EEG Rhythms Research Articles

Coherent categorical information triggers integration-related alpha dynamics.

To create coherent visual experiences, the brain spatially integrates the complex and dynamic information it receives from the environment. We previously demonstrated that feedback-related alpha activity carries stimulus-specific information when two spatially and temporally coherent naturalistic inputs can be integrated into a unified percept. In this study, we sought to determine whether such integration-related alpha dynamics are triggered by categorical coherence in visual inputs. In an EEG experiment, we manipulated the degree of coherence by presenting pairs of videos from the same or different categories through two apertures in the left and right visual hemifields. Critically, video pairs could be video-level coherent (i.e., stem from the same video), coherent in their basic-level category, coherent in their superordinate category, or incoherent (i.e., stem from videos from two entirely different categories). We conducted multivariate classification analyses on rhythmic EEG responses to decode between the video stimuli in each condition. As the key result, we significantly decoded the video-level coherent and basic-level coherent stimuli, but not the superordinate coherent and incoherent stimuli, from cortical alpha rhythms. This suggests that alpha dynamics play a critical role in integrating information across space, and that cortical integration processes are flexible enough to accommodate information from different exemplars of the same basic-level category.NEW & NOTEWORTHY Our brain integrates dynamic inputs across the visual field to create coherent visual experiences. Such integration processes have previously been linked to cortical alpha dynamics. In this study, the integration-related alpha activity was observed not only when snippets from the same video were presented, but also when different video snippets from the same basic-level category were presented, highlighting the flexibility of neural integration processes.

The Post-Flight Rehabilitation Program for Astronauts in Health Resort: Case Report Series

INTRODUCTION. The prospects for studying deep space are associated with the development of methods and programs for restoring the body of astronauts after flights. In this case, an important role is given to the implementation of aspects of restoring the functions of the nervous system of astronauts at the 2nd stage of post-flight rehabilitation (PFR) in Health Resort.
 AIM. To develop a program of rehabilitation for cosmonauts in Health Resort (stage 2 of PFR), aimed at restoring the functions of the nervous system.
 MATERIALS AND METHODS. The study involved 5 cosmonauts. Diagnostics included assessment of psychological and psychophysiological parameters and properties of the nervous system, the functional state of the autonomic nervous system. The rehabilitation program included: intake of mineral water “Slavyanovsky”, carbon dioxide-hydrogen sulfide baths, baro-, halo-, inhalation and climate therapy, a course of 5 sessions of neurofeedback on the β-rhythm of the brain. The duration of PFR was 21 days, procedures were carried out every other day.
 RESULTS AND DISCUSSION. An assessment of heart rate variability revealed the predominance of the influence of the parasympathetic division. After the rehabilitation course, there was a decrease in the stiffness index. There was a positive trend towards normalization of indicators: blood saturation, reflection index, a marker of left ventricular function, cardiac output, blood pressure and oxygen absorption from the microcirculatory system. The dynamics of biofeedback training indicators based on the β-rhythm indicated the orderliness and synchronization of the electroencephalogram (EEG) rhythm signal; an increase in the efficiency of mental work was also revealed.
 CONCLUSION. The results obtained indicate the positive impact of the developed program of the 2nd stage of PFR, as a result of which a positive dynamics of indicators of autonomic regulation, central hemodynamics, improvement of mental performance, orderliness and synchronization of the EEG rhythm signal.

A neurophysiological basis for aperiodic EEG and the background spectral trend

Electroencephalograms (EEGs) display a mixture of rhythmic and broadband fluctuations, the latter manifesting as an apparent 1/f spectral trend. While network oscillations are known to generate rhythmic EEG, the neural basis of broadband EEG remains unexplained. Here, we use biophysical modelling to show that aperiodic neural activity can generate detectable scalp potentials and shape broadband EEG features, but that these aperiodic signals do not significantly perturb brain rhythm quantification. Further model analysis demonstrated that rhythmic EEG signals are profoundly corrupted by shifts in synapse properties. To examine this scenario, we recorded EEGs of human subjects being administered propofol, a general anesthetic and GABA receptor agonist. Drug administration caused broadband EEG changes that quantitatively matched propofol’s known effects on GABA receptors. We used our model to correct for these confounding broadband changes, which revealed that delta power, uniquely, increased within seconds of individuals losing consciousness. Altogether, this work details how EEG signals are shaped by neurophysiological factors other than brain rhythms and elucidates how these signals can undermine traditional EEG interpretation.

Role of machine learning and deep learning techniques in EEG-based BCI emotion recognition system: a review

Emotion is a subjective psychophysiological reaction coming from external stimuli which impacts every aspect of our daily lives. Due to the continuing development of non-invasive and portable sensor technologies, such as brain-computer interfaces (BCI), intellectuals from several fields have been interested in emotion recognition techniques. Human emotions can be recognised using a variety of behavioural cues, including gestures and body language, voice, and physiological markers. The first three, however, might be ineffective because people sometimes conceal their genuine emotions either intentionally or unknowingly. More precise and objective emotion recognition can be accomplished using physiological signals. Among other physiological signals, Electroencephalogram (EEG) is more responsive and sensitive to variation in affective states. Various EEG-based emotion recognition methods have recently been introduced. This study reviews EEG-based BCIs for emotion identification and gives an outline of the progress made in this field. A summary of the datasets and techniques utilised to evoke human emotions and various emotion models is also given. We discuss several EEG feature extractions, feature selection/reduction, machine learning, and deep learning algorithms in accordance with standard emotional identification process. We provide an overview of the human brain's EEG rhythms, which are closely related to emotional states. We also go over a number of EEG-based emotion identification research and compare numerous machine learning and deep learning techniques. In conclusion, this study highlights the applications, challenges and potential areas for future research in identification and classification of human emotional states.

Electrophysiological features of bioelectrical activity of the horse brain

Purpose: to study the bioelectrical activity of the brain and determine the parameters of electroencephalograms in sports and untrained horses.Materials and methods. The object of the study were horses aged from 2 to 20 years in the amount of 150 heads. The animals were in active training and were also used for rental training in horse riding; their average weight was 550.78 kg; the breeds were Oryol Trotter, Russian Trotter, American Trotter and Hanoverian. During EEG recording, horses were restrained using a halter at the junctions. Experiments were carried out in the morning or afternoon, 1 hour before feeding/training or 1 hour after. EEG registration was carried out with patch electrodes in 6 unipolar leads using a special patented Ippolitova / Gauss helmet for recording EEG in animals, the electrodes on which are located along the projections of different lobes of the brain, which allows non-invasively, without the use of sedatives and shaving hair, to record the electrical activity of the brain . EEG recording continued for 10-15 minutes. We assessed brain rhythms using the following indicators: Rhythm severity index (RSI) – the degree of manifestation of the rhythm, the percentage of time during which a given rhythm is recorded (%). Frequency – the number of complete oscillations performed per unit of time, sec. (Hz). Amplitude is the largest deviation (from the average) value of a quantity, the range of oscillations.Results. As a result of the research and analysis of the obtained EEG data, waves of bioelectrical activity of the brain in horses were established in the following ranges: alpha rhythm (α-rhythm) – 7.1 – 13 Hz, amplitude up to 100 µV; beta-1 rhythm (low-frequency β-1 rhythm) – 13.1 – 25 Hz, amplitude – 3-5 µV; beta-2 rhythm (high-frequency β-2 rhythm) – 25.1 – 40 Hz, amplitude – 3-5 µV; delta rhythm (δ rhythm) – 0.3 – 4 Hz, amplitude up to 40 µV; theta rhythm (Ѳ-rhythm) – 4.1 – 7 Hz, amplitude about 30 µV; gamma rhythm (γ rhythm) – above 40.1 µV. When analyzing EEG rhythms in animals, the most pronounced spontaneous brain activity at rest in the horses studied is the delta rhythm in all leads and hemispheres. We observe oscillations with a frequency of 0.3–4 Hz and an amplitude of up to 40 μV. The waveform is regular, sinus. Such oscillations represent the delta rhythm.

Evaluation of Acupuncture Efficacy in Modulating Brain Activity with Periodic-aperiodic EEG Measurements.

Acupuncture is an important therapeutic method of traditional Chinese medicine and can effectively modulate brain disorders. The therapeutic efficacy of acupuncture is hard to evaluate due to lacking of effective measurements of brain activity. In this work, we design an EEG-based monitoring system to evaluate therapeutic effect of acupuncture on human brain by extracting periodic-aperiodic features. Power spectral density is estimated to compute the adjusted power of periodic oscillatory rhythm in EEG under acupuncture stimulation. It is exhibited that the brain activity in alpha band (8-12 Hz) is significantly enhanced during acupuncture, especially in parietal and occipital lobe regions. To probe the modulatory effect of acupuncture on aperiodic brain activity, we calculate the aperiodic exponent based on the parameterization of EEG power spectra. The aperiodic exponent decreases along with acupuncture process, which is more significant in central and frontal lobe regions. Furthermore, sensitivity of different brain regions to acupuncture is assessed by the integration of adjusted power and aperiodic exponent. Experimental results demonstrate the effectiveness of proposed periodic-aperiodic measurements of EEG signals, by which different effects of four acupuncture manipulations are precisely evaluated and a knowledge graph is established. The monitoring system provides a new perspective to quantitatively evaluate acupuncture effect on human brain and improve its therapeutic efficacy in clinical applications for neural disorders.

EEG study to HPT in patients with NS pathology using EEG database “EEGHUB.GE”

Nowadays Hyperventilation is a provocation tool used during the electroen-cephalogram (EEG) registration to detect epileptiform activity. Even in cases where there are no typical manifestations on EEG during hyperventilation, the analysis of the shows a variety of responses, changes, and combinations. EEG data of patients from “EEGHUB.GE” was used. Big Data EEG collected in Georgia have been and integrated and uploaded to the European Open Science Cloud (EOSC). The aim of study is determined the pattern to hyperventilation based on time of onset EEG and age of patients 2186 patients, 1139 females and 1047 males aged 3 to 51years was studded. In 1201 patients EEG reaction/response to hyperventilation was within the normal while those with pathological EEG reaction to hyperventilation (PERH) were 985. The patients were split into the following age groups: 3-6, 7-12, 13-18, 19-30, 31-50. Pathological EEG responses revealed of 3 types. During ongoing/current study pathological EEG-reactions to hyperventilation (I, II, and III) has been classified according to two parameters: time of manifestation (first, second, and third minutes) and age (3-6, 7-12, 13-18, 19-30, 31-50, 51and above) of the patient. The results have shown three types of pathological reactions of EEG and detected the time of manifestation – at the first, second and third minutes of hyperventilation: I type reaction on EEG 74.2% p<0001; II type reaction in 22.6% and III type reaction in the first minute – in 3.2% patients. The data of patients are valid by age and type of reaction due to the first type of reaction prevailed in all age categories p<0,002, χ2(10)x(10)2=28.2. The distribution of patients by pathological reaction to Hyperventilation by age: EEG to hyperventilation revealed in 853 patients (86.6%) p <0.0001; χ2(2)=689,791 in the first minute. in in 95 patients (9.6%) p<0.0001 at the second minutes. The prevailed type I reaction on EEG in 64, 2%. II type reaction was in 29. 4%. III type reaction 6.3% patients Types EEG Reaction to Hyperventilation concerning age is not reliable to third minute of Hyperventilation. Three types of EEG reaction to hyperventilation were revealed in all age-groups of patients with various CNS dysfunctions: In all age groups the disorganization of basic EEG rhythm in the first, second and third minutes of hyperventilation. In the first minute of hyperventilation between all types of EEG reaction and age (3-6, 7-12, 13-18, 19-30, 31-50) a significant ratio was revealed, which was not observed in the second and third minutes. EEG-response to hyperventilation based on ages of patients should be taken account for scientific and clinical study.

Automatic Seizure Detection Based on Stockwell Transform and Transformer.

Epilepsy is a chronic neurological disease associated with abnormal neuronal activity in the brain. Seizure detection algorithms are essential in reducing the workload of medical staff reviewing electroencephalogram (EEG) records. In this work, we propose a novel automatic epileptic EEG detection method based on Stockwell transform and Transformer. First, the S-transform is applied to the original EEG segments, acquiring accurate time-frequency representations. Subsequently, the obtained time-frequency matrices are grouped into different EEG rhythm blocks and compressed as vectors in these EEG sub-bands. After that, these feature vectors are fed into the Transformer network for feature selection and classification. Moreover, a series of post-processing methods were introduced to enhance the efficiency of the system. When evaluating the public CHB-MIT database, the proposed algorithm achieved an accuracy of 96.15%, a sensitivity of 96.11%, a specificity of 96.38%, a precision of 96.33%, and an area under the curve (AUC) of 0.98 in segment-based experiments, along with a sensitivity of 96.57%, a false detection rate of 0.38/h, and a delay of 20.62 s in event-based experiments. These outstanding results demonstrate the feasibility of implementing this seizure detection method in future clinical applications.

Interhemispheric asymmetry of the EEG rhythms coupling accompanies cognitive awakening during bimanual performance of a psychomotor test

Interhemispheric asymmetry of the EEG rhythms coupling accompanies cognitive awakening during bimanual performance of a psychomotor test

Effects of Virtual Reality Cognitive Training on Neuroplasticity: A Quasi-Randomized Clinical Trial in Patients with Stroke.

Cognitive Rehabilitation (CR) is a therapeutic approach designed to improve cognitive functioning after a brain injury, including stroke. Two major categories of techniques, namely traditional and advanced (including virtual reality-VR), are widely used in CR for patients with various neurological disorders. More objective outcome measures are needed to better investigate cognitive recovery after a stroke. In the last ten years, the application of electroencephalography (EEG) as a non-invasive and portable neuroimaging method has been explored to extract the hallmarks of neuroplasticity induced by VR rehabilitation approaches, particularly within the chronic stroke population. The aim of this study is to investigate the neurophysiological effects of CR conducted in a virtual environment using the VRRS device. Thirty patients with moderate-to-severe ischemic stroke in the chronic phase (at least 6 months after the event), with a mean age of 58.13 (±8.33) for the experimental group and 57.33 (±11.06) for the control group, were enrolled. They were divided into two groups: an experimental group and a control group, receiving neurocognitive stimulation using VR and the same amount of conventional neurorehabilitation, respectively. To study neuroplasticity changes after the training, we focused on the power band spectra of theta, alpha, and beta EEG rhythms in both groups. We observed that when VR technology was employed to amplify the effects of treatments on cognitive recovery, significant EEG-related neural improvements were detected in the primary motor circuit in terms of power spectral density and time-frequency domains. Indeed, EEG analysis suggested that VR resulted in a significant increase in both the alpha band power in the occipital areas and the beta band power in the frontal areas, while no significant variations were observed in the theta band power. Our data suggest the potential effectiveness of a VR-based rehabilitation approach in promoting neuroplastic changes even in the chronic phase of ischemic stroke.

Abnormalities in Occipital Cortical Sources of Resting‐State EEG Rhythms in Patients with Epileptiform Activity and Mild Cognitive Impairment due to Alzheimer’s Disease

AbstractBackgroundAlzheimer’s Disease (AD) patients present a higher risk of developing epilepsy compared to the age‐matched general population. Neurodegenerative cognitive disorders in general are closely linked with epilepsy and subclinical Epileptiform Electroencephalographic Activity (EEA). Furthermore, AD with Mild Cognitive Impairment (MCI) patients present abnormalities in posterior cortical sources of resting‐state eyes‐closed EEG rhythms at delta (< 4 Hz) and low‐frequency alpha (8‐10 Hz) rhythms. This retrospective study tested the hypothesis that ADMCI patients with EEA (ADMCI‐EEA) may be characterized by specific disease‐related abnormalities in delta and alpha rsEEG source activities compared with ADMCI patients without EEA (ADMCI‐noEEA) and noADMCI controls.MethodWe analysed data from an international database (www.pdwaves.org) comprising clinical, neuropsychological, rsEEG (25‐30 minutes), cerebrospinal fluid (CSF), and structural magnetic resonance imaging (sMRI) data in n = 42 ADMCI and n = 32 noADMCI patients without a diagnosis of epilepsy. The database also included clinical, neuropsychological, and rsEEG data of n = 38 matched cognitively unimpaired elderly (Nold) persons, used as additional controls in the present study. The rsEEG traces were reviewed by expert epileptologists blinded to the diagnostic status. EEA was detected in n = 8 ADMCI and n = 13 no‐ADMCI patients (Table 1). Individual alpha frequency peak (IAF) was used to determine the rsEEG delta, theta, and alpha bands. Regional rsEEG cortical sources were estimated using the eLORETA freeware.ResultCompared to the Nold and noADMCI‐noEEA groups, the noADMCI‐EEA group showed greater temporal rsEEG delta cortical source activity. Conversely, compared to the Nold and ADMCI‐noEEA groups, the ADMCI‐EEA group showed greater temporal and occipital rsEEG delta cortical source activity (Figure 1). Notably, CSF amyloid (but not sMRI‐based cortical atrophy) burden was greater in the ADMCI‐EEA than in ADMCI‐noEEA patients (Table 2).ConclusionThe abnormal temporal rsEEG delta cortical source activity in the ADMCI‐EEA and noADMCI‐EEA groups may reflect an unspecific susceptibility of the temporal lobe to slowing rsEEG activity for several neuropathological processes. Instead, the abnormal occipital rsEEG delta cortical source activity in the ADMCI‐EEA over the control groups may reflect a specific susceptibility of the occipital lobe to slowing rsEEG activity for AD‐related neuropathological processes in the cholinergic tract to the occipital cortex.

Mental fatigue assessment by an arbitrary channel EEG based on morphological features and LSTM-CNN

In order to achieve more sensitive mental fatigue assessment (MFA) based on an arbitrary channel EEG, this study proposed a series of feature extraction methods that combine mathematical morphology (MM), as well as an LSTM-CNN architecture. Firstly, 37 subjects had their resting-state EEGs collected at rested wakefulness (RW) and after 24 h of sleep deprivation (SD) using a 30-channel EEG acquisition device, the RW and SD groups were regarded as the negative and positive groups of mental fatigue, respectively, and the EEG collection were further categorized into two conditions: eye-opened state (EO) and eye-closed state (EC). Then, since MM can reflect the morphological characteristics of EEG rhythms and their potentials relatively independently of the time-frequency analysis and phase calculation, the MM methods were found to better reflect the mental fatigue after SD statistically, whether for single features (ANOVA: p<0.000001), multiple features (clustering by K-means, t-test: p<0.01), or time series feature spaces (calculating CD, t-test: p<0.01) of a single channel. Finally, the LSTM-CNN enhanced the generalization ability when dealing with different single-channel EEG by combining GRUs with convolutional layers: comparing the AUCs of different architectures for MFA based on an arbitrary channel, LSTM-CNN (0.992) > LSTM network (0.94) > CNN (0.831) > MLP (0.754). Moreover, the use of MM also improved the accuracy of analyzed architectures, and the true/false positive rate (TPR/FPR) of the LSTM-CNN architecture for MFA based on an arbitrary channel reached 97.024 %/3.497 %, which provided a feasible solution for the arbitrary channel EEG-based MFA.

Infra-slow fluctuations in cortical potentials and respiration drive fast cortical EEG rhythms in sleeping and waking states

ObjectiveInfra-slow fluctuations (ISF, 0.008–0.1 Hz) characterize hemodynamic and electric potential signals of human brain. ISFs correlate with the amplitude dynamics of fast (>1 Hz) neuronal oscillations, and may arise from permeability fluctuations of the blood–brain barrier (BBB). It is unclear if physiological rhythms like respiration drive or track fast cortical oscillations, and the role of sleep in this coupling is unknown. MethodsWe used high-density full-band electroencephalography (EEG) in healthy human volunteers (N = 21) to measure concurrently the ISFs, respiratory pulsations, and fast neuronal oscillations during periods of wakefulness and sleep, and to assess the strength and direction of their phase-amplitude coupling. ResultsThe phases of ISFs and respiration were both coupled with the amplitude of fast neuronal oscillations, with stronger ISF coupling being evident during sleep. Phases of ISF and respiration drove the amplitude dynamics of fast oscillations in sleeping and waking states, with different contributions. ConclusionsISFs in slow cortical potentials and respiration together significantly determine the dynamics of fast cortical oscillations. SignificanceWe propose that these slow physiological phases play a significant role in coordinating cortical excitability, which is a fundamental aspect of brain function.

Broadband Noise Exposure Psychoacoustic Effects Evaluation in Healthy Volunteers

The noise generated by life support systems is one of the factors that continuously affects the astronauts during their stay on the International Space Station. Long-term monitoring of the astronauts auditory system state demonstrates the possibility of developing both temporary and permanent (irreversible) shifts in hearing thresholds. In addition, it has been shown that prolonged noise exposure can adversely affect the quality of the operator’s activity and efficient performance. However, to date, the topic of psychoacoustic effects caused by noise exposure has not been studied enough. An experimental evaluation of the “non-auditory” effects of “white” noise exposure with an 85 dB intensity and 2 hours duration in healthy volunteers with normal hearing was carried out. The results indicate the negative noise impact on concentration, the amount of RAM and its use efficiency, the cognitive processes associated with the recognition and incoming information analysis speed. In addition, a significant change in the parameters of the volunteers bioelectrical brain activity was noted: an increase in the power of α- and β-rhythms and a decrease in the power of θ- EEG rhythms under the noise influence. Taking these data into account, the authors suggest a decrease in the operator’s activity quality of the volunteers in noise exposure conditions.

EEG synchronized left prefrontal transcranial magnetic stimulation (TMS) for treatment resistant depression is feasible and produces an entrainment dependent clinical response: A randomized controlled double blind clinical trial

BackgroundSynchronizing a TMS pulse with a person's underlying EEG rhythm can modify the brain's response. It is unclear if synchronizing rTMS trains might boost the antidepressant effect of TMS. In this first-in-human trial, we demonstrated that a single TMS pulse over the prefrontal cortex produces larger effects in the anterior cingulate depending on when it is fired relative to the individual's EEG alpha phase. Objective/hypothesesWe had three hypotheses. 1) It is feasible to synchronize repetitive TMS (rTMS) delivery to a person's preferred prefrontal alpha phase in each train of every session during a 30-visit TMS depression treatment course. 2) EEG-synchronized rTMS would produce progressive entrainment greater than unsynchronized (UNSYNC) rTMS. And 3) SYNC TMS would have better antidepressant effects than UNSYNC (remission, final Hamilton Depression Rating <10). MethodsWe enrolled (n = 34) and treated (n = 28) adults with treatment resistant depression (TRD) and randomized them to receive six weeks (30 treatments) of left prefrontal rTMS at their individual alpha frequency (IAF) (range 6–13 Hz). Prior to starting the clinical trial, all patients had an interleaved fMRI-EEG-TMS (fET) scan to determine which phase of their alpha rhythm would produce the largest BOLD response in their dorsal anterior cingulate. Our clinical EEG-rTMS system then delivered the first TMS pulse in each train time-locked to this patient-specific ‘preferred phase’ of each patient's left prefrontal alpha oscillation. We randomized patients (1:1) to SYNC or UNSYNC, and all were treated at their IAF. Only the SYNC patients had the first pulse of each train for all sessions synchronized to their individualized preferred alpha phase (75 trains/session ×30 sessions, 2250 synchronizations per patient over six weeks). The UNSYNC group used a random firing with respect to the alpha wave. All other TMS parameters were balanced between the two groups.The system interfaced with a MagStim Horizon air-cooled Fig. 8 TMS coil. All patients were treated at their IAF, coil in the F3 position, 120 % MT, frequency 6–13 Hz, 40 pulses per train, average 15-s inter-train interval, 3000 pulses per session. All patients, raters, and treaters were blinded. ResultsIn the intent to treat (ITT) sample, both groups had significant clinical improvement from baseline with no significant between-group differences, with the USYNC group having mathematically more remitters but fewer responders. (ITT -15 SYNC; 13 UNSYNC, response 5 (33 %), 1 (7 %), remission 2 (13 %), 6 (46 %). The same was true with the completer sample - 12 SYNC; 12 UNSYNC, response 4, 4 (both 30 %), remission 2 (17 %), 3 (25 %)). The clinical EEG phase synchronization system performed well with no failures. The average treatment session was approximately 90 min, with 30 min for placing the EEG cap and the actual TMS treatment for 45 min (which included gathering 10 min of resting EEG). Four subjects (1 SYNC) withdrew before six weeks of treatment. All 24 completer patients were treated for six weeks despite the trial occurring during the COVID pandemic. SYNC patients exhibited increased post-stimulation EEG entrainment over the six weeks. A detailed secondary analysis of entrainment data in the SYNC group showed that responders and non-responders in this group could be cleanly separated based on the total number of sessions with entrainment and the session-to-session precision of the entrained phase. For the SYNC group only, depression improvement was greater when more sessions were entrained at similar phases. ConclusionsSynchronizing prefrontal TMS with a patient's prefrontal alpha frequency in a blinded clinical trial is possible and produces progressive EEG entrainment in synchronized patients only. There was no difference in overall clinical response in this small clinical trial. A secondary analysis showed that the consistency of the entrained phase across sessions was significantly associated with response outcome only in the SYNC group. These effects may not simply be due to how the stimulation is delivered but also whether the patient's brain can reliably entrain to a precise phase. EEG-synchronized clinical delivery of TMS is feasible and requires further study to determine the best method for determining the phase for synchronization.

Effect of head-mounted virtual reality and vibrotactile feedback in ERD during motor imagery Brain–computer interface training

ABSTRACT Brain–computer interfaces (BCIs) can provide a non-muscular channel of control to stroke patients for motor rehabilitation. This can be achieved through the use of motor imagery (MI) training, involving the modulation of sensorimotor rhythms. The practice of MI has been shown to be able to strengthen key motor pathways when reinforced with rewarding feedback. Recently, there has been a growing evidence of the positive impact of embodied virtual reality (VR) and vibrotactile feedback in MI training. Nonetheless, it is not yet clear what the optimal MI-BCI setup is for evoking stronger sensorimotor rhythms in VR. In this study, we investigate the impact of head-mounted VR, and vibrotactile feedback during MI-BCI training in the induced sensorimotor rhythms. To achieve this, 19 healthy subjects performed MI training with embodied VR between four conditions: head-mounted vs. screen VR, with and without vibrotactile feedback; and two control conditions: abstract MI without embodied feedback, and motor execution. The event-related desynchronization (ERD) and the lateralization indices (LI) of the Alpha and Beta EEG rhythms were analyzed in a within-subject design. Results show that the combination of vibrotactile feedback and embodied VR can induce stronger and more lateralized Alpha ERD; nonetheless, LI was not significantly different across conditions.

Quantitative EEG analysis in typical absence seizures: unveiling spectral dynamics and entropy patterns.

A typical absence seizure is a generalized epileptic event characterized by a sudden, brief alteration of consciousness that serves as a hallmark for various generalized epilepsy syndromes. Distinguishing between similar interictal and ictal electroencephalographic (EEG) epileptiform patterns poses a challenge. However, quantitative EEG, particularly spectral analysis focused on EEG rhythms, shows potential for differentiation. This study was designed to investigate discernible differences in EEG spectral dynamics and entropy patterns during the pre-ictal and post-ictal periods compared to the interictal state. We analyzed 20 EEG ictal patterns from 11 patients with confirmed typical absence seizures, and assessed recordings made during the pre-ictal, post-ictal, and interictal intervals. Power spectral density (PSD) was used for the quantitative analysis that focused on the delta, theta, alpha, and beta bands. In addition, we measured EEG signal regularity using approximate (ApEn) and multi-scale sample entropy (MSE). Findings demonstrate a significant increase in delta and theta power in the pre-ictal and post-ictal intervals compared to the interictal interval, especially in the posterior brain region. We also observed a notable decrease in entropy in the pre-ictal and post-ictal intervals, with a more pronounced effect in anterior brain regions. These results provide valuable information that can potentially aid in differentiating epileptiform patterns in typical absence seizures. The implications of our findings are promising for precision medicine approaches to epilepsy diagnoses and patient management. In conclusion, our quantitative analysis of EEG data suggests that PSD and entropy measures hold promise as potential biomarkers for distinguishing ictal from interictal epileptiform patterns in patients with confirmed or suspected typical absence seizures.

An Improved Canonical Correlation Analysis for EEG Inter-Band Correlation Extraction.

(1) Background: Emotion recognition based on EEG signals is a rapidly growing and promising research field in affective computing. However, traditional methods have focused on single-channel features that reflect time-domain or frequency-domain information of the EEG, as well as bi-channel features that reveal channel-wise relationships across brain regions. Despite these efforts, the mechanism of mutual interactions between EEG rhythms under different emotional expressions remains largely unexplored. Currently, the primary form of information interaction between EEG rhythms is phase-amplitude coupling (PAC), which results in computational complexity and high computational cost. (2) Methods: To address this issue, we proposed a method of extracting inter-bands correlation (IBC) features via canonical correlation analysis (CCA) based on differential entropy (DE) features. This approach eliminates the need for surrogate testing and reduces computational complexity. (3) Results: Our experiments verified the effectiveness of IBC features through several tests, demonstrating that the more correlated features between EEG frequency bands contribute more to emotion classification accuracy. We then fused IBC features and traditional DE features at the decision level, which significantly improved the accuracy of emotion recognition on the SEED dataset and the local CUMULATE dataset compared to using a single feature alone. (4) Conclusions: These findings suggest that IBC features are a promising approach to promoting emotion recognition accuracy. By exploring the mutual interactions between EEG rhythms under different emotional expressions, our method can provide valuable insights into the underlying mechanisms of emotion processing and improve the performance of emotion recognition systems.

Technique for Measuring Electrical Activity of the Brain in Experiments on Laboratory Animals

This article presents a technique for experimental EEG analysis, which includes construction of a frequency spectrum trend, determination of the spectral power of signals in standard frequency ranges, followed by normalization by the maximum value in all channels and in all frequency ranges. This technique can be used to carry out a comparative analysis of data in any time interval, to evaluate the dynamics of changes in each of the main EEG rhythms, to identify the presence of a fronto-occipital gradient and interhemispheric asymmetry, and to evaluate the values of the integral index, which reflects the ratio of high- and lowfrequency rhythms.

Combination of EEG-Guided Adaptive Neurostimulation with Resonance Scanning in Correction of Stress-Induced States and Cognitive Rehabilitation of University Students.

The correction of stress-induced states and cognitive rehabilitation were carried out during the examination session in three university student groups comparable in the number, sex, and age. In the experimental group, a combination of EEG-guided adaptive neurostimulation with preliminary resonance scanning was used. In control group 1, only EEG-guided adaptive neurostimulation was used. In control group 2, musical-acoustic stimuli were presented without feedback from the subject's EEG. Experiments with preliminary resonance scanning revealed the maximum positive effects compared to the two control types of stimulation. A significant increase in the power of EEG rhythms, especially in the alpha range, was accompanied by a significant increase in subjective indicators of the functional state and cognitive activity. These results can be explained from the standpoint of the progressive involvement of the resonant, integrative, and neuroplasticity mechanisms of the brain into the processes of normalization the functional state of the body under the influence of combined stimulation procedures.