Scalp Electrodes Research Articles

Auditory closed-loop stimulation on sleep slow oscillations using in-ear EEG sensors.

Acoustic stimulation synchronized to slow oscillations in scalp electroencephalograms has been shown to enhance sleep features, which makes it promising in boosting cognitive functions as well as in the treatment of some sleep disturbances. Nevertheless, scalp electrode sensors are resource intensive and poorly tolerated by sleeping patients. The aim of this study was to investigate the potential usability of in-the-ear electroencephalography to implement auditory closed-loop stimulation during sleep. For this, we evaluated the agreement between slow oscillation recordings obtained through the in-ear sensor and those obtained simultaneously from standard scalp electrodes during naps of 13 healthy subjects. We found that in-ear activity provided enough information to automatically detect sleep slow oscillations in real-time. Based on this, we successfully enhanced scalp slow oscillations using auditory single-cycle closed-loop brain-state-dependent stimulation based on in-ear signals acquired in 11 further subjects. We conclude that in-ear sensors provide a feasible technology for the enhancement of sleep patterns, and could pave the way for new clinical applications in the near future.

The relation between belief in a just world and early processing of deserved and undeserved outcomes: An ERP study

ABSTRACT We used event-related potentials (ERPs) to examine how quickly people in general, and certain people in particular, process deservingness-relevant information. Female university students completed individual difference measures, including individual differences in the belief in a just world (BJW), a belief that people get what they deserve. They then read stories in which an outcome was deserved, undeserved, or neither deserved nor undeserved (i.e., “neutral”) while their ERPs were recorded with scalp electrodes. We found no overall differentiation between early ERP responses (<300 ms post-stimulus onset) to deserved, undeserved, and neutral outcomes. However, BJW correlated with the difference between early ERP responses to these forms of information (rs from |.44| to |.61|; ps from .018 to < .001). The early nature of our effects (e.g., 96 ms after stimulus onset) suggests individual differences in socially-relevant information processing that begins before conscious evaluation of the stimuli. Potential underlying processes include automatic attention to schema-relevant information and to unexpected (and therefore salient) information and automatic processing of belief-consistent information. Our research underscores the importance of the concept of deservingness in human information processing as well as the utility of ERP technology and robust statistical analyses in investigations of complex social stimuli.

An Unexpected Cause of Recurrent Fetal Decelerations

An Unexpected Cause of Recurrent Fetal Decelerations

EEG Dipole Source Localization using Deep Neural Network

Electroencephalography (EEG) inverse problem is a typical inverse problem, in which the electrical activity within the brain is reconstructed based on EEG data collected from the scalp electrodes. In this paper, the four-layer concentric head model is used for simulation firstly, four deep neural network models including a multilayer perceptron (MLP) model and three convolutional neural networks (CNNs) are adopted to solve EEG inverse problem based on equal current dipole (ECD) model. In the simulations, 100,000 samples are generated randomly, of which 60% are used for network training and 20% are used for cross-validation. Eventually, the generalization performance of the model using the optimal function is measured by the errors in the rest 20% testing set. The experimental results show that the absolute error, relative error, mean positioning error and standard deviation of the four models are extremely low. The CNN with 6 convolutional layers and 3 pooling layers (CNN-3) is the best model. Its absolute error is about 0.015, its relative error is about 0.005, and its dipole position error is 0.040±0.029 cm. Furthermore, we use CNN-3 for source localization of the real EEG data in Working Memory. The results are in accord with physiological experience. The deep neural network method in our study needs fewer calculation parameters, takes less time, and has better positioning results.

Electric hearing and tinnitus suppression by noninvasive ear stimulation

While noninvasive brain stimulation is convenient and cost effective, its utility is limited by the substantial distance between scalp electrodes and their intended neural targets in the head. The tympanic membrane, or eardrum, is a thin flap of skin deep in an orifice of the head that may serve as a port for improved efficiency of noninvasive stimulation. Here we chose the cochlea as a target because it resides in the densest bone of the skull and is adjacent to many deep-brain-stimulation structures. We also tested the hypothesis that noninvasive electric stimulation of the cochlea may restore neural activities that are missing in acoustic stimulation. We placed an electrode in the ear canal or on the tympanic membrane in 25 human adults (10 females) and compared their stimulation efficiency by characterizing the electrically-evoked auditory sensation. Relative to ear canal stimulation, tympanic membrane stimulation was four times more likely to produce an auditory percept, required eight times lower electric current to reach the threshold and produced two-to-four times more linear suprathreshold responses. We further measured tinnitus suppression in 14 of the 25 subjects who had chronic tinnitus. Compared with ear canal stimulation, tympanic membrane stimulation doubled both the probability (22% vs. 55%) and the amount (−15% vs. −34%) of tinnitus suppression. These findings extended previous work comparing evoked perception and tinnitus suppression between electrodes placed in the ear canal and on the scalp. Together, the previous and present results suggest that the efficiency of conventional scalp-based noninvasive electric stimulation can be improved by at least one order of magnitude via tympanic membrane stimulation. This increased efficiency is most likely due to the shortened distance between the electrode placed on the tympanic membrane and the targeted cochlea. The present findings have implications for the management of tinnitus by offering a potential alternative to interventions using invasive electrical stimulation such as cochlear implantation, or other non-invasive transcranial electrical stimulation methods.

Investigating Neurophysiological Markers of Symptom Severity in Alzheimer's Disease.

Alzheimer's disease (AD) is characterized by a progressive decline in cognitive functioning for which there is a stark lack of effective treatments. Investigating the neurophysiological markers of symptom severity in AD may aid in the identification of alternative treatment targets. In the current study we used a multimodal approach to investigate the association between functional connectivity (specifically between scalp electrodes placed over frontal and parietal regions) and symptom severity in AD, and to explore the relationship between connectivity and cortical excitability. 40 people with AD (25 mild severity, 15 moderate severity) underwent neurobiological assessment (resting state electroencephalography (EEG) and prefrontal transcranial magnetic stimulation (TMS) with EEG) and cognitive assessment. Neurobiological outcomes were resting state functional connectivity and TMS-evoked potentials. Cognitive outcomes were scores on the Alzheimer's Disease Assessment Scale-Cognitive Subscale, Mini-Mental Status Examination, and a measure of episodic verbal learning. Greater contralateral functional theta connectivity between frontal scalp electrodes and parietal scalp electrodes was associated with poorer cognitive performance. In addition, significant correlations were seen between the contralateral theta connectivity and the N100 and P60 TMS-evoked potentials measured from electrodes over the left dorsolateral prefrontal cortex. Together these findings provide initial support for the use of multimodal neurophysiological approaches to investigate potential therapeutic targets in AD. Suggestions for future research are discussed.

Covariate-adjusted hybrid principal components analysis for region-referenced functional EEG data.

Electroencephalography (EEG) studies produce region-referenced functional data via EEG signals recorded across scalp electrodes. The high-dimensional data can be used to contrast neurodevelopmental trajectories between diagnostic groups, for example between typically developing (TD) children and children with autism spectrum disorder (ASD). Valid inference requires characterization of the complex EEG dependency structure as well as covariate-dependent heteroscedasticity, such as changes in variation over developmental age. In our motivating study, EEG data is collected on TD and ASD children aged two to twelve years old. The peak alpha frequency, a prominent peak in the alpha spectrum, is a biomarker linked to neurodevelopment that shifts as children age. To retain information, we model patterns of alpha spectral variation, rather than just the peak location, regionally across the scalp and chronologically across development. We propose a covariate-adjusted hybrid principal components analysis (CA-HPCA) for EEG data, which utilizes both vector and functional principal components analysis while simultaneously adjusting for covariate-dependent heteroscedasticity. CA-HPCA assumes the covariance process is weakly separable conditional on observed covariates, allowing for covariate-adjustments to be made on the marginal covariances rather than the full covariance leading to stable and computationally efficient estimation. The proposed methodology provides novel insights into neurodevelopmental differences between TD and ASD children.

A new method of localizing brain activity using the scalp EEG data

We propose a spatial localization method for scalp electroencephalography (EEG). The technique allows reliable and unambiguous identification of the activity for any intracerebral source by its spatial coordinates. Our "virtually implanted electrode" is based on the dynamics and correlation analysis of signals in the EEG leads with the addition of artificially generated data. The generated data helps us accurately model the electrical potential distribution between the studied source and the scalp electrodes. Each modeled intracerebral source is analyzed independently, so the proposed method does not require an estimate of the possible number of sources. The method produces output values that can be interpreted as the "local field" elec-trical activity for the implanted electrode in the corresponding brain point. The signal cleaning procedure makes it possible to exclude the influence from neighboring regions and detect a given brain region's isolated electrical activity. For verification, the method was applied in the empathy study on a sample of 16 people. We compared the background EEG of calm wakefulness with the EEG of empathic behavior for the 33 preselected brain regions.

Continuous Scoring of Depression From EEG Signals via a Hybrid of Convolutional Neural Networks.

Depression score is traditionally determined by taking the Beck depression inventory (BDI) test, which is a qualitative questionnaire. Quantitative scoring of depression has also been achieved by analyzing and classifying pre-recorded electroencephalography (EEG) signals. Here, we go one step further and apply raw EEG signals to a proposed hybrid convolutional and temporal-convolutional neural network (CNN-TCN) to continuously estimate the BDI score. In this research, the EEG signals of 119 individuals are captured by 64 scalp electrodes through successive eyes-closed and eyes-open intervals. Moreover, all the subjects take the BDI test and their scores are determined. The proposed CNN-TCN provides mean squared error (MSE) of 5.64±1.6 and mean absolute error (MAE) of 1.73±0.27 for eyes-open state and also provides MSE of 9.53±2.94 and MAE of 2.32±0.35 for the eyes-closed state, which significantly surpasses state-of-the-art deep network methods. In another approach, conventional EEG features are elicited from the EEG signals in successive frames and apply them to the proposed CNN-TCN in conjunction with known statistical regression methods. Our method provides MSE of 10.81±5.14 and MAE of 2.41±0.59 that statistically outperform the statistical regression methods. Moreover, the results with raw EEG are significantly better than those with EEG features.

Cortical activity, kinematics and trunk muscles activity response to pelvis movements during unstable sitting.

Balance control is a leading component of human motor activities and its impairment is associated with an increased risk of falling, lower back pain due to impaired motor control mechanism. Prolonged sitting position at workplace is one of the risk factors of reduced postural control and lower back pain. To evaluate theta and alpha waves cortical activity, trunk muscles activity and kinematics in static sitting, dynamic sitting on different platforms: simple wobble board (WB) and wobble board on bearing surface (WBB). The kinematics of body segments, electromyography of five trunk muscles, electroencephalography of 32 scalp electrodes were measured during balance tasks in sitting position for 17 subjects with continuous seated position at workplace. Cortical power on WBB1 increase in fronto - central (p< 0.05) region while on WBB2 increase in centro - parietal region (p< 0.05). WBB2 increase more muscles compared with WB2. The amplitude of movement of ASIS, Th10 can be seen lower on WBB compared with WB (p< 0.05). The study shows that WBB can increase personalized sitting and improve trunk motor control during hours of prolonged sitting.

A Computational Modeling Study to Investigate the Use of Epicranial Electrodes to Deliver Interferential Stimulation to Subcortical Regions.

Background: Epicranial cortical stimulation (ECS) is a minimally invasive neuromodulation technique that works by passing electric current between subcutaneous electrodes positioned on the skull. ECS causes a stronger and more focused electric field in the cortex compared to transcranial electric stimulation (TES) where the electrodes are placed on the scalp. However, it is unknown if ECS can target deeper regions where the electric fields become relatively weak and broad. Recently, interferential stimulation (IF) using scalp electrodes has been proposed as a novel technique to target subcortical regions. During IF, two high, but slightly different, frequencies are applied which sum to generate a low frequency field (i.e., 10 Hz) at a target subcortical region. We hypothesized that IF using ECS electrodes would cause stronger and more focused subcortical stimulation than that using TES electrodes.Objective: Use computational modeling to determine if interferential stimulation-epicranial cortical stimulation (IF-ECS) can target subcortical regions. Then, compare the focality and field strength of IF-ECS to that of interferential Stimulation-transcranial electric stimulation (IF-TES) in the same subcortical region.Methods: A human head computational model was developed with 19 TES and 19 ECS disk electrodes positioned on a 10–20 system. After tetrahedral mesh generation the model was imported to COMSOL where the electric field distribution was calculated for each electrode separately. Then in MATLAB, subcortical targets were defined and the optimal configurations were calculated for both the TES and ECS electrodes.Results: Interferential stimulation using ECS electrodes can deliver stronger and more focused electric fields to subcortical regions than IF using TES electrodes.Conclusion: Interferential stimulation combined with ECS is a promising approach for delivering subcortical stimulation without the need for a craniotomy.

A Novel Approach to Teaching the Cervical Exam: A Versatile, Low-Cost Simulation for Labor and Delivery Learners.

This technical report describes the making of cervical exam models that can be used to teach cervical dilation and effacement, with the versatility to teach additional obstetrical skills including artificial rupture of membranes (AROM) and fetal scalp electrode (FSE) placement. These models, primarily constructed from materials that are low cost and/or easily accessible within a healthcare setting, can be used to educate nurses, medical students, residents, and other healthcare professionals to improve the evaluation of the labor progress.

Input–Output Functions in Human Heads Obtained With Cochlear Implant and Transcranial Electric Stimulation

Electric stimulation is used to treat a number of neurologic disorders such as epilepsy and depression. However, delivering the required current to far-field neural targets is often ineffective because of current spread through low-impedance pathways. Here, the specific aims are to develop an empirical measure for current passing through the human head and to optimize stimulation strategies for targeting deeper structures, including the auditory nerve, by utilizing the cochlear implant (CI). Outward input/output (I/O) functions were obtained by CI stimulation and recording scalp potentials in five CI subjects. Conversely, inward I/O functions were obtained by noninvasive transcranial electric stimulation (tES) and recording intracochlear potentials using the onboard recording capability of the CI. I/O measures indicate substantial current spread, with a maximum of 2.2% gain recorded at the inner ear target during tES (mastoid-to-mastoid electrode configuration). Similarly, CI stimulation produced a maximum of 1.1% gain at the scalp electrode nearest the CI return electrode. Gain varied with electrode montage according to a point source model that accounted for distances between the stimulating and recording electrodes. Within the same electrode montages, current gain patterns varied across subjects suggesting the importance of tissue properties, geometry, and electrode positioning. These results provide a novel objective measure of electric stimulation in the human head, which can help to optimize stimulation parameters that improve neural excitation of deep structures by reducing the influence of current spread. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Differences in Performance of ASD and ADHD Subjects Facing Cognitive Loads in an Innovative Reasoning Experiment

We aim to investigate whether EEG dynamics differ in adults with ASD (Autism Spectrum Disorders) and ADHD (attention-deficit/hyperactivity disorder) compared with healthy subjects during the performance of an innovative cognitive task, Aristotle’s valid and invalid syllogisms, and how these differences correlate with brain regions and behavioral data for each subject. We recorded EEGs from 14 scalp electrodes (channels) in 21 adults with ADHD, 21 with ASD, and 21 healthy, normal subjects. The subjects were exposed in a set of innovative cognitive tasks (inducing varying cognitive loads), Aristotle’s two types of syllogism mentioned above. A set of 39 questions were given to participants related to valid–invalid syllogisms as well as a separate set of questionnaires, in order to collect a number of demographic and behavioral data, with the aim of detecting shared information with values of a feature extracted from EEG, the multiscale entropy (MSE), in the 14 channels (‘brain regions’). MSE, a nonlinear information-theoretic measure of complexity, was computed to extract a feature that quantifies the complexity of the EEG. Behavior-Partial Least Squares Correlation, PLSC, is the method to detect the correlation between two sets of data, brain, and behavioral measures. -PLSC, a variant of PLSC, was applied to build a functional connectivity of the brain regions involved in the reasoning tasks. Graph-theoretic measures were used to quantify the complexity of the functional networks. Based on the results of the analysis described in this work, a mixed 14 × 2 × 3 ANOVA showed significant main effects of group factor and brain region* syllogism factor, as well as a significant brain region* group interaction. There are significant differences between the means of MSE (complexity) values at the 14 channels of the members of the ‘pathological’ groups of participants, i.e., between ASD and ADHD, while the difference in means of MSE between both ASD and ADHD and that of the control group is not significant. In conclusion, the valid–invalid type of syllogism generates significantly different complexity values, MSE, between ASD and ADHD. The complexity of activated brain regions of ASD participants increased significantly when switching from a valid to an invalid syllogism, indicating the need for more resources to ‘face’ the task escalating difficulty in ASD subjects. This increase is not so evident in both ADHD and control. Statistically significant differences were found also in the behavioral response of ASD and ADHD, compared with those of control subjects, based on the principal brain and behavior saliences extracted by PLSC. Specifically, two behavioral measures, the emotional state and the degree of confidence of participants in answering questions in Aristotle’s valid–invalid syllogisms, and one demographic variable, age, statistically and significantly discriminate the three groups’ ASD. The seed-PLC generated functional connectivity networks for ASD, ADHD, and control, were ‘projected’ on the regions of the Default Mode Network (DMN), the ‘reference’ connectivity, of which the structural changes were found significant in distinguishing the three groups. The contribution of this work lies in the examination of the relationship between brain activity and behavioral responses of healthy and ‘pathological’ participants in the case of cognitive reasoning of the type of Aristotle’s valid and invalid syllogisms, using PLSC, a machine learning approach combined with MSE, a nonlinear method of extracting a feature based on EEGs that captures a broad spectrum of EEGs linear and nonlinear characteristics. The results seem promising in adopting this type of reasoning, in the future, after further enhancements and experimental tests, as a supplementary instrument towards examining the differences in brain activity and behavioral responses of ASD and ADHD patients. The application of the combination of these two methods, after further elaboration and testing as new and complementary to the existing ones, may be considered as a tool of analysis in helping detecting more effectively such types of disorders.

Four-channel current switching device to enable multi-electrode magnetic resonance current density imaging.

Neurostimulation with multiple scalp electrodes has shown enhanced effects in recent studies. However, visualizations of stimulation-induced internal current distributions in brain is only possible through simulated current distributions obtained from computer model of human head. While magnetic resonance current density imaging (MRCDI) has a potential for direct in-vivo measurement of currents induced in brain with multi-electrode stimulation, existing MRCDI methods are only developed for two-electrode neurostimulation. A major bottleneck is the lack of a current switching device which is typically used to convert the DC current of neurostimulation devices into user-defined waveforms of positive and negative polarity with delays between them. In this work, we present a design of a four-electrode current switching device to enable simultaneous switching of current flowing through multiple scalp electrodes.

Abstract 1122‐000218: Flow Reversal After Stent Diversion Causes In‐Stent Thrombosis and Anemia‐Driven Ischemia

Introduction : Large symptomatic ICA aneurysms are rare, but present a life threatening risk of rupture that increases with size, female sex, and age &gt;50 at the time of diagnosis, among other risk factors. Historically, large carotid aneurysms have been treated with intentional carotid sacrifice, requiring recruitment of contralateral, posterior, and ECA‐supplied collaterals to provide flow to the anterior circulation previously supplied by the sacrificed ICA, lest the patient experience an iatrogenic stroke. While still a viable option in some cases, flow diverting stents provide an attractive alternative to vessel sacrifice. By providing a channel for blood to bypass the aneurysm, the stent can effectively exclude the aneurism from active circulation while preserving a path for blood to travel to the anterior cerebral circulation it currently provides. Methods : Here, we present a case of a 1.3 cm symptomatic left cavernous ICA aneurysm treated with such a flow diverting stent. Results : The patient presented to the emergency department with left sided ptosis. CTA head and neck revealed the 1.3 cm left sided cavernous ICA aneurysm. She was treated endovascularly under general anesthesia with continuous intra‐operative monitoring. The procedure was complicated by iatrogenic flow reversal through the Circle of Willis at the time of stent deployment and resultant in situ thrombosis of the stent without alteration in electrical signals recorded at the scalp – thus creating a de facto carotid sacrifice without intra‐operative complication. Follow up doppler study revealed a loss of flow through the left ICA and reversal of flow through the ophthalmic artery on the left side, thus confirming ECA collateral supply to the area. Post‐op course was complicated by extensive bleeding from the scalp electrode sites used for intraoperative monitoring due to hyper‐response to aspirin‐ticagrelor dual‐antiplatelet therapy. This gave rise to a symptomatic anemia that manifested as pressure‐dependent left‐sided circulatory failure on exam – specifically hemiparesis and aphasia. The symptoms ultimately resolved with pressure augmentation, blood transfusion, and supportive care in the Neuro ICU. The patient was successfully transitioned to a general neurology floor with subsequent resolution of the anemia and, correspondingly, the symptoms. Conclusions : The patient was discharged to rehab and at 4‐month follow‐up is again living independently with no residual deficits. This case has significance for pre‐operative anti‐platelet optimization for flow diverting stents, management of post‐operative complications of flow‐diverting stent placement including thrombosis and bleeding, and optimal critical care support for patients with pressure‐dependent ischemia. Specifically, the course of the patient’s symptoms and anemia raise the question of optimal hemoglobin targets in the subset of patients with pressure‐dependent ischemia, and how to best reach those targets.

Schizophrenia Classification Using Resting State EEG Functional Connectivity: Source Level Outperforms Sensor Level.

Disrupted functional and structural connectivity measures have been used to distinguish schizophrenia patients from healthy controls. Classification methods based on functional connectivity derived from EEG signals are limited by the volume conduction problem. Recorded time series at scalp electrodes capture a mixture of common sources signals, resulting in spurious connections. We have transformed sensor level resting state EEG times series to source level EEG signals utilizing a source reconstruction method. Functional connectivity networks were calculated by computing phase lag values between brain regions at both the sensor and source level. Brain complex network analysis was used to extract features and the best features were selected by a feature selection method. A logistic regression classifier was used to distinguish schizophrenia patients from healthy controls at five different frequency bands. The best classifier performance was based on connectivity measures derived from the source space and the theta band.The transformation of scalp EEG signals to source signals combined with functional connectivity analysis may provide superior features for machine learning applications.

Motor Imagery, Execution, and Observation Classification using Small Amount of EEG Data with Multiple Two-Class CNNs.

This study attempted to classify a small amount of electroencephalogram (EEG) data on five states: four tasks involving right index-finger flexion (kinesthetic motor imagery, visual motor imagery, motor execution, and motor observation) and resting with eyes open. We employed a convolutional neural network (CNN) as a classifier and compared the classification accuracies of two types of CNNs: 1) a "single five-class CNN," which classified the aforementioned states with a single CNN and 2) "multiple two-class CNNs," wherein ten CNNs that classify pairs of states were combined. In addition, the classification accuracies were compared between two scenarios: one wherein the EEGs from all 19 scalp probe electrodes (19-channel EEG) were adopted as input data for the CNN, and the other wherein the EEGs of four regions closely related to the motor execution and observation of the index finger (4-channel EEG) were adopted. The classification accuracies of the single five-class CNN with 19- and 4-channel EEGs were 48.2 ± 5.9% and 46.6 ± 6.9%, respectively, and those of the multiple two-class CNNs with 19- and 4-channel EEGs were 52.8 ± 9.7% and 47.5 ± 9.4%, respectively. These results indicate the effectiveness of multiple two-class CNNs that utilize the EEGs of all scalp electrodes as input data for classifying motor imagery, execution, and observation, even in the case of the marginal dataset.

Influence of the Number of Channels and Classification Algorithm on the Performance Robustness to Electrode Shift in Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces.

There remains an active investigation on elevating the classification accuracy and information transfer rate of brain-computer interfaces based on steady-state visual evoked potential. However, it has often been ignored that the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can be affected through the minor displacement of the electrodes from their optimal locations in practical applications because of the mislocation of electrodes and/or concurrent use of electroencephalography (EEG) devices with external devices, such as virtual reality headsets. In this study, we evaluated the performance robustness of SSVEP-based BCIs with respect to the changes in electrode locations for various channel configurations and classification algorithms. Our experiments involved 21 participants, where EEG signals were recorded from the scalp electrodes densely attached to the occipital area of the participants. The classification accuracies for all the possible cases of electrode location shifts for various channel configurations (1–3 channels) were calculated using five training-free SSVEP classification algorithms, i.e., the canonical correlation analysis (CCA), extended CCA, filter bank CCA, multivariate synchronization index (MSI), and extended MSI (EMSI). Then, the performances of the BCIs were evaluated using two measures, i.e., the average classification accuracy (ACA) across the electrode shifts and robustness to the electrode shift (RES). Our results showed that the ACA increased with an increase in the number of channels regardless of the algorithm. However, the RES was enhanced with an increase in the number of channels only when MSI and EMSI were employed. While both ACA and RES values for the five algorithms were similar under the single-channel condition, both ACA and RES values for MSI and EMSI were higher than those of the other algorithms under the multichannel (i.e., two or three electrodes) conditions. In addition, EMSI outperformed MSI when comparing the ACA and RES values under the multichannel conditions. In conclusion, our results suggested that the use of multichannel configuration and employment of EMSI could make the performance of SSVEP-based BCIs more robust to the electrode shift from the optimal locations.

Low doses of LSD reduce broadband oscillatory power and modulate event-related potentials in healthy adults.

Classical psychedelics, including psilocybin and lysergic acid diethylamide (LSD), are under investigation as potential therapeutic agents in psychiatry. Whereas most studies utilize relatively high doses, there are also reports of beneficial effects of "microdosing," or repeated use of very low doses of these drugs. The behavioral and neural effects of these low doses are not fully understood. To examine the effects of LSD (13 μg and 26 μg) versus placebo on resting-state electroencephalography (EEG) and event-related potential (ERP) responses in healthy adults. Twenty-two healthy men and women, 18 to 35 years old, participated in 3 EEG sessions in which they received placebo or LSD (13 μg and 26 μg) under double-blind conditions. During each session, participants completed drug effect and mood questionnaires at hourly intervals, and physiological measures were recorded. During expected peak drug effect, EEG recordings were obtained, including resting-state neural oscillations in scalp electrodes over default mode network (DMN) regions and P300, N170, and P100 ERPs evoked during a visual oddball paradigm. LSD dose-dependently reduced oscillatory power across delta, theta, alpha, beta, and gamma frequency bands during both eyes closed and eyes open resting conditions. During the oddball task, LSD dose-dependently reduced ERP amplitudes for P300 and N170 components and increased P100 latency. LSD also produced dose-related increases in positive mood, elation, energy, and anxiety and increased heart rate and blood pressure. On a measure of altered states of consciousness, LSD dose-dependently increased Blissful State, but not other indices of perceptual or sensory effects typical of psychedelic drugs. The subjective effects of the drug were not correlated with the EEG measures. Low doses of LSD produced broadband cortical desynchronization over the DMN during resting state and reduced P300 and N170 amplitudes, patterns similar to those reported with higher doses of psychedelics. Notably, these neurophysiological effects raise the possibility that very low doses of LSD may produce subtle behavioral and perhaps therapeutic effects that do not rely on the full psychedelic experience.