EEG Frequency Research Articles

Disentangling periodic and aperiodic resting EEG correlates of personality

Previous studies of resting electroencephalography (EEG) correlates of personality traits have conflated periodic and aperiodic sources of EEG signals. Because these are associated with different underlying neural dynamics, disentangling them can avoid measurement confounds and clarify findings. In a large sample (n = 300), we investigated how disentangling these activities impacts findings related to two research programs within personality neuroscience. In Study 1 we examined associations between Extraversion and two putative markers of reward sensitivity—Left Frontal Alpha asymmetry (LFA) and Frontal-Posterior Theta (FPT). In Study 2 we used machine learning to predict personality trait scores from resting EEG. In both studies, power within each EEG frequency bin was quantified as both total power and separate contributions of periodic and aperiodic activity. In Study 1, total power LFA and FPT correlated negatively with Extraversion (r ∼ -0.14), but there was no relation when LFA and FPT were derived only from periodic activity. In Study 2, all Big Five traits could be decoded from periodic power (r ∼ 0.20), and Agreeableness could also be decoded from total power and from aperiodic indices. Taken together, these results show how separation of periodic and aperiodic activity in resting EEG may clarify findings in personality neuroscience. Disentangling these signals allows for more reliable findings relating to periodic EEG markers of personality, and highlights novel aperiodic markers to be explored in future research.

Precise prediction of cerebrospinal fluid amyloid beta protein for early Alzheimer's disease detection using multimodal data.

Alzheimer's disease (AD) constitutes a neurodegenerative disorder marked by a progressive decline in cognitive function and memory capacity. The accurate diagnosis of this condition predominantly relies on cerebrospinal fluid (CSF) markers, notwithstanding the associated burdens of pain and substantial financial costs endured by patients. This study encompasses subjects exhibiting varying degrees of cognitive impairment, encompassing individuals with subjective cognitive decline, mild cognitive impairment, and dementia, constituting a total sample size of 82 participants. The primary objective of this investigation is to explore the relationships among brain atrophy measurements derived from magnetic resonance imaging, atypical electroencephalography (EEG) patterns, behavioral assessment scales, and amyloid β-protein (Aβ) indicators. The findings of this research reveal that individuals displaying reduced Aβ1-42/Aβ-40 levels exhibit significant atrophy in the frontotemporal lobe, alongside irregularities in various parameters related to EEG frequency characteristics, signal complexity, inter-regional information exchange, and microstates. The study additionally endeavors to estimate Aβ1-42/Aβ-40 content through the application of a random forest algorithm, amalgamating structural data, electrophysiological features, and clinical scales, achieving a remarkable predictive precision of 91.6%. In summary, this study proposes a cost-effective methodology for acquiring CSF markers, thereby offering a valuable tool for the early detection of AD.

Functional Connectivity Alterations in Patients with Post-stroke Epilepsy Based on Source-level EEG and Graph Theory.

We investigated the differences in functional connectivity based on the source-level electroencephalography (EEG) analysis between stroke patients with and without post-stroke epilepsy (PSE). Thirty stroke patients with PSE and 35 stroke patients without PSE were enrolled. EEG was conducted during a resting state period. We used a Brainstorm program for source estimation and the connectivity matrix. Data were processed according to EEG frequency bands. We used a BRAPH program to apply a graph theoretical analysis. In the beta band, radius and diameter were increased in patients with PSE than in those without PSE (2.699 vs. 2.579, adjusted p = 0.03; 2.261 vs. 2.171, adjusted p = 0.03). In the low gamma band, radius was increased in patients with PSE than in those without PSE (2.808 vs. 2.617, adjusted p = 0.03). In the high gamma band, the radius, diameter, average eccentricity, and characteristic path length were increased (1.828 vs. 1.559, adjusted p < 0.01; 2.653 vs. 2.306, adjusted p = 0.01; 2.212 vs. 1.913, adjusted p < 0.01; 1.425 vs. 1.286, adjusted p = 0.01), whereas average strength, mean clustering coefficient, and transitivity were decreased in patients with PSE than in those without PSE (49.955 vs. 55.055, adjusted p < 0.01; 0.727 vs. 0.810, adjusted p < 0.01; 1.091 vs. 1.215, adjusted p < 0.01). However, in the delta, theta, and alpha bands, none of the functional connectivity measures were different between groups. We demonstrated significant alterations of functional connectivity in patients with PSE, who have decreased segregation and integration in brain network, compared to those without PSE.

Compensatory brain activity pattern is not present in older adults during the n-back task performance-Findings based on EEG frequency analysis.

Cognitive ability is one of the most important enablers for successful aging. At the same time, cognitive decline is a well-documented phenomenon accompanying the aging process. Nevertheless, it is acknowledged that aging can also be related to positive processes that allow one to compensate for the decline. These processes include the compensatory brain activity of older adults primarily investigated using fMRI and PET. To strengthen the cognitive interpretation of compensatory brain activity in older adults, we searched for its indicators in brain activity measured by EEG. The study sample comprised 110 volunteers, including 50 older adults (60-75 years old) and 60 young adults (20-35 years old) who performed 1-back, 2-back, and 3-back tasks while recording the EEG signal. The study analyzed (1) the level of cognitive performance, including sensitivity index, the percentage of correct answers to the target, and the percentage of false alarm errors; (2) theta and alpha power for electrodes located in the frontal-midline (Fz, AF3, AF4, F3, F4, FC1, and FC2) and the centro-parietal (CP1, CP2, P3, P4, and Pz) areas. Cognitive performance was worse in older adults than in young adults, which manifested in a significantly lower sensitivity index and a significantly higher false alarm error rate at all levels of the n-back task difficulty. Simultaneously, performance worsened with increasing task difficulty regardless of age. Significantly lower theta power in the older participants was observed at all difficulty levels, even at the lowest one, where compensatory activity was expected. At the same time, at this difficulty level, cognitive performance was worse in older adults than in young adults, which could reduce the chances of observing compensatory brain activity. The significant decrease in theta power observed in both age groups with rising task difficulty can reflect a declining capacity for efficient cognitive functioning under increasing demands rather than adapting to this increase. Moreover, in young adults, alpha power decreased to some extent with increasing cognitive demand, reflecting adaptation to them, while in older adults, no analogous pattern was observed. In conclusion, based on the results of the current study, the presence of compensatory activity in older adults cannot be inferred.

Scented Solutions: Examining the Efficacy of Scent Interventions in Mitigating Driving Fatigue.

Fatigued driving threatens the safety of people's lives and property. Scent countermeasures offer minimal disruption and high efficacy, making them a promising approach. The aim of this study was to explore the application of scent countermeasures in alleviating fatigued driving. This study explored changes in EEG frequency bands (alpha, beta, theta, and gamma) and the activity of EEG metrics (R(α/β), Rθ/(α+β) and R(α+θ)/(α+β)) in the temporal lobe during driving tasks, selected fatigued driving identifiers, and aided validation by investigating subjective fatigue with the Karolinska Sleepiness Scale (KSS). The EEG indicators all increased, with a significant increase in R(α/β). R(α/β) was combined with the KSS to explore the effects of three scents, peppermint, grapefruit, and lavender, on driving fatigue. The subjective questionnaire results indicated that all three scents significantly improved driving fatigue, with significantly lower levels of driving fatigue compared to the control group. The analysis of EEG signals revealed a significant decrease in R(α/β) after the implementation of scent countermeasures. Moreover, R(α/β) was found to be lower in all three odor intervention groups compared to the control group. All three scents were found to significantly alleviate driving fatigue. The grapefruit scent had a better timely effect in relieving driving fatigue and the lavender scent had a longer effectiveness. This study provides further exploration for the application of odor interventions to alleviate driving fatigue. This study provides a practical reference for drivers to use odors to avoid fatigue in order to improve road safety.

Electroencephalogram monitoring during ketamine antidepressant treatment: a pilot study

Introduction Depression is a major cause of disability world-wide. Up to a third of patients have a treatment-resistant form (TRD), presenting a major challenge. Ketamine has been introduced as a novel rapid-acting antidepressant effective in this population. However, at present, ketamine treatment is not routinely informed by any objective neural markers. Basic research has shown promising electroencephalogram (EEG) changes including a decrease in alpha power. However, clinical translation is lacking.ObjectivesAssess the feasibility of identifying EEG correlates of ketamine infusions in a routine outpatient setting with a low-cost, easily usable system.MethodsThe study was carried out at the Oxford Health Foundation Trust Ketamine Clinic (ethics reference 22/EM/0226). N=18 EEG recordings from N=12 patients were collected (5 women, mean age 44, range 33-62, IV dose 0.5-1mg/kg over 40min). 4-channel EEG was collected with a Muse-S headband at 256Hz, from 5min before to 55min after infusion start. 5s epochs were rejected if gyroscope data indicated head movement above 10 deg/s or if amplitude was above 200μV. A spectrogram (4s window, 3s overlap) as well as band-limited power (theta: 4-8Hz, alpha: 8-13Hz, beta: 13-25Hz) were computed. Significance of changes was found with a repeated measures analysis of variance (RM-ANOVA) on power in 5min segments together with post-hoc Tukey’s P-values.ResultsAcross the ketamine infusion recordings, there was a significant effect of time (F=3.65, P=0.0105) and Channel*Time interaction (F=3.80, P&lt;0.001) on the EEG spectrum. Effects were largest on temporal electrodes, particularly TP9 in the alpha and theta bands (Figure 1, Table 1).Table 1:Effect sizes (Cohen’s d) and FDR-corrected ANOVA P-values for ketamine effects on each EEG channel and frequency band. P&lt;0.05 was considered significant (bold). n.s. = not significant (P&gt;0.2). Channel / Band TP9AF7AF8TP10Theta1.16 (P=0.019)0.11 (n.s.)0.11 (n.s.)0.42 (P=0.113)Alpha1.41 (P&lt;0.001)0.12 (n.s.)0.17 (n.s.)0.605 (P=0.113)Beta1.19 (P=0.112)0.03 (n.s.)0.08 (n.s.)0.21 (n.s.)Image:ConclusionsIn a routine outpatient setting, sub-anaesthetic ketamine infusions in TRD patients were associated with decreased fronto-temporal EEG alpha and theta power. Future work should assess the potential of low-cost routine EEG, and alpha desaturation specifically, to inform individualised ketamine treatment.Disclosure of InterestNone Declared

Depression assessment using integrated multi-featured EEG bands deep neural network models: Leveraging ensemble learning techniques

Mental Status Assessment (MSA) holds significant importance in psychiatry. In recent years, several studies have leveraged Electroencephalogram (EEG) technology to gauge an individual's mental state or level of depression. This study introduces a novel multi-tier ensemble learning approach to integrate multiple EEG bands for conducting mental state or depression assessments. Initially, the EEG signal is divided into eight sub-bands, and then a Long Short-Term Memory (LSTM)-based Deep Neural Network (DNN) model is trained for each band. Subsequently, the integration of multi-band EEG frequency models and the evaluation of mental state or depression level are facilitated through a two-tier ensemble learning approach based on Multiple Linear Regression (MLR). The authors conducted numerous experiments to validate the performance of the proposed method under different evaluation metrics. For clarity and conciseness, the research employs the simplest commercialized one-channel EEG sensor, positioned at FP1, to collect data from 57 subjects (49 depressed and 18 healthy subjects). The obtained results, including an accuracy of 0.897, F1-score of 0.921, precision of 0.935, negative predictive value of 0.829, recall of 0.908, specificity of 0.875, and AUC of 0.8917, provide evidence of the superior performance of the proposed method compared to other ensemble learning techniques. This method not only proves effective but also holds the potential to significantly enhance the accuracy of depression assessment.

Transient Seizure Clusters and Epileptiform Activity Following Widespread Bilateral Hippocampal Interneuron Ablation.

Interneuron loss is a prominent feature of temporal lobe epilepsy in both animals and humans and is hypothesized to be critical for epileptogenesis. As loss occurs concurrently with numerous other potentially proepileptogenic changes, however, the impact of interneuron loss in isolation remains unclear. For the present study, we developed an intersectional genetic approach to induce bilateral diphtheria toxin-mediated deletion of Vgat-expressing interneurons from dorsal and ventral hippocampus. In a separate group of mice, the same population was targeted for transient neuronal silencing with DREADDs. Interneuron ablation produced dramatic seizure clusters and persistent epileptiform activity. Surprisingly, after 1 week seizure activity declined precipitously and persistent epileptiform activity disappeared. Occasional seizures (≈1/day) persisted to the end of the experiment at 4 weeks. In contrast to the dramatic impact of interneuron ablation, transient silencing produced large numbers of interictal spikes, a significant but modest increase in seizure occurrence and changes in EEG frequency band power. Taken together, findings suggest that the hippocampus regains relative homeostasis-with occasional breakthrough seizures-in the face of an extensive and abrupt loss of interneurons.

Auditory mismatch negativity in pre-manifest and manifest Huntington’s disease

AimThe aim of this study was to investigate the characteristics of the electrophysiological brain response elicited in a passive acoustic oddball paradigm, i.e. mismatch negativity (MMN), in patients with Huntington’s disease (HD) in the premanifest (pHD) and manifest (mHD) phases. In this regard, we correlated the results of event-related potentials (ERP) with disease characteristics. MethodsThis was an observational cross-sectional MMN study. In addition to the MMN recording of the passive oddball task, all subjects with first-degree inheritance for HD underwent genetic testing for mutant HTT, the Huntington’s Disease Rating Scale, the Total Functional Capacity Scale, the Problem Behaviors Assessment short form, and the Mini-Mental State Examination. ResultsWe found that global field power (GFP) was reduced in the MMN time window in mHD patients compared to pHD and normal controls (NC). In the pHD group, MMN amplitude was only slightly and not significantly increased compared to mHD, while pHD patients showed increased theta coherence between trials compared to mHD. In the entire sample of HD gene carriers, the main MMN traits were not correlated with motor performance, cognitive impairment and functional disability. ConclusionThese results suggest an initial and subtle deterioration of pre-attentive mechanisms in the presymptomatic phase of HD, with an increasing phase shift in the MMN time frame. This result could indicate initial functional changes with a possible compensatory effect. SignificanceAn initial and slight decrease in MMN associated with increased phase coherence in the corresponding EEG frequencies could indicate an early functional involvement of pre-attentive resources that could precede the clinical expression of HD.

EEG band power and phase‐amplitude coupling in patients with Dravet syndrome

AbstractObjectiveDravet syndrome (DS) is an epileptic encephalopathy caused by haploinsufficiency of the SCN1A gene. SCN1A gene deficiency limits the firing rates of fast‐spiking inhibitory interneurons, which should reflect in abnormal aggregate network oscillatory electroencephalography (EEG) activity that can be measured by spectral power and phase‐amplitude coupling (PAC) analysis. In this retrospective pilot study, we tested whether spectral EEG frequency band power and PAC metrics distinguish children with DS from age‐matched controls, an early step toward establishing EEG markers of target engagement by gene or drug therapy.MethodsEEG data were collected from patients with DS (N = 6) and age‐matched control pediatric participants (N = 11) and analyzed for cumulative spectral power and PAC and classification capacity of these metrics, by logistic regression analysis. For this initial spectral and PAC analysis, we focused on sleep EEG, where myogenic artifact is minimal and where δ–γ and θ–γ coupling is otherwise expected to be robust.ResultsCumulative δ (1– &lt;4 Hz) and θ (4–7 Hz) power was significantly reduced in the DS group, compared with age‐matched controls (p = 0.001 and p = 0.02, respectively). The δ power was a stronger classifier of separating DS from controls than θ power, with 87% and 83% accuracy, respectively. The γ power trended toward significant reduction (p = 0.08) in the DS group. We found significantly lower PAC between 1–2 Hz phase and 63–80 Hz amplitude in patients with DS compared with the age‐matched controls (p = 0.003), with 78% classification accuracy between groups for PAC.InterpretationIn this pilot study assessing EEG patterns during sleep, we found lower δ–θ power and PAC in patients with DS versus controls, which may reflect abnormal aggregate macroscale network communication patterns resulting from SCN1A deficiency. These measures may be useful metrics of therapeutic target engagement, particularly if the therapy restores the underlying DS pathophysiology. The sorting capacity of these metrics distinguished patients with DS from patients without DS and may in turn facilitate near‐future development of disease and therapy target engagement biomarkers in this syndrome.

No Frequency-Specific Effect of Transcranial Random Noise Stimulation on Resting EEG.

Transcranial random noise stimulation (tRNS) is a form of noninvasive transcranial electrical stimulation that applies alternating current in various randomized frequencies to the cortex, thereby improving cognitive functioning in multiple domains. However, the precise mechanism of tRNS, as well as its impact on human electroencephalography (EEG), remains unclear. This is partly because most studies have used tRNS in conjunction with a cognitive task, making it difficult to tease apart whether the observed changes in EEG are a result of tRNS, the cognitive task, or their interaction. Forty-nine healthy individuals participated in this study and were randomly assigned to active tRNS (n = 24) and sham (n = 25) groups. tRNS was delivered for 20 minutes over Fp1/Fp2 and Oz. Resting-state EEG data were collected before and after either tRNS or sham stimulation. Cluster-based permutation tests using FieldTrip revealed no frequency-specific effect of tRNS on resting-state EEG data across four frequency bands (theta, alpha, beta, gamma). These observations suggest that tRNS itself does not target or alter specific EEG frequencies. Rather, tRNS most likely interacts with the cognitive task/activity at hand to produce an observable difference in post-tRNS EEG. Positive tRNS-EEG findings from previous studies are also likely to have resulted from the interactive and cognitive activity-dependent nature of tRNS.

Do Miniature Eye Movements Affect Neurofeedback Training Performance? A Combined EEG-Eye Tracking Study

EEG-based neurofeedback is a prominent method to modulate one’s own brain activity in a desired direction. However, the EEG signal can be disturbed by artifacts, e.g., eye movements, which can consequently confound the neurofeedback performance. Involuntary miniature eye movements can be hardly detected by conventional EEG correction methods such as recording the electro-oculogram (EOG) and subtracting EOG activity from the EEG signal. However, such miniature eye movements can influence EEG activity, especially in the Gamma frequency range, enormously. In the present study, we investigated whether power in different EEG frequencies can be effectively modulated by self-control of brain signals during neurofeedback training and/or whether changes in EEG power are provoked by miniature eye movements during the training. To this end, 24 participants performed one session of SMR and one session of Gamma neurofeedback training. Additionally, in each training session sham feedback was performed. An eye tracker was used to detect miniature eye movements (< 1°) during neurofeedback training. About two thirds of the participants were able to increase their SMR power over the course of NF training, while one third was able to increase Gamma power. Generally, miniature eye movements induced a strong Gamma power increase. The number of eye movements also increased numerically over the course of the NF training. However, we did not find a significant relationship with the NF training performance. This is a first indication that miniature saccades do not affect NF training performance, but should not be neglected during NF training. Our results have to be confirmed in future studies.

EEG rhythm separation and time-frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI.

Motor imagery electroencephalogram (EEG) is widely employed in brain-computer interface (BCI) systems. As a time-frequency analysis method for nonlinear and non-stationary signals, multivariate empirical mode decomposition (MEMD) and its noise-assisted version (NA-MEMD) has been widely used in the preprocessing step of BCI systems for separating EEG rhythms corresponding to specific brain activities. However, when applied to multichannel EEG signals, MEMD or NA-MEMD often demonstrate low robustness to noise and high computational complexity. To address these issues, we have explored the advantages of our recently proposed fast multivariate empirical mode decomposition (FMEMD) and its noise-assisted version (NA-FMEMD) for analyzing motor imagery data. We emphasize that FMEMD enables a more accurate estimation of EEG frequency information and exhibits a more noise-robust decomposition performance with improved computational efficiency. Comparative analysis with MEMD on simulation data and real-world EEG validates the above assertions. The joint average frequency measure is employed to automatically select intrinsic mode functions that correspond to specific frequency bands. Thus, FMEMD-based classification architecture is proposed. Using FMEMD as a preprocessing algorithm instead of MEMD can improve the classification accuracy by 2.3% on the BCI Competition IV dataset. On the Physiobank Motor/Mental Imagery dataset and BCI Competition IV Dataset 2a, FMEMD-based architecture also attained a comparable performance to complex algorithms. The results indicate that FMEMD proficiently extracts feature information from small benchmark datasets while mitigating dimensionality constraints resulting from computational complexity. Hence, FMEMD or NA-FMEMD can be a powerful time-frequency preprocessing method for BCI.

Subject-Independent Emotion Recognition Based on EEG Frequency Band Features and Self-Adaptive Graph Construction.

Emotion is one of the most important higher cognitive functions of the human brain and plays an important role in transaction processing and decisions. In traditional emotion recognition studies, the frequency band features in EEG signals have been shown to have a high correlation with emotion production. However, traditional emotion recognition methods cannot satisfactorily solve the problem of individual differences in subjects and data heterogeneity in EEG, and subject-independent emotion recognition based on EEG signals has attracted extensive attention from researchers. In this paper, we propose a subject-independent emotion recognition model based on adaptive extraction of layer structure based on frequency bands (BFE-Net), which is adaptive in extracting EEG map features through the multi-graphic layer construction module to obtain a frequency band-based multi-graphic layer emotion representation. To evaluate the performance of the model in subject-independent emotion recognition studies, extensive experiments are conducted on two public datasets including SEED and SEED-IV. The experimental results show that in most experimental settings, our model has a more advanced performance than the existing studies of the same type. In addition, the visualization of brain connectivity patterns reveals that some of the findings are consistent with previous neuroscientific validations, further validating the model in subject-independent emotion recognition studies.

Sleep Physiology and Neurocognition Among Adolescents With Attention-Deficit/Hyperactivity Disorder

Few studies have characterized the nature of sleep problems among adolescents with attention-deficit/hyperactivity disorder (ADHD) using polysomnography (PSG). Additionally, although adolescents with ADHD and adolescents with sleep disturbances display similar neurocognitive deficits, the role of sleep in contributing to neurocognitive impairment in adolescent ADHD is unknown. This study investigated differences in PSG-measured sleep among adolescents with ADHD compared with non-psychiatric controls and associations with neurocognition. Medication-free adolescents aged 13 to 17 (N = 62, n= 31 with ADHD; mean age= 15.3 years; 50% female) completed a diagnostic evaluation, 3 nights of ambulatory PSG, the Cambridge Neuropsychological Test Automated Battery, and subjective reports of sleep and executive functioning. Linear regressions covarying for age, sex, and pubertal status examined group differences in sleep indices, and partial Pearson correlations assessed relations between sleep and neurocognition. Although adolescents with ADHD did not exhibit differences in PSG-measured sleep duration, awakenings, or latency (ps > .05) compared with non-psychiatric controls, they displayed lower slow wave sleep percentage (β=-.40) and non-rapid eye movement (NREM) electroencephalogram (EEG) delta power (β=-.29). They also exhibited greater stage 2 percentage (β= .41), NREM EEG sigma power (β= .41), and elevated self-reported sleep disturbances (ps< .05). Lower NREM EEG delta power, increased high-frequency power, and slower decline in NREM EEG delta power overnight were associated with poorer neurocognition among adolescents with ADHD. Adolescents with ADHD reported more sleep disturbances than non-psychiatric controls and exhibited differences in sleep stage distribution and NREM sleep EEG frequency. Sleep-EEG spectral indices were associated with impaired neurocognition, suggesting that physiological sleep processes may underlie neurocognitive deficits in ADHD. Future studies may clarify whether sleep plays a causal role in neurocognitive impairments in adolescent ADHD and whether interventions normalizing sleep improve neurocognition. Sleep Dysfunction and Neurocognitive Outcomes in Adolescent ADHD; https://clinicaltrials.gov/; NCT02897362. We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. We actively worked to promote sex and gender balance in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list.

Improving Mental Skills in Precision Sports by Using Neurofeedback Training: A Narrative Review.

Primary objectives of neurofeedback training (NFT) are to improve concentration, stress and anxiety management, and performance optimisation. This narrative review examines the role of NFT as a tool to improve mental and cognitive skills of target shooting and archery athletes. Current research discusses how neurofeedback training can act on brain waves by influencing specific EEG frequency bands in order to improve cognitive flexibility. This contribution reports studies that have applied neurofeedback protocols in precision disciplines such as archery and shooting. The results of the studies considered showed that neurofeedback can lead to faster reaction times, more sustained attention, and better emotion management, contributing significantly to athletes' performance. Furthermore, it is emphasised that neurofeedback could be combined with other techniques such as motor imagination to maximise effectiveness in precision sports training. This review emphasises the importance of future studies that focus on the integration of neurofeedback with biofeedback in neuromodulation protocols. Current perspectives and limitations of research in this area are also indicated. Neuromodulation by means of neurofeedback represents a promising strategy to improve the overall mental and cognitive abilities of target shooting and archery athletes with an interesting potential for high-level performance. Future research should focus on integrated approaches and customised protocols to optimise the use of neurofeedback in a precision sports context.

Dysmaturation of sleep state and electroencephalographic activity after hypoxia-ischaemia in preterm fetal sheep

Antenatal hypoxia-ischaemia (HI) in preterm fetal sheep can trigger delayed evolution of severe, cystic white matter injury (WMI), in a similar timecourse to WMI in preterm infants. We therefore examined how severe hypoxia-ischaemia affects recovery of electroencephalographic (EEG) activity. Chronically instrumented preterm fetal sheep (0.7 gestation) received 25 min of complete umbilical cord occlusion (UCO, n = 9) or sham occlusion (controls, n = 9), and recovered for 21 days. HI was associated with a shift to lower frequency EEG activity for the first 5 days with persisting loss of EEG power in the delta and theta bands, and initial loss of power in the alpha and beta bands in the first 14 days of recovery. In the final 3 days of recovery, there was a marked rhythmic shift towards higher frequency EEG activity after UCO. The UCO group spent less time in high-voltage sleep, and in the early evening (7:02 pm ± 47 min) abruptly stopped cycling between sleep states, with a shift to a high frequency state for 2 h 48 min ± 40 min, with tonic electromyographic activity. These findings demonstrate persisting EEG and sleep state dysmaturation after severe hypoxia-ischaemia. Loss of fetal or neonatal sleep state cycling in the early evening may be a useful biomarker for evolving cystic WMI.

L-serine treatment in patients with GRIN-related encephalopathy: a phase 2A, non-randomized study.

GRIN-related disorders are rare developmental encephalopathies with variable manifestations and limited therapeutic options. Here, we present the first non-randomized, open-label, single-arm trial (NCT04646447) designed to evaluate the tolerability and efficacy of L-serine in children with GRIN genetic variants leading to loss-of-function. In this phase 2A trial, patients aged 2-18 years with GRIN loss-of-function pathogenic variants received L-serine for 52 weeks. Primary end points included safety and efficacy by measuring changes in the Vineland Adaptive Behavior Scales, Bayley Scales, age-appropriate Wechsler Scales, Gross Motor Function-88, Sleep Disturbance Scale for Children, Pediatric Quality of Life Inventory, Child Behavior Checklist and the Caregiver-Teacher Report Form following 12 months of treatment. Secondary outcomes included seizure frequency and intensity reduction and EEG improvement. Assessments were performed 3 months and 1 day before starting treatment and 1, 3, 6 and 12 months after beginning the supplement. Twenty-four participants were enrolled (13 males/11 females, mean age 9.8 years, SD 4.8), 23 of whom completed the study. Patients had GRIN2B, GRIN1 and GRIN2A variants (12, 6 and 5 cases, respectively). Their clinical phenotypes showed 91% had intellectual disability (61% severe), 83% had behavioural problems, 78% had movement disorders and 58% had epilepsy. Based on the Vineland Adaptive Behavior Composite standard scores, nine children were classified as mildly impaired (cut-off score > 55), whereas 14 were assigned to the clinically severe group. An improvement was detected in the Daily Living Skills domain (P = 0035) from the Vineland Scales within the mild group. Expressive (P = 0.005), Personal (P = 0.003), Community (P = 0.009), Interpersonal (P = 0.005) and Fine Motor (P = 0.031) subdomains improved for the whole cohort, although improvement was mostly found in the mild group. The Growth Scale Values in the Cognitive subdomain of the Bayley-III Scale showed a significant improvement in the severe group (P = 0.016), with a mean increase of 21.6 points. L-serine treatment was associated with significant improvement in the median Gross Motor Function-88 total score (P = 0.002) and the mean Pediatric Quality of Life total score (P = 0.00068), regardless of severity. L-serine normalized the EEG pattern in five children and the frequency of seizures in one clinically affected child. One patient discontinued treatment due to irritability and insomnia. The trial provides evidence that L-serine is a safe treatment for children with GRIN loss-of-function variants, having the potential to improve adaptive behaviour, motor function and quality of life, with a better response to the treatment in mild phenotypes.

Enhanced performance of EEG-based brain-computer interfaces by joint sample and feature importance assessment.

Electroencephalograph (EEG) has been a reliable data source for building brain-computer interface (BCI) systems; however, it is not reasonable to use the feature vector extracted from multiple EEG channels and frequency bands to perform recognition directly due to the two deficiencies. One is that EEG data is weak and non-stationary, which easily causes different EEG samples to have different quality. The other is that different feature dimensions corresponding to different brain regions and frequency bands have different correlations to a certain mental task, which is not sufficiently investigated. To this end, a Joint Sample and Feature importance Assessment (JSFA) model was proposed to simultaneously explore the different impacts of EEG samples and features in mental state recognition, in which the former is based on the self-paced learning technique while the latter is completed by the feature self-weighting technique. The efficacy of JSFA is extensively evaluated on two EEG data sets, i.e., SEED-IV and SEED-VIG. One is a classification task for emotion recognition and the other is a regression task for driving fatigue detection. Experimental results demonstrate that JSFA can effectively identify the importance of different EEG samples and features, leading to enhanced recognition performance of corresponding BCI systems.

An Autonomous Sleep-Stage Detection Technique in Disruptive Technology Environment.

Autonomous sleep tracking at home has become inevitable in today's fast-paced world. A crucial aspect of addressing sleep-related issues involves accurately classifying sleep stages. This paper introduces a novel approach PSO-XGBoost, combining particle swarm optimisation (PSO) with extreme gradient boosting (XGBoost) to enhance the XGBoost model's performance. Our model achieves improved overall accuracy and faster convergence by leveraging PSO to fine-tune hyperparameters. Our proposed model utilises features extracted from EEG signals, spanning time, frequency, and time-frequency domains. We employed the Pz-oz signal dataset from the sleep-EDF expanded repository for experimentation. Our model achieves impressive metrics through stratified-K-fold validation on ten selected subjects: 95.4% accuracy, 95.4% F1-score, 95.4% precision, and 94.3% recall. The experiment results demonstrate the effectiveness of our technique, showcasing an average accuracy of 95%, outperforming traditional machine learning classifications. The findings revealed that the feature-shifting approach supplements the classification outcome by 3 to 4 per cent. Moreover, our findings suggest that prefrontal EEG derivations are ideal options and could open up exciting possibilities for using wearable EEG devices in sleep monitoring. The ease of obtaining EEG signals with dry electrodes on the forehead enhances the feasibility of this application. Furthermore, the proposed method demonstrates computational efficiency and holds significant value for real-time sleep classification applications.