O2 Channels Research Articles

Effect of electroacupuncture on the characteristic signal of electroencephalogram in the patients with chronic insomnia： a randomized controlled trial

To observe the effect of electroacupuncture (EA) on sleep quality and electroencephalogram (EEG) signal in the patients with chronic insomnia and to explore its mechanism. Sixty patients with chronic insomnia were randomized into an EA group (30 cases, 3 cases dropped out) and a health learning group (30 cases, 3 cases dropped out). In the health learning group, the sleep health education was provided. In the EA group, besides the health education as the health learning group, EA was delivered at Baihui (GV20), Yintang (GV24+), bilateral Shenmen (HT7), bilateral Sanyinjiao (SP6), with continuous wave, 2 Hz in frequency, once every other day, and 3 interventions a week. EA was given for 4 weeks successi-vely, composed of 12 treatments. The score of Pittsburgh sleep quality index (PSQI), Hamilton anxiety scale (HAMA), and patient health questionnaire-9 (PHQ-9) before and after treatment were compared between the two groups. The EEG signals were collected and the relative power and entropy of EEG before and after treatment were compared between the two groups. After treatment, the scores of PSQI, HAMA and PHQ-9, the relevant power of β wave in F8 and T3 channels, and that of α wave in T4 channel, and the entropy in T3 channel under the eyes-open (EO) resting state, as well as the relevant power and the entropy of γ wave in F8 channel under the eyes-closed (EC) resting state in the EA group were lower (P<0.05) than those before the treatment. All of the above results were lower than those of the health learning group at the same time points (P<0.05). Under the EO resting state, the relevant power of the θ and δ waves of T3 channel and θ wave of T4, O2 and Pz channels of patients in the EA group after the treatment were increased (P<0.05) than those before the treatment, and higher (P<0.05) than those of the health learning group at the same time points. Acupuncture is effective on chronic insomnia, which may be related to the regulation EEG signals.

The influence of mental calculations on brain regions and heart rates

Performing mathematical calculations is a cognitive activity that can affect biological signals. This study aims to examine the changes in electroencephalogram (EEG) and electrocardiogram (ECG) signals of 36 healthy subjects during the performance of arithmetic tasks. To process EEG signals in different frequency bands, the energy and entropy of entropy (EoE) were extracted from the power spectrum and phase spectrum, respectively. Statistical analysis was conducted to determine meaningful features. These features were sent into support vector machine (SVM) and multi-layer perception (MLP) classifiers to assess their capability in separating math and rest classes. Results indicated the highest classification accuracy of 98.4% for classifying good counters in math and rest state using the MLP method. Based on the majority of features selected for each EEG channel, discriminative brain areas were identified. Analyzing EEG signals proved that math calculation may have multiple influences on various parts of the brain. By comparing good counters’ brain activities to those in a resting state, prominent changes were observed in the F4, C4, T4, T5, P3, and O2 areas. However, O1 and O2 channels showed significant changes in the brain of bad counters compared to the resting state. Considering ECG signals also demonstrated that during math calculation the number of heart rates per minute surpasses the rest state. These alterations can occur due to cognitive abilities or emotional processes which were observed to be prominent in subjects who performed more accurate calculations.

Polysomnographic features prior to dream enactment behaviors in isolated rapid eye movement sleep behavior disorder

ObjectiveThis study aimed to identify electroencephalogram correlates of dream enactment behaviors (DEBs) and elucidate their cortical dynamics in patients with isolated/idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD). MethodsThis cross-sectional study included 15 patients with iRBD. Two REM sleep periods in routine polysomnography were compared: the 60 s preceding the DEBs (“pre-representative behavior” [preR]), and the 60 s with the least submental electromyogram activity (“background” [BG]). Six EEG frequency bands and electrooculogram were analyzed; power spectra, coherence and phase-locking values in four 15-s periods were examined to assess trends. These indices were also compared between preR and BG. ResultsCompared with BG, significantly higher delta power in the F3 channel and gamma power in the F4 and O2 channels were observed during preR. For functional connectivity, the widespread beta-band connectivity was significantly increased during preR than BG. ConclusionBefore notable REM sleep behaviors, uneven distributed higher EEG spectral power in both very low and high frequencies, and increased wide-range beta band functional connectivity, were observed over 60 s, suggesting cortical correlates to subsequent DEBs. SignificanceThis study may shed light on the pathological mechanisms underlies RBD through the routine vPSG analysis, leading to detection of DEBs.

An Event Detection Mechanism with Deep Feature Extraction and Optimal Loss Function Based XGBoost Classifier

Purpose: Interoceptions are a combination of sensation, integration, and interpretation of internal bodily signals. Interoceptions are bidirectionally related to the human being mental and physiological health, and well-being. Sleep and different interoceptive modalities are proven to share common relations. Heartbeat Evoked Potential (HEP) is known as a robust readout to interoceptive processes. In this study, we focused on the relation between HEP modulations and sleep-related disorders. Materials and Methods: We investigated four different sleep-related disorders, including insomnia, rapid eye movement behavior disorder, periodic limb movements and nocturnal frontal lobe epilepsy, and provided HEP signals of multiple Electroencephalogram (EEG) channels over the right hemisphere to compare these disorders with the control group. Here, we investigated and compared the results of 35 subjects, including seven subjects for the control group and seven subjects for each of above-mentioned sleep disorders. Results: By comparing HEP responses of the control group with sleep-related patients’ groups, statistically significant HEP differences were detected over right hemisphere EEG channels, including FP2, F4, C4, P4, and O2 channels. These significant differences were also observed over the grand average HEP amplitude activity of channels over the right hemisphere in the sleep-related disorders as well. Conclusion: Our results between the control group and groups of patients suffering from sleep-related disorders demonstrated that during different stages of sleep, HEPs show significant differences over multiple right hemisphere EEG channels. Interestingly, by comparing different sleep disorders with each other, we observed that each of these HEP differences’ patterns over specific channels and during certain sleep stages bear considerable resemblances to each other.

Investigation of Heartbeat Evoked Potential (HEP) Response During Different Stages of Sleep in Sleep Disorders

Purpose: Interoceptions are a combination of sensation, integration, and interpretation of internal bodily signals. Interoceptions are bidirectionally related to the human being mental and physiological health, and well-being. Sleep and different interoceptive modalities are proven to share common relations. Heartbeat Evoked Potential (HEP) is known as a robust readout to interoceptive processes. In this study, we focused on the relation between HEP modulations and sleep-related disorders. Materials and Methods: We investigated four different sleep-related disorders, including insomnia, rapid eye movement behavior disorder, periodic limb movements and nocturnal frontal lobe epilepsy, and provided HEP signals of multiple Electroencephalogram (EEG) channels over the right hemisphere to compare these disorders with the control group. Here, we investigated and compared the results of 35 subjects, including seven subjects for the control group and seven subjects for each of above-mentioned sleep disorders. Results: By comparing HEP responses of the control group with sleep-related patients’ groups, statistically significant HEP differences were detected over right hemisphere EEG channels, including FP2, F4, C4, P4, and O2 channels. These significant differences were also observed over the grand average HEP amplitude activity of channels over the right hemisphere in the sleep-related disorders as well. Conclusion: Our results between the control group and groups of patients suffering from sleep-related disorders demonstrated that during different stages of sleep, HEPs show significant differences over multiple right hemisphere EEG channels. Interestingly, by comparing different sleep disorders with each other, we observed that each of these HEP differences’ patterns over specific channels and during certain sleep stages bear considerable resemblances to each other.

Assessment of visual fatigue in SSVEP-based brain-computer interface: a comprehensive study.

Fatigue deteriorates the performance of a brain-computer interface (BCI) system; thus, reliable detection of fatigue is the first step to counter this problem. The fatigue evaluated by means of electroencephalographic (EEG) signals has been studied in many research projects, but widely different results have been reported. Moreover, there is scant research when considering the fatigue on steady-state visually evoked potential (SSVEP)-based BCI. Therefore, nowadays, fatigue detection is not a completely solved topic. In the current work, the issues found in the literature that led to the differences in the results are identified and saved by performing a new experiment on an SSVEP-based BCI system. The experiment was long enough to produce fatigue in the users, and different SSVEP stimulation ranges were used. Additionally, the EEG features commonly reported in the literature (EEG rhythms powers, SNR, etc.) were calculated as well as newly proposed features (spectral features and Lempel-Ziv complexity). The analysis was carried out on O1, Oz and O2 channels. This work found a tendency of displacement from high-frequency rhythms to low-frequency ones, and thus, better EEG features should present a similar behaviour. Then, the 'relative power' of EEG rhythms, the rates (θ + α)/β, α/β and θ/β, some spectral features (central and mean frequencies, asymmetry and kurtosis coefficients, etc.) and Lempel-Ziv complexity are proposed as reliable EEG features for fatigue detection. Hence, this set of features may be used to construct a more trustworthy fatigue index.

Analyzing neural activity under prolonged mask usage through EEG

In recent COVID times, mask has been a compulsion at workplaces and institutes as a preventive measure against multiple viral diseases including coronavirus (COVID-19) disease. However, the effects of prolonged mask-wearing on humans’ neural activity are not well known. This paper is to investigate the effect of prolonged mask usage on the human brain through electroencephalogram (EEG), which acquires neural activity and translates it into comprehensible electrical signals. The performances of 10 human subjects with and without mask were assessed on a random patterned alphabet game. Besides EEG, physiological parameters of oxygen saturation, heart rate, blood pressure, and body temperature were recorded. Spectral and statistical analysis were performed on the recorded entities along with linear discriminant analysis (LDA) on extracted spectral features. The mean EEG spectral power in alpha, beta, and gamma sub-bands of the subjects with mask was smaller than the subjects without mask. The performances on the task and the oxygen saturation level between the two groups differed significantly (p < 0.05). Whereas, the blood pressure, body temperature, and heart rate of both groups were similar. Based on the LDA analysis, the occipital and frontal lobes exhibited the greatest variability in channel measurements, with O1 and O2 channels in the occipital lobe demonstrating significant variations within the alpha band due to visual focus, while the F3, AF3, and F7 channels were found to be differentiating within the beta and gamma frequency bands due to the cognitive stimulating tasks. All other channels were observed to be non-discriminatory.

Epileptic seizure detection combining power spectral density and high-frequency oscillations

Detection of pre-seizure signs in epileptic signals may help patients to survive the seizure with minimal damage. This study aims to detect epileptic seizure patterns using EEG datasets of five patients. The signals' maximum power spectral density (PSD) and high-frequency oscillations (HFOs) signals are investigated. The PSDs of all patients' signals are calculated, and the subbands of the maximum PSD are examined. It is observed that 95%, 85%, 85%, 75%, and 80% of the channels of the five patients are in the sum of delta and theta subbands of the maximum PSD, respectively. All patients' maximum power frequency subbands of F4 and T3 channels included only delta and theta subbands. Furthermore, frequency increase rates of pre-ictal and ictal signals are investigated, and increasing PSDs of ripples and fast ripples are then calculated. A much higher-frequency ripple follows the low-frequency ripple in 75%, 75%, 65%, 85%, and 75% of the channels of the first, second, third, fourth, and fifth patients, respectively. For the pre-ictal data, a much higher frequency ripple is not seen, followed by a low-frequency ripple in 90%, 85%, 75%, 90%, and 90% of all channels of five patients, respectively. In addition, in this study, the frequency of signals is observed to be 80 Hz and above in the Fp2, C4, P4, O2, and Pz channels, which are common to all patients. Consequently, examining PSD and HFO signals ensures the detection of the differences between the data sets and detects the epileptic seizure patterns in all five patients.

An explicit formulation for the retrieval of the overlap function in an elastic and Raman aerosol lidar

Abstract. We derive an explicit (i.e., non-iterative) formula for the retrieval of the overlap function in an aerosol lidar with both elastic and Raman N2 and/or O2 channels used for independent measurements of aerosol backscatter and extinction coefficients. The formula requires only the measured, range-corrected elastic and the corresponding Raman signals, plus an assumed lidar ratio. We assess the influence of the lidar ratio error in the overlap function retrieval and present retrieval examples.

Effect of bridging hydroxyl on the interfacial charge transfer for photocatalytic degradation of toluene

The mechanism of charge transfer to reactants during photocatalytic degradation of volatile organic compounds remains unclear. Herein, TiO2 was heated in a hydrogen atmosphere to increase the content of bridging hydroxyl (OHB), and atmosphere surface photovoltage spectroscopy (ASPV) was used to study the effect of changed surface sites on the transfer of photogenerated charge carriers to reactants (O2, H2O, and toluene). Results showed that the removal rate and mineralization efficiency of toluene were improved after hydrogen treatment. The ASPV tests found that hydrogen treatment created electron transfer channels to O2 and hole transfer channels to toluene/water, and reduced H2O’s interference with electron utilization by O2. Hydrogen in the newly formed OHB changed the electron distribution on the TiO2 surface by injecting electrons. The OHB and surrounding Ti4−x ions altered the adsorption form of H2O, thereby facilitating the transfer of electrons to O2 and holes to toluene.

The new wireless EEG device Mentalab Explore is a valid and reliable system for the measurement of resting state EEG spectral features

Technological advancements in neuroscience have provided many conveniences to scientists, researchers, and consumers. The emerging electroencephalography (EEG) devices are good examples for it. The objective of this study was to investigate the validity and reliability of a new wireless EEG device Mentalab Explore for resting-state EEG (rsEEG) recordings in eyes open and eyes closed conditions. Twenty-three healthy subjects were recruited for the study. The subjects visited the laboratory on two occasions. On the first day, both devices were used to record rsEEG data, and after 24 h, only the Mentalab Explore was used to record rsEEG for test–retest reliability analysis. We compared the alpha peak frequency, suppression, and mean power between the two devices. Intraclass correlation coefficient was calculated for test–retest reliability analysis. Power spectral density (PSD) was calculated using Welch method. The PSD (eyes closed p < 0.0001; eyes open p = 0.01–0.0001), alpha peak frequency (p < 0.0001), and alpha suppression (p = 0.002–0.0001) from the two EEG devices as well as the test–retest results from the Mentalab device were significantly correlated. There were no significant differences in alpha peak frequency or suppression between the clinical-grade and the new devices for O1 and O2 channels in eyes-closed condition. The two measurements completed ∼24 h apart using the Mentalab system were similar for all the variables. We showed that the consumer-grade Mentalab Explore with gel electrodes is a reliable and valid EEG recording device for rsEEG spectral features.

Time-frequency Analysis of Electroencephalogram Signals in a Perceptual Decision-Making Task of Random Dot Kinematograms

Purpose: Perceptual decision-making is the act of choosing one option from a set of alternatives based on available sensory information. Regarding the serious role of this act in human personal and social lives, the neurophysiological analysis of the brain during this type of decisions is of great interest. In this research, the underlying neural mechanism of these decisions is investigated using a perceptual decision-making Electroencephalogram (EEG) dataset with a perceptual discrimination task.&#x0D; Materials and Methods: An online available dataset containing the pre-processed EEG signals of 24 healthy participants during the perceptual decision-making task of Random Dot Kinematograms was used. After a secondary pre-processing stage, clean EEG signal was divided into 1.3-second segments and averaged for Event-Related Potential (ERP) and Event-Related Spectral Perturbation (ERSP) calculations. The task engagement index was also calculated and averaged among all participants.&#x0D; Results: According to the results, the amplitude of the N200 component in O1 and O2 channels was larger for correct choices than incorrect ones. Furthermore, in the O2 channel, it was observed that the average alpha power near 200 milli-seconds after stimulus onset was slightly higher in high and low confidence choices than medium confidence choices. The beta band power in the PO2 channel was also higher for correct choices rather than incorrect ones in this interval. Moreover, the results represented that the task engagement index was higher in medium confidence choices, especially in occipital and parieto-occipital channels.&#x0D; Conclusion: The larger N200 amplitude and the higher beta power for correct choices, and the lower alpha power for medium confidence choices may be due to more attention of the individuals to the stimuli. This phenomenon can be observed in the task engagement indices as well. This could be because the user expended more effort in medium confidence to bring one of the choices to the decision threshold.

Emotion Self-Regulation in Neurotic Students: A Pilot Mindfulness-Based Intervention to Assess Its Effectiveness through Brain Signals and Behavioral Data.

Neuroticism has recently received increased attention in the psychology field due to the finding of high implications of neuroticism on an individual’s life and broader public health. This study aims to investigate the effect of a brief 6-week breathing-based mindfulness intervention (BMI) on undergraduate neurotic students’ emotion regulation. We acquired data of their psychological states, physiological changes, and electroencephalogram (EEG), before and after BMI, in resting states and tasks. Through behavioral analysis, we found the students’ anxiety and stress levels significantly reduced after BMI, with p-values of 0.013 and 0.027, respectively. Furthermore, a significant difference between students in emotion regulation strategy, that is, suppression, was also shown. The EEG analysis demonstrated significant differences between students before and after MI in resting states and tasks. Fp1 and O2 channels were identified as the most significant channels in evaluating the effect of BMI. The potential of these channels for classifying (single-channel-based) before and after BMI conditions during eyes-opened and eyes-closed baseline trials were displayed by a good performance in terms of accuracy (~77%), sensitivity (76–80%), specificity (73–77%), and area-under-the-curve (AUC) (0.66–0.8) obtained by k-nearest neighbor (KNN) and support vector machine (SVM) algorithms. Mindfulness can thus improve the self-regulation of the emotional state of neurotic students based on the psychometric and electrophysiological analyses conducted in this study.

Impairment of EEG functional connectivity in dementia during recognition of emotional facial expression

AbstractBackgroundThe performance of facial expression recognition is affected both in neurocognitive disorders and in healthy elderly. Emotional content processing is expected to be adversely affected in dementia and as well in Parkinson's disease patients. The present study aims to compare the impaired facial expression recognition between different types of dementia patients, functional connectivity changes in patients with mild cognitive impairment, and patients with dementia were investigated by event‐related EEG‐coherence.Method25 Healthy elderly controls (HC), 25 Mild Cognitive Impairment patients (MCI), 25 patients with Alzheimer's disease (AD), 15 patients with Parkinson's disease with Mild Cognitive Impairment (PDMCI), and 16 patients with Parkinson's disease Dementia (PDD) were included in the study. EEG was recorded while angry, happy, and neutral facial expressions were presented. The artifacts were rejected by using Independent‐Component‐Analysis and Current‐Source‐Density. Magnitude‐squared coherence values of theta (4‐7Hz, 50‐400ms) responses were calculated. Inter‐hemispheric and intra‐hemispheric (frontal and central centered) pairs of F3, F4, C3, C4, T7, T8, Tp7, Tp8, P3, P4, O1, O2 channels were used. Two separate inter and intra hemispheric repeated‐measures‐ANOVA models were used for group comparisons.ResultThere were no group differences for intra‐hemispheric comparisons, but GroupXLocation comparison (p=.040) was significant, HE and MCI groups had higher coherence values in central‐ temporal, central‐ temporoparietal, and central‐ occipital connections compared to PDMCI, PDD, and AD patient groups (Figure 1). FaceXGroup (p=.009) interaction was significant for inter‐hemispheric coherence values, HC and MCI groups had higher interhemispheric coherence values than the PDMCI, PDD, and AD, especially during recognition of "angry" facial expression.ConclusionThe connectivity between the central and posterior regions increased with emotional face content; this increase seems to be preserved in MCI. On the other hand, it wasn't maintained in patients with more severe neurocognitive impairment; PDMCI, PDD, and AD groups had reduced central‐posterior connectivity and interhemispheric connectivity during the facial expression recognition paradigm. The decrease in inter and intra‐connectivity in the posterior regions is consistent with behavioral scores and may indicate neural losses as a phenomenon.

A dry electroencephalogram electrode for applications in steady-state visual evoked potential-based brain–computer interface systems

High-efficiency electroencephalogram (EEG) dry electrodes are a key component of brain–computer interface (BCI) technology because of their direct contact with the scalp. In this study, a semi-flexible polydopamine (PDA)/Pt-TiO2 electrode is prepared for the dry-contact acquisition of EEG signals. The PDA biofilm adheres strongly to the scalp and maintains a dynamic balance of water and ions. The Pt nanoparticles and TiO2 nanotube array together result in fast electron transfer. Therefore, the interface impedance between the dry PDA/Pt-TiO2 electrode and scalp is as low as 19.63–24.53 kΩ. The spontaneous EEG signal collected simultaneously using the dry PDA/Pt-TiO2 and wet Ag/AgCl electrodes had a correlation coefficient of up to 99.9%. In a steady-state visual evoked potential (SSVEP)-based BCI system, the dry electrode was used to collect EEG feedback signals for stimulations at 27 different frequencies in the range of 7–19.25 Hz. For these feedback signals, O1, Oz, and O2 channels in the occipital area exhibited high signal-to-noise ratios of 11.3, 11.8, and 11 dB, respectively. A volunteer wore an EEG headband with three PDA/Pt-TiO2 dry electrodes and successfully controlled the robotic arm of the SSVEP-BCI system in the untrained mode. The dry PDA/Pt-TiO2 electrode-based EEG cap is comfortable to wear, the identification signals of the SSVEP paradigm are accurate, and it is suitable for controlling external devices including a keyboard in the SSVEP-BCI system.

Quantitation of transmembrane O 2 flux via RhAG in a neutral‐buoyancy assay

The central dogma of transmembrane gas flux had been that all gases cross all membranes by dissolving in the lipid phase of the membrane. Contradicting this simple view are three discoveries: (1) CO2-impermeable membranes; (2) CO2-permeable membrane proteins or “gas-channels”, namely, certain aquaporins (AQPs) and rhesus (Rh) proteins; and (3) a reduction in CO2-permeability when incorporating the CO2-impermeable Nicotiana tabacum AQP NtPIP2;1, into artificial membranes. The characterization of the CO2 and NH3 permeabilities of AQPs 0-9 and several rhesus (Rh) proteins shows that they can exhibit gas selectivity. Our laboratory has found that, during O2-offloading, ~55% of O2 exits mouse red blood cells (RBCs) via AQP1 and the Rh complex (i.e., RhAG + mRh). To characterize the mechanism by which RhAG and other proteins conduct O2, and to screen for novel O2 channels in a heterologous expression system, we have modified of our Neutral Buoyancy Assay (NBA), originally developed to assess transmembrane N2 fluxes. We inject a precise volume of N2 gas (number of gas molecules = nGas) into a Xenopus oocyte, which we place into a saline-containing pressure-resistant tube. We then impose sufficient pressure in the air phase above the air-water interface to collapse the injected bubble enough to maintain the oocyte at a depth of 5 cm. As N2 gas dissolves in the cytosol and ultimately diffuses into the extracellular fluid (ECF), the bubble shrinks, cell density increases, and the oocyte sinks. A camera/computer combination detects the sinking and decreases air-phase pressure enough to maintain neutral buoyancy at 5 cm depth. Calibration exercises allow us to compute the time course of ΔnGas, and thus gas efflux. Here we modify the NBA to quantify gas influx. When we raise [N2] in the ECF from [N2]o = 0.56 mM (room air at 1×ATA) to [N2]o = 2.06 mM (pre-equilibrating saline with 93% N2/7% O2 at 3×ATA) at constant [O2]o = 0.26 mM, N2 enters the cell during the NBA and nGas rises. If we now hold [N2]o constant at 2.06 mM but selectively increase [O2]o from 0.26 mM to 0.91 mM (pre-equilibrating saline with 78.96% N2/21% O2/0.04% CO2 at 3.5×ATA), nGas rises faster. Compared to control oocytes (injected with H2O rather than cRNA encoding RhAG), RhAG-expressing oocytes exhibit no significant difference in ΔnGas over 1000 s when [N2]o = 2.06 mM/[O2]o = 0.26 mM. Thus, RhAG appears not to be an N2 channel. However, when [N2]o = 2.06 mM/[O2]o = 0.91 mM, ΔnGas over 1000 s is significantly greater in RhAG vs control oocytes. Thus, RhAG is selective for O2 over N2. When we repeat the low-to-high [O2]o experiment with oocytes expressing NtPIP1;3 (reported to facilitate O2 flux in a spectrophotometric assay when expressed in yeast protoplasts), we also observe substantial increases in ΔnGas over 1000 s vs control oocytes. These data are consistent with our previous findings in RBCs that the Rh complex constitutes a major pathway for O2 flux across the plasma membrane, and also supports the NtPIP1;3 report from another laboratory. Although we specifically developed the NBA for N2 fluxes, here we demonstrate the versatility of the assay for measuring transmembrane fluxes of other gases.

Decrease in brain complexity with methylphenidate treatment in boys diagnosed with attention deficit hyperactivity disorder: An entropy-based qeeg analysis

IntroductionAttention deficit hyperactivity (ADHD) disorder is a common childhood neurodevelopmental disorder, and Methylphenidate (MPH) is a first-line therapeutic option for treating ADHD.However, how brain complexity and entropy changes with methylphenidate treatment the clinical implications of possible changes in entropy and the clinical implications of possible changes in entropy have yet to be studied.ObjectivesThis study aimed to reveal how the MPH treatment affects the complexity in the brain of children with ADHD by entropy-based qEEG analysis. In addition, the presence of the relationship between possible neurophysiological changes to be detected with clinical variables and how they are two other important questions of this study to be answered.MethodsDuring eyes-open resting, EEG signals were recorded from 25 boys with ADHD-combined type before MPH administration and at the end of the 1st month of the treatment. Approximate entropy (ApEn), sample entropy (SampEn), permutation entropy (PermEn) were used to analyse.ResultsA statistically significant decrease in entropy level was found with MPH treatment in the F4 channel according to approximate entropy (ApEn) and sample entropy (SampEn) analysis (p&lt;0.05). In addition, according to permutation entropy (PermEn) analysis, the decrease in entropy with MPH treatment in the regions indicated by F3, F4, P4, T3, T6, and O2 channels was found to be statistically significant (p &lt;0.05).ConclusionsThis is the first study to investigate how MPH treatment affects the complexity in the brain of children with ADHD. Entropy-based qEEG analysis may be a new method that can be used in diagnostic, clinical and prognostic predictions in ADHD.DisclosureNo significant relationships.

Transcranial Direct Current Stimulation Effect on Virtual Hand Illusion.

Virtual reality (VR) is effectively used to evoke the mirror illusion, and transcranial direct current stimulation (tDCS) synergistically facilitates this illusion. This study investigated whether a mirror virtual hand illusion (MVHI) induced by an immersive, first-person-perspective, virtual mirror system could be modulated by tDCS of the primary motor cortex. Fourteen healthy adults (average age 21.86 years ±0.47, seven men and seven women) participated in this study, and they experienced VR with and without tDCS-the tDCS and sham conditions, each of which takes ∼30 minutes-on separate days to allow the washout of the tDCS effect. While experiencing VR, the movements of the virtual left hand reflected the flexion and extension of the real right hand. Subsequently, electroencephalogram was recorded, the magnitude of the proprioceptive shift was measured, and the participants provided responses to a questionnaire regarding hand ownership. A significant difference in the proprioceptive shift was observed between the tDCS and sham conditions. In addition, there was significant suppression of the mu power in Pz, and augmentation of the beta power in the Pz, P4, O1, and O2 channels. The difference in proprioceptive deviation between the two conditions showed significant negative correlation with mu suppression over the left frontal lobe in the tDCS condition. Finally, the question "I felt that the virtual hand was my own hand" received a significantly higher score under the tDCS condition. In short, applying tDCS over the motor cortex facilitates the MVHI by activating the attentional network over the parietal and frontal lobes such that the MVHI induces more proprioceptive drift, which suggests that the combination of VR and tDCS can enhance the immersive effect in VR. This result provides better support for the use of the MVHI paradigm in combination with tDCS for recovery from illnesses such as stroke.

Information-Based Classification of Electroencephalography (EEG) Signals for Healthy Adolescents and Adolescents with Symptoms of Schizophrenia

Analysis of the brain activity is the major research area in human neuroscience. Besides many works that have been conducted on analysis of brain activity in case of healthy subjects, investigation of brain activity in case of patients with different brain disorders also has aroused the attention of many researchers. An interesting category of works belong to the comparison of brain activity between healthy subjects and patients with brain disorders. In this research, for the first time, we compare the brain activity between adolescents with symptoms of schizophrenia and healthy subjects, by information-based analysis of their Electroencephalography (EEG) signals. For this purpose, we benefit from the Shannon entropy as the indicator of information content. Based on the results of analysis, EEG signal in case of healthy subjects contains more information than EEG signal in case of subjects with schizophrenia. The result of statistical analysis showed the significant variation in the Shannon entropy of EEG signal between healthy adolescents and adolescents with symptoms of schizophrenia in case of P3, O1 and O2 channels. The employed method of analysis in this research can be further extended in order to investigate the variations in the information content of EEG signal in case of subjects with other brain disorders versus healthy subjects.

VR motion sickness recognition by using EEG rhythm energy ratio based on wavelet packet transform

Background and objectivesVirtual reality motion sickness (VRMS) is one of the main factors hindering the development of VR technology. At present, the VRMS recognition methods using electroencephalogram (EEG) signals have poor applicability to multiple subjects. MethodsAiming at this dilemma, the wavelet packet transform (WPT), was used to propose a feature extraction method for EEG rhythm energy ratios of delta (δ), theta (θ), alpha (α), and beta (β) in this research. Moreover, VRMS was recognized by combining k-Nearest Neighbor classifier (k-NN), support vector machine (SVM) with polynomial kernel (polynomial-SVM) and radial basis function kernel (RBF-SVM), respectively. The method is that the raw EEG signals were de-noised by an elliptical band-pass filter and segmented by a fixed window, 7-level db4 WPT was performed on each EEG segment, and the wavelet packet energy ratios of delta, theta, alpha and beta rhythms from FP1, FP2, C3, C4, P3, P4, O1 and O2 channels were calculated and combined to form feature vectors for recognizing VRMS. ResultsUnder the condition of 4-s window size, the average VRMS recognition accuracy of polynomial-SVM for the single subject was 92.85%, and the VRMS recognition accuracy of 18 subjects was about 79.25%. ConclusionsCompared with other VRMS recognition methods, this method does not only have a higher recognition accuracy to a single subject, but also have better applicability to multiple subjects. Meanwhile, when using the EEG four rhythm energy ratios of FP1, FP2, C3, C4, P3, P4, O1 and O2 channels as feature vectors, the polynomial-SVM achieved better VRMS recognition performance than the k-NN and RBF-SVM.