta activity; and an increase in alpha and theta activity while driving. Coherencies between normalized high frequency HFnu and EEG band were high whereas the gain was the maximum for delta band followed by theta band. Phase shift between theta band and (HFnu) was 11.24 ± 13.67°. Cardiac vagal activity shows largely corresponding change in theta band by a phase shift subsequent to a lead of 5.35 ± 4.15 min. These results show that the cognitive fatigue can be estimated by using cECG based on coherence between EEG and cECG.

Neuromodulating attention and mind-wandering processes with multi-session real-time electroencephalogram.

Previous studies showed the efficacy of a single session real-time electroencephalogram (rtEEG) protocols in sensorimotor rhythm (SMR) or Theta up-training. However, the impact of this training on sustained or mind-wandering attention was only modest. This could be explained by the lack of specificity in distinct rtEEG training protocols given their limitation in inhibiting or decreasing the amplitude of down-trained bands. Additionally, multiple sessions of rtEEG in up-training/down-training SMR (sensorimotor rhythm) and Theta along with better ways of tracking sustained and mind-wandering attention protocols may be required to achieve consistent effects. Here we describe the effects of a 10-session trial of up-training/down-training SMR or Theta (SMR⇑Theta⇓; Theta⇑SMR⇓), looking at the effects of 2 rtEEG training protocols in 2 n of 1 subject designs. We also tested to impact of this training in sustained and mind-wandering attention during the course of a Sustained Attention Response Task (SART). The present trial confirmed the potentiality of a multi-session protocol in up-training Theta or SMR and, as consequence, increasing sustained attention (Theta up-training) and mind-wandering attention (SMR up-training). However, the simultaneous increase of the Theta amplitude in the SMR⇑Theta⇓ (and the more modest increase of the SMR amplitude in Theta⇑SMR⇓) reduced the specificity of the rtEEG training. Future studies should build on the potentiality of extended rtEEG protocols on this attention paradigm but increasing the specificity of the trained EEG bands choosing less tedious/more motivating feedback instruments (SMR⇑Theta⇓) and conducting the Theta⇑SMR⇓ training eyes closed.

Changes in Sleep Characteristics and Breathing Parameters During Sleep in Early and Late Pregnancy.

Few studies have objectively evaluated sleep characteristics during pregnancy or investigated the relationship between altered spectral electroencephalogram (EEG) bands and sleep-disordered breathing (SDB). The study aimed to describe changes in sleep as measured by polysomnography (PSG) and spectral EEG bands during pregnancy and to examine the relationship between delta power in non-rapid eye movement (NREM) sleep and SDB. This is a secondary analysis of a prospective study. One hundred twenty-three women underwent full PSG in early pregnancy, and 97 repeated PSG in late pregnancy. Spectral analysis of the EEG in NREM sleep was performed. We used linear and logistic mixed-model regression to analyze the sleep measures and linear regression to explore the association between delta power and apnea-hypopnea index (AHI) changes during pregnancy. In late pregnancy, women had shorter sleep duration, poorer sleep efficiency, more awakenings, more stage N2 sleep, less slow wave sleep, less REM sleep, higher AHI, and higher periodic limb movement index compared to early pregnancy. The percentage of stage N1 sleep, sleep latency, REM sleep latency, and arousal index frequency did not change. Regarding EEG-spectra, delta and theta powers decreased, but beta-2 power increased during pregnancy. In multivariable analyses, greater reduction of delta power was associated with larger increases in AHI (β [95% confidence interval] = -0.038 [-0.073, -0.002], P = .040). Estimates suggest that each one-unit increase in AHI reduces delta power by 4% in late pregnancy. PSG-measured sleep characteristics change during pregnancy. Delta power decreases when the severity of SDB increases during pregnancy. A commentary on this article appears in this issue on page 1095.

Link Prediction Investigation of Dynamic Information Flow in Epilepsy.

As a brain disorder, epilepsy is characterized with abnormal hypersynchronous neural firings. It is known that seizures initiate and propagate in different brain regions. Long-term intracranial multichannel electroencephalography (EEG) reflects broadband ictal activity under seizure occurrence. Network-based techniques are efficient in discovering brain dynamics and offering finger-print features for specific individuals. In this study, we adopt link prediction for proposing a novel workflow aiming to quantify seizure dynamics and uncover pathological mechanisms of epilepsy. A dataset of EEG signals was enrolled that recorded from 8 patients with 3 different types of pharmocoresistant focal epilepsy. Weighted networks are obtained from phase locking value (PLV) in subband EEG oscillations. Common neighbor (CN), resource allocation (RA), Adamic-Adar (AA), and Sorenson algorithms are brought in for link prediction performance comparison. Results demonstrate that RA outperforms its rivals. Similarity, matrix was produced from the RA technique performing on EEG networks later. Nodes are gathered to form sequences by selecting the ones with the highest similarity. It is demonstrated that variations are in accordance with seizure attack in node sequences of gamma band EEG oscillations. What is more, variations in node sequences monitor the total seizure journey including its initiation and termination.

GLOBAL SYNCHRONIZATION INDEX AS A NEW INDICATOR FOR TRACKING COGNITIVE FUNCTION CHANGES BY TRANSCRANIAL DIRECT CURRENT STIMULATION IN A TRAUMATIC BRAIN INJURED PATIENT: A CASE REPORT

Traumatic brain injury (TBI) is a leading cause of long-term neurological disability such as cognitive impairments and executive dysfunction. Noninvasive transcranial direct current stimulation (tDCS), which can change brain excitability, was introduced and has been applied for therapeutic purposes regarding cognitive dysfunction after TBI. We investigated the effect of tDCS on the cognitive function and cortical power distribution by dual analysis of resting state Electroencephalography (EEG) with Global Synchronization Index (GSI) and Low-Resolution Brain Electromagnetic Tomography Analysis (LORETA) in a TBI patient. A 77-year-old male, who went through a traumatic brain injury showed severe impairment of cognitive function. We performed 20 sessions of 2mA tDCS over the left dorsolateral prefrontal cortex (DLPFC) and measured Korean version of the Mini-Mental Status Examination (K-MMSE) and Rancho Los Amigos (RLA) level at baseline, 10 days and 20 days each after tDCS. EEG was also performed on same days with K-MMSE and RLA tests. After tDCS, the cognitive function measured by K-MMSE and RLA level were improved. GSI showed decrease in the low frequency band (theta wave) and increase in high frequency bands (alpha and beta wave) (Table 1). LORETA analysis showed incresed cortical activation components in high frequency bands at 20 days after tDCS (Figure 1). Graphical representation of the LORETA results comparing the EEG between baseline and tDCS 10 (a) and between baseline and tDCS 20 (b). Blue-light blue colors indicate increased component and yellow-red colors indicate decreased component of each EEG bands. We suggest that tDCS might be effective in TBI patients, and the alterations of cognitive networks could be effectively studied using GSI analysis together with LORETA.

Eliminating Individual Bias to Improve Stress Detection from Multimodal Physiological Data.

Stress monitoring is important for mental wellbeing and early detection of related disorders. The current work is focused on stress detection from multiple non-invasive physiological signals like Electroencephalogram (EEG), Photoplethysmogram (PPG) and Galvanic Skin Response (GSR). We show that, compared to using only the well known EEG band powers in different frequencies for stress detection, an early fusion with GSR and PPG features shows a significant improvement. Maximum Relevance Minimum Redundancy (mRMR) based feature selection is used to identify the most suitable physiological features correlating with stress. A major contribution of this work lies in eliminating subject-specific bias to improve the classification accuracy. We use self-reported values of Valence, Arousal and Dominance to cluster subjects and build separate classification models specific to clusters. The proposed approach is validated on a publicly available dataset comprising 146 data instances from 10 subjects. The performances of Leave-One- Subject-Out cross validation (LOSOCV) in terms of mean Fscores are 0.61 using EEG features only, 0.64 using early fusion of EEG, GSR and PPG features and 0.69 by applying our clustering technique before fusion and classification.

Quantitative Analysis of a Marked Point Process based Sleep Spindle Detector (MPP-SSD).

Sleep spindles result from interactions between the thalamic and cortical neurons during the NREM2 stage. Studies show that these waxing and waning episodes of field potentials may have an implied role in memory consolidation, cellular plasticity and neuronal development besides serving as important markers in several neuronal pathologies. For these reasons, accurate spindle scoring of polysomnographic signals is important and has garnered interest in automating the tedious process of scoring via visual inspection. In this paper, we employ a transient model for automatic sleep spindle detection designed as a Marked Point Process (MPP). Further, in order to simplify the model development, the determination of the atoms was done independently for each of the EEG bands. However, this brings the problem of quantifying the effect of the required bandpass filtering, which was not done in previous work. Here we change the Q- factor of the filters and evaluate the effect on the detections provided by the model, when compared with two sleep experts. Several statistics are utilized, and we conclude that the design of the bandpass filters affects the performance. Low Q filters were thought necessary, but the results show that the optimal Q - factor is around 2.

The local earth magnetic field changes impact on weekly hospitalization due to unstable angina pectoris

Objectives: Acute coronary syndrome as an acute oxygenated blood deprivation to the heart muscle due to atherosclerotic plaque rupture in the coronary artery followed by thrombosis is possibly associated with changes in the Earth’s local time varying magnetic field as they strongly influence hormonal and other regulatory systems’ activity. This study analyses the correlation between prevalence of the acute coronary syndrome and the changes in the local time varying aspects of the magnetic field. Methods: Seven-hundred patients admitted to Cardiology Department of Hospital of Lithuanian University of Health Sciences Kaunas Clinics within 2016 due to acute coronary syndrome were retrospectively included into the study. The number of cases per week was compared with the weekly changes of the local Earth magnetic field. The one-year period was divided into two periods: the first-half of the year (weeks 1 to 26) and the second-half of the year (weeks 27 to 52) and more detailed analyses were performed accordingly to the significance of the left main artery lesion. Mean power of local magnetic field fluctuations in Lithuania, measured in pT2 in five different frequency ranges where overlaps between the Schumann resonance and EEG frequency ranges (named as SDelta (0-3.5 Hz), STheta (3.5-7 Hz), SAlpha (7-15 Hz), SBeta (15-32 Hz) and SGamma (32-66 Hz) to distinguish them from the EEG bands). Results: Statistically significant weak and moderate correlations between weekly prevalence of acute coronary syndrome admissions and the magnetic field intensities changes were found. Higher intensities in the SBeta and SGamma ranges were associated with a higher number of admissions throughout the year in females and the SGamma range was associated with higher number of admission only during the second-half of the year in males. A higher intensity magnetic field in SDelta, STheta, SAlpha and SBeta ranges was associated with a higher admissions number due to left main artery lesions in males, while a higher intensity in the SGamma range was associated with higher number of admissions due to left main artery lesions in females through the year. Conclusion: Significant correlation between acute coronary syndrome and changes in the local Earth time varying magnetic field intensities was found. Some frequency ranges are associated with an episode of an acute coronary syndrome. Left main artery lesions significantly correlated with magnetic field changes in most of the frequency ranges in males while only one of the frequency ranges in females.

Neurological and Psychological Changes in Hemodialysis Patients Before and After the Treatment.

Neurological, psychological, and cognitive disorders in chronic kidney disease may contribute to poor quality of life in these patients. The aim of this study was to assess the electroencephalographic, psychological, and cognitive changes before and after hemodialysis (HD) compared with healthy controls (HC). Sixteen HD patients and 15 HC were enrolled. Electroencephalogram (EEG), Minnesota multiphasic personality inventory (MMPI-2) Satisfaction profile (SAT-P), and Neuropsychological test Global z-scores (NPZ5) were performed before (T0) and after (T1) HD treatment and in HC. Renal function, inflammatory markers and mineral metabolism indexes were also evaluated. Patients did not show significant differences before and after HD in the absolute and relative power of band of EEG, except in Theta/Alpha index (P < 0.001). At T1, HD patients showed significant differences in Beta, Delta and Theta band, in addition to Theta/alpha index, with respect to HC. Moreover, HD patients showed significant differences in specific MMPI-2 clinical and content scales, SAT-P domains and NPZ5 tests of memory and concentration with respect to HC. We also observed significant correlations between renal function, mineral metabolism, inflammatory markers and psychocognitive alterations. In our sample EEG abnormalities tend to reduce, but not significantly, after HD treatment and differences remain present with respect to HC. In HD patients cognitive and psychological alterations were associated with reduced quality of life and correlated with mineral metabolism and inflammation. Modification in EEG and in psychological and cognitive parameters should be assessed in a larger HD population to confirm our observation.

Electroencephalogram alpha asymmetry in patients with depressive disorders: current perspectives.

PurposeElectroencephalogram (EEG) alpha asymmetry (AA) in depressive disorders has been of interest over the last few decades, but it continues to remain unclear whether EEG AA can discriminate between healthy and depressive individuals.Materials and methodsA systematic literature search for papers addressing EEG AA using the keywords alpha asymmetry, depression, and EEG was performed in PubMed. All studies were checked for sample size, gender, handedness, reference, recording protocol, EEG band range, impedance, type of analysis, drugs, and comorbidity.ResultsA total of 61 articles were found, of which 44 met our inclusion criteria. They have been consecutively analyzed in respect of methodology and results. Approximately 25% (11/44) of the studies did not mention or ignored handedness, 41% (18/44) of the studies used data with only self-reported handedness, and only 34.1% (15/44) of all studies tested handedness. Only 35% (15/44) of the studies reported pharmacological treatment, and only 35% (15/44) of the studies controlled for medication. A total of 52% (23/44) of the studies reported comorbidity, and only 30% (13/44) of the studies controlled for comorbidity. Only 29.6% (13/44) of the studies reported education. In all, 30.5% (13/44) of the studies analyzed group differences and correlations, while 15.9 (7/44) of the studies used only correlational analyses. A total of 52.3% (23/44) of the studies analyzed only group differences. Alpha range was fixed (8–13 Hz) in 59.1% (26/44) of all studies. Reference to common average was used in seven of 44 studies (15.9%). In all, nine of 44 (20.5%) studies used the midline central position as reference, 22 of 44 (50%) studies used the ear or the mastoid as reference, and four of 44 (9.1%) studies used the nose as reference.ConclusionDiscriminative power of EEG AA for depressed and healthy controls remains unclear. A systematic analysis of 44 studies revealed that differences in methodology and disregarding proper sampling are problematic. Ignoring handedness, gender, age, medication, and comorbidity could explain inconsistent findings. Hence, we formulated a guideline for requirements for future studies on EEG AA in order to allow for better comparisons.

Neuroimaging Study of Alpha and Beta EEG Biofeedback Effects on Neural Networks.

Neural networks interaction was studied in healthy men (20-35years old) who underwent 20 sessions of EEG biofeedback training outside the MRI scanner, with concurrent fMRI-EEG scans at the beginning, middle, and end of the course. The study recruited 35 subjects for EEG biofeedback, but only 18 of them were considered as "successful" in self-regulation of target EEG bands during the whole course of training. Results of fMRI analysis during EEG biofeedback are reported only for these "successful" trainees. The experimental group (N = 23 total, N = 13 "successful") upregulated the power of alpha rhythm, while the control group (N = 12 total, N = 5 "successful") beta rhythm, with the protocol instructions being as for alpha training in both. The acquisition of the stable skills of alpha self-regulation was followed by the weakening of the irrelevant links between the cerebellum and visuospatial network (VSN), as well as between the VSN, the right executive control network (RECN), and the cuneus. It was also found formation of a stable complex based on the interaction of the precuneus, the cuneus, the VSN, and the high level visuospatial network (HVN), along with the strengthening of the interaction of the anterior salience network (ASN) with the precuneus. In the control group, beta enhancement training was accompanied by weakening of interaction between the precuneus and the default mode network, and a decrease in connectivity between the cuneus and the primary visual network (PVN). The differences between the alpha training group and the control group increased successively during training. Alpha training was characterized by a less pronounced interaction of the network formed by the PVN and the HVN, as well as by an increased interaction of the cerebellum with the precuneus and the RECN. The study demonstrated the differences in the structure and interaction of neural networks involved into alpha and beta generating systems forming and functioning, which should be taken into account during planning neurofeedback interventions. Possibility of using fMRI-guided biofeedback organized according to the described neural networks interaction may advance more accurate targeting specific symptoms during neurotherapy.

Neuromodulating Attention and Mind-Wandering Processes with a Single Session Real Time EEG.

Our minds are continuously alternating between external attention (EA) and mind wandering (MW). An appropriate balance between EA and MW is important for promoting efficient perceptual processing, executive functioning, decision-making, auto-biographical memory, and creativity. There is evidence that EA processes are associated with increased activity in high-frequency EEG bands (e.g., SMR), contrasting with the dominance of low-frequency bands during MW (e.g., Theta). The aim of the present study was to test the effects of two distinct single session real-time EEG (rtEEG) protocols (SMR up-training/Theta down-training-SMR⇑Theta⇓; Theta up-training/SMR down-training-Theta⇑SMR⇓) on EA and MW processes. Thirty healthy volunteers were randomly assigned to one of two rtEEG training protocols (SMR⇑Theta⇓; Theta⇑SMR⇓). Before and after the rtEEG training, participants completed the attention network task (ANT) along with several MW measures. Both training protocols were effective in increasing SMR (SMR⇑Theta⇓) and theta (Theta⇑SMR⇓) amplitudes but not in decreasing the amplitude of down-trained bands. There were no significant effects of the rtEEG training in either EA or MW measures. However, there was a significant positive correlation between post-training SMR increases and the use of deliberate MW (rather than spontaneous) strategies. Additionally, for the Theta⇑SMR⇓ protocol, increase in post-training Theta amplitude was significantly associated with a decreased efficiency in the orientation network.

Prediction of central neuropathic pain in spinal cord injury based on EEG classifier

ObjectivesTo create a classifier based on electroencephalography (EEG) to identify spinal cord injured (SCI) participants at risk of developing central neuropathic pain (CNP) by comparing them with patients who had already developed pain and with able bodied controls. MethodsMultichannel EEG was recorded in the relaxed eyes opened and eyes closed states in 10 able bodied participants and 31 subacute SCI participants (11 with CNP, 10 without NP and 10 who later developed pain within 6 months of the EEG recording). Up to nine EEG band power features were classified using linear and non-linear classifiers. ResultsThree classifiers (artificial neural networks ANN, support vector machine SVM and linear discriminant analysis LDA) achieved similar average performances, higher than 85% on a full set of features identifying patients at risk of developing pain and achieved comparably high performance classifying between other groups. With only 10 channels, LDA and ANN achieved 86% and 83% accuracy respectively, identifying patients at risk of developing CNP. ConclusionTransferable learning classifier can detect patients at risk of developing CNP. EEG markers of pain appear before its physical symptoms. Simple and complex classifiers have comparable performance. SignificanceIdentify patients to receive prophylaxic treatment of CNP.

F125. Brain connectivity related to sleep-wake state: An intracranial EEG study

Introduction Understanding brain connectivity in health and disease is a major challenge for basic and translational neuroscience. Direct EEG recordings from the brain surface (electrocorticography; ECoG) in epilepsy provide unique opportunity for studying human neuro-electric connectivity with reference to the wake, sleep and epileptic states. A major conundrum is reconciling the views of sleep being a disconnected state (Massimini et al., 2005) with the hypersynchronicity of sleep that favors seizure occurrence in the partial epilepsies, implying a heightened connectivity. Using spectral analysis and graph-theoretic measures (Bullmore and Sporns, 2009) applied to ECoG recordings in 6 patients undergoing continuous monitoring, we demonstrate how a reconciliation between these two scenarios is possible. Methods ECoG data in average reference format from six patients with refractory focal epilepsy undergoing prolonged pre-surgical video-EEG telemetry with subdural grid electrodes was analyzed. Segments of wakefulness and sleep (25–30 s long) were concatenated into contiguous epochs and normalized to zero mean and unit variance. Power spectra were computed by standard methods and the amplitude spectra curve-fitted empirically with a three-parameter function. The analysis was repeated over sliding windows of 30 s duration across the whole epoch. A fuzzy C-means method was used to cluster each parameter triplet into an activity score between [0–1], with 0 representing deep sleep, and 1 being alert wakefulness. The epoch time series were then filtered into the canonical Berger δ, θ, α, β and γ EEG bands. For each band, standard graph-theoretic measures were computed over sliding window segments across whole epochs to correspond with the activity score computations. Pearson correlations between the activity score and concurrently computed graph-theoretic connectivity metrics were calculated, and statistically significant correlations following Bonferroni correction (by a factor of 30) retained. Results We found that the coherence network modularity in the beta bands – relevant to high-frequency seizure-onset rhythms – correlated positively with wakefulness, while delta, theta and alpha modularity correlated negatively. An approximately reverse relationship was observed with respect to clustering coefficient. Conclusion Our results complement those obtained by resting state fMRI (Cox et al., 2014) and cortico-cortical evoked potentials (Usami et al., 2015) of sleep. We suggest that ECoG-based brain connectivity metrics are both state (sleep-wake) dependent and time-scale (waveform frequency) dependent. It is possible for the cortex to be ‘disconnected’ with respect to frequencies ostensibly underlying conscious wakefulness in the sleep state, yet ‘hyperconnected’ on time scales relevant to the transmission of epileptic seizures.

F160. Investigation of the effects of transcranial direct current stimulation and neurofeedback by continuous performance test and beck inventories

Transcranial direct current stimulation (tDCS) method is a noninvasive neuromodulation technique based on weak direct current stimulation through the scalp. Neurofeedback (NFB) is a learning strategy which may help to alter brain wave parameters of a person by giving electroencephalography (EEG) feedback via special developed computer programs. Based on the aforementioned evidence, the study was designed to examine primarily the possible fascinating and supportive effects of tDCS application in addition to NFB training on healthy volunteers. Secondly, we aimed to examine brain wave parameters of by giving EEG feedback alteration by Continuous Performance Test (CPT), Beck Inventories (BI) used in order to measure; the selective attention, response time and suppression. Sixteen healthy volunteers were divided into two groups consisting of eight subjects. One of the groups were trained by sensorimotor rhythm (SMR) NFB training; 2 days per week, 10 sessions of 30 min, other group received 10 min tDCS before each NFB training session. CPT, BI were used in order to measure the selective attention, response time and suppression. Obtained EEG band values and rates were evaluated offline. According to statistical analyses, average of depression scores of NFB + tDCS group (7.000 ± 4.07) was decreased compared to before (9,625 ± 5.60) NFB + tDCS practice, it was significantly decreased when compared with statistical analyze of t-test (t (7) = 2628, p < .05). After trainings, the results of CPT were compared with repeated measure ANOVA.Sessions were analyzed by comparing 1st, 2nd, 5th and 10th practice results. Although there was no significant difference of right answers of the group of NFB (F (4,28) = 2215, p = .093), NFB + tDCS were significantly increased (F (4,28) = 6557, p = .001). Each groups CPT score were compared by repeated measures of ANOVA, statistically significance was found between the correct response latencies of the NFB group (F (4,28) = 3282, p = .025) and NFB + tDCS group (F(4,28) = 3143, p = .030). When the frequency bands were compared between the NFB training group with post hoc Bonferroni pairwise comparisons showed that difference was significant between 1st practice (1.515 ± .39) and at the 10th application (1.996 ± .42 (p = .002). When the inter-application of frequency bands at the group of NFB + tDCS statistically significant between the first 1th stimulation (10.165 ± 1.34) and 10th stimulation (14.128 ± 1.72)(p = .012), The difference on theta/beta band is significant at 2th practice (2.074 ± .098) to 10th practice (1.824 ± .084) (.013). While NFB + tDCS had significance at theta/beta ratios and alpha band amplitudes, NFB group had significance at theta/SMR ratios. Results suggest that NFB training shows its effects at the end of 10 sessions, and additional use of tDCS did not accelerate improvement as expected. However, its therapeutic effect on anxiety and depression scores and inhibition deficits of subjects were clear.

Does Resting-state EEG Band Power Reflect Fluid Intelligence?

Fluid intelligence refers to the ability to engage in abstract reasoning, which is independent of life experiences and knowledge. Brain studies have associated fluid intelligence with resting-state electroencephalograms (EEGs). This study investigated the relationship between resting-state EEG band power and fluid intelligence. The resting-state EEGs of 39 healthy volunteers were recorded from 32 scalp locations with a linked ears reference while the subjects were positioned in a Faraday cage for 15 min (10 min, eyes open; 5 min, eyes closed). The same participants completed the Raven’s Standard Progressive Matrices (RSPM) test. Brain electrical activity was divided into delta, theta, alpha, and beta bands, and correlation coefficients were calculated to identify associations between the resting-state EEG powers and RSPM scores. The mean RSPM score of the participants was 49.31 (females=50.43, males=48). No correlation between the resting-state EEG power values (delta, theta, alpha, and beta bands) and fluid intelligence was found. The results of correlation analyses showed no relationship between EEG data and intelligence scores. These results indicate that data on resting-state EEG power values do not contribute to an understanding of fluid intelligence. However, brain network studies may deepen our understanding of the neural mechanisms underpinning intelligence.