Brain Signals Research Articles

Leveraging Distributed Brain Signal at Rest to Predict Internalizing Symptoms in Youth: Deriving a Polyneuro Risk Score From the ABCD Study Cohort

BackgroundThe prevalence of internalizing psychopathology rises precipitously from early to mid-adolescence, yet the underlying neural phenotypes that give rise to depression and anxiety during this developmental period remain unclear. MethodsYouths from the Adolescent Brain Cognitive Development (ABCD) Study (ages 9–10 years at baseline) with a resting-state functional magnetic resonance imaging scan and mental health data were eligible for inclusion. Internalizing subscale scores from the Brief Problem Monitor-Youth Form were combined across 2 years of follow-up to generate a cumulative measure of internalizing symptoms. The total sample (N = 6521) was split into a large discovery dataset and a smaller validation dataset. Brain-behavior associations of resting-state functional connectivity with internalizing symptoms were estimated in the discovery dataset. The weighted contributions of each functional connection were aggregated using multivariate statistics to generate a polyneuro risk score (PNRS). The predictive power of the PNRS was evaluated in the validation dataset. ResultsThe PNRS explained 10.73% of the observed variance in internalizing symptom scores in the validation dataset. Model performance peaked when the top 2% functional connections identified in the discovery dataset (ranked by absolute β weight) were retained. The resting-state functional connectivity networks that were implicated most prominently were the default mode, dorsal attention, and cingulo-parietal networks. These findings were significant (p < 1 × 10−6) as accounted for by permutation testing (n = 7000). ConclusionsThese results suggest that the neural phenotype associated with internalizing symptoms during adolescence is functionally distributed. The PNRS approach is a novel method for capturing relationships between resting-state functional connectivity and behavior.

DGSD: Dynamical graph self-distillation for EEG-based auditory spatial attention detection

Auditory Attention Detection (AAD) aims to detect the target speaker from brain signals in a multi-speaker environment. Although EEG-based AAD methods have shown promising results in recent years, current approaches primarily rely on traditional convolutional neural networks designed for processing Euclidean data like images. This makes it challenging to handle EEG signals, which possess non-Euclidean characteristics. In order to address this problem, this paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input. Specifically, to effectively represent the non-Euclidean properties of EEG signals, dynamical graph convolutional networks are applied to represent the graph structure of EEG signals, which can also extract crucial features related to auditory spatial attention in EEG signals. In addition, to further improve AAD detection performance, self-distillation, consisting of feature distillation and hierarchical distillation strategies at each layer, is integrated. These strategies leverage features and classification results from the deepest network layers to guide the learning of shallow layers. Our experiments are conducted on two publicly available datasets, KUL and DTU. Under a 1-second time window, we achieve results of 90.0% and 79.6% accuracy on KUL and DTU, respectively. We compare our DGSD method with competitive baselines, and the experimental results indicate that the detection performance of our proposed DGSD method is not only superior to the best reproducible baseline but also significantly reduces the number of trainable parameters by approximately 100 times.

Macroscopic brain dynamics beyond contralateral primary motor cortex for movement prediction

This study investigates the complex relationship between upper limb movement direction and macroscopic neural signals in the brain, which is critical for understanding brain-computer interfaces (BCI). Conventional BCI research has primarily focused on a local area, such as the contralateral primary motor cortex (M1), relying on the population-based decoding method with microelectrode arrays. In contrast, macroscopic approaches such as electroencephalography (EEG) and magnetoencephalography (MEG) utilize numerous electrodes to cover broader brain regions. This study probes the potential differences in the mechanisms of microscopic and macroscopic methods. It is important to determine which neural activities effectively predict movements. To investigate this, we analyzed MEG data from nine right-handed participants while performing arm-reaching tasks. We employed dynamic statistical parametric mapping (dSPM) to estimate source activity and built a decoding model composed of long short-term memory (LSTM) and a multilayer perceptron to predict movement trajectories. This model achieved a high correlation coefficient of 0.79 between actual and predicted trajectories. Subsequently, we identified brain regions sensitive to predicting movement direction using the integrated gradients (IG) method, which assesses the predictive contribution of each source activity. The resulting salience map demonstrated a distribution without significant differences across motor-related regions, including M1. Predictions based solely on M1 activity yielded a correlation coefficient of 0.42, nearly half as effective as predictions incorporating all source activities. This suggests that upper limb movements are influenced by various factors such as movement coordination, planning, body and target position recognition, and control, beyond simple muscle activity. All of the activities are needed in the decoding model using macroscopic signals. Our findings also revealed that contralateral and ipsilateral hemispheres contribute equally to movement prediction, implying that BCIs could potentially benefit patients with brain damage in the contralateral hemisphere by utilizing brain signals from the ipsilateral hemisphere. In conclusion, this study demonstrates that macroscopic activity from large brain regions significantly contributes to predicting upper limb movement. Non-invasive BCI systems would require a comprehensive collection of neural signals from multiple brain regions.

Teach a man to fish: Hyper-brain evidence on scaffolding strategy enhancing creativity acquisition and transfer

Creativity is an indispensable competency in today's innovation-driven society. Yet, the influences of instructional strategy, a key determinant of educational outcomes, on the creativity-fostering process remains an unresolved mystery. We proposed that instructional strategy affects creativity cultivation and further investigated the intricate neural mechanisms underlying this relationship. In a naturalistic laboratory setting, 66 instructor-learner dyads were randomized into three groups (scaffolding, explanation, and control), with divergent thinking instructions separately. Functional near-infrared spectroscopy (fNIRS) hyperscanning simultaneously collected brain signals in the prefrontal cortex and temporal-parietal junction regions. Results indicated that learners instructed with a scaffolding strategy demonstrated superior creative performance both in acquisition (direct learning) and transfer (use in a novel context) of creativity skills, compared to pretest levels. In contrast, the control and explanation groups did not exhibit such effects. Notably, we also observed remarkable interbrain neural synchronization (INS) between instructors and learners in the left superior frontal cortex in the scaffolding group, but not in the explanation or control groups. Furthermore, INS positively predicted enhancements in creativity performance (acquisition and transfer), indicating that it is a crucial neural mechanism in the creativity-fostering process. These findings reveal that scaffolding facilitates the acquisition and transfer of creativity and deepen our understanding of the neural mechanisms underlying the process of creativity-fostering. The current study provides valuable insights for implementing teaching strategies to fostering creativity.

A comprehensive chemotyping and gonadal regulation of seven kisspeptinergic neuronal populations in the mouse brain.

Herein, we present a systematic analysis, using dual and multiplex RNAscope methods, of seven kisspeptinergic neuronal populations, based on their chemotyping and distribution throughout the mouse brain. The co-expression of mRNAs coding for neuropeptides, for excitatory and inhibitory transmitter vesicular transporters, and for sex steroid receptors are described in four hypothalamic and three extra-hypothalamic nuclei. These include a newly characterized kisspeptin-expressing ventral premammillary nucleus cell group co-expressing vesicular glutamate transporter 2, pituitary adenylate cyclase-activating polypeptide and neurotensin mRNAs. Kisspeptin mRNA ( Kiss1) was observed within both somatic and dendritic compartments at a single-cell level in two hypothalamic sites, a prominent and previously undescribed feature of kisspeptin neurons in these two cell groups. Patterns of altered Kiss1 expression following gonadectomy among these seven KP populations suggest that androgen receptor signaling may also play a previously unremarked role in gonadal feedback regulation of kisspeptinergic neuronal function. Data from this study provide a chemoanatomical basis for hypothesis generation regarding the functional diversity of kisspeptinergic signaling in hypothalamic and extrahypothalamic brain.

Manipulation of feeding patterns in high fat diet fed rats improves microbiota composition dynamics, inflammation and gut-brain signaling

Chronic consumption of high fat (HF) diets has been shown to increase meal size and meal frequency in rodents, resulting in overeating. Reducing meal frequency and establishing periods of fasting, independently of caloric intake, may improve obesity-associated metabolic disorders. Additionally, diet-driven changes in microbiota composition have been shown to play a critical role in the development and maintenance of metabolic disorders. In this study, we used a pair-feeding paradigm to reduce meal frequency and snacking episodes while maintaining overall intake and body weight in HF fed rats. We hypothesized that manipulation of feeding patterns would improve microbiota composition and metabolic outcomes. Male Wistar rats were placed in three groups consuming either a HF, low fat diet (LF, matched for sugar), or pair-fed HF diet for 7 weeks (n = 11–12/group). Pair-fed animals received the same amount of food consumed by the HF fed group once daily before dark onset (HF-PF). Rats underwent oral glucose tolerance and gut peptide cholecystokinin sensitivity tests. Bacterial DNA was extracted from the feces collected during both dark and light cycles and sequenced via Illumina MiSeq sequencing of the 16S V4 region. Our pair-feeding paradigm reduced meal numbers, especially small meals in the inactive phase, without changing total caloric intake. This shift in feeding patterns reduced relative abundances of obesity-associated bacteria and maintained circadian fluctuations in microbial abundances. These changes were associated with improved gastrointestinal (GI) function, reduced inflammation, and improved glucose tolerance and gut to brain signaling. We concluded from these data that targeting snacking may help improve metabolic outcomes, independently of energy content of the diet and hyperphagia.

Novel insights into the molecular mechanisms underlying anti-nociceptive effect of myricitrin against reserpine-induced fibromyalgia model in rats: Implication of SIRT1 and miRNAs

Ethnopharmacological relevanceManilkara zapota (L.) P. Royen, also termed sapodilla or chikoo, is a significant plant in ethnomedicine because of its long history of traditional medical applications. In diverse cultures, sapodilla is believed to protect against oxidative stress, inflammation, and some chronic diseases because of its high antioxidant content. The naturally occurring antioxidant myricitrin (MYR) flavonoid is primarily found in the leaves and other plant parts of sapodilla and it is well-known for having therapeutic qualities and possible health advantages. Aim of the studyTo appraise the possible impact of MYR on a rat model of reserpine-induced fibromyalgia (FM) and explore its mechanism of action. Materials and methodsIsolation and identification of MYR with more than 99% purity from Manilkara zapota leaves were primarily done and confirmed through chromatographic and spectrophotometric techniques. To develop FM model, reserpine (RSP) was injected daily (1 mg/kg, s.c.) for three successive days. Then, MYR (10 mg/kg, i.p.) and pregabalin (PGB, 30 mg/kg, p.o.) were given daily for another five days. Behavioral changes were assessed through open field test (OFT), hot plate test, and forced swimming test (FST). Further analyses of different brain parameters and signaling pathways were performed to assess monoamines levels, oxidative stress, inflammatory response, apoptotic changes as well as silent information regulator 1 (SIRT1) and micro RNAs (miRNAs) expressions. ResultsFrom High-Performance Liquid Chromatography (HPLC) analysis, the methanol extract of sapodilla leaves contains 166.17 μg/ml of MYR. Results of behavioral tests showed a significant improvement in RSP-induced nociceptive stimulation, reduced locomotion and exploration and depressive-like behavior by MYR. Biochemical analyses showed that MYR significantly ameliorated the RSP-induced imbalance in brain monoamine neurotransmitters. In addition, MYR significantly attenuated oxidative stress elicited by RSP via up-regulating nuclear factor erythroid 2-related factor 2 (Nrf2) and heme oxygenase-1 (HO-1) protein expressions, enhancing superoxide dismutase (SOD) and catalase (CAT) activities, and reducing malondialdehyde (MDA) content in brain. The RSP-provoked inflammatory response was also diminished by MYR treatment as shown by a significant decreased NOD-like receptor protein 3 (NLRP3) inflammasome expression along with reduced levels of interleukin 1 beta (IL-1β) and nuclear factor-κB (NF-κB). Furthermore, the anti-apoptotic activity of MYR was demonstrated by a marked rise in Bcl-2-associated X protein (BAX)/B cell lymphoma-2 (Bcl-2) ratio by lowering Bcl-2 while increasing BAX levels. In addition, MYR treatment significantly boosted the expression of SIRT1 deacetylase in RSP-treated animals. Interestingly, molecular docking showed the ability of MYR to form a stable complex in the binding site of SIRT1. Regarding miRNAs, MYR effectively ameliorated RSP-induced changes in miR-320 and miR-107 gene expressions. ConclusionOur findings afford new insights into the anti-nociceptive profile of MYR in the RSP-induced FM model in rats. The underlying mechanisms involved direct binding and activation of SIRT1 to influence different signaling cascades, including Nrf2 and NF-κB/NLRP3 together with modulation of miRNAs. However, more in-depth studies are needed before proposing MYR as a new clinically relevant drug in the management of FM.

Transferrable Subject-Independent Feature Representation for Discriminating EEG-Based Brain Signals

Subject-independent Electroencephalography (EEG) recognition remains challenging due to inherent variability of brain anatomy across different subjects. Such variability is further complicated by the Volume Conduction Effect (VCE) that introduces channel-interference noise, exacerbating subject-specific biases in the recorded EEG signals. Existing studies, often relying large datasets and entangled spatial-temporal features, struggle to overcome this bias, particularly in scenarios with limited EEG data. To this end, we propose a Temporal-Connective EEG Representation Learning (TCERL) framework that disentangles temporal and spatial feature learning. TCERL first employs an one-dimensional convolutional network to extract channel-specific temporal features, mitigating channel-interference noise caused by VCE. Building upon these temporal features, TCERL then leverages Graph Neural Networks to extract subject-invariant topological features from a functional brain network, constructed using the channel-specific features as nodes and functional connectivity as the adjacency matrix. This approach allows TCERL to capture robust representations of brain activity patterns that generalize well across different subjects. Our empirical experiment demonstrates that TCERL outperforms state-of-the-art across a range of training subjects on four public benchmarks and is less sensitive to subject variability. The performance gain is highlighted when limited subjects are available, suggesting the robustness and transferability of the proposed method. Source code can be found in: https://github.com/haoweilou/TCERL .

System Design and Development of a Novel, Unique Neuro Physical Medical Treatment Method for Spinal Muscular Atrophy and Similar Neurological Muscle Diseases.

The project is entirely original to the author. It proposes the theory of a method under development aimed at treating SMA and other related neurological muscle disorders The study will utilize a 14-channel EEG device to analyze brain data. The brain signals recorded by this device will be transmitted to the patient's muscles, potentially restoring functions lost in SMA patients. These functions include coughing, swallowing, breathing, chewing, walking, and movements of the hands, arms, and other muscles. By using this EEG device, brain signals can be visualized as waves, and its specialized software allows users to manipulate objects on a computer screen with thought alone. The system also simulates facial movements and expressions on-screen

A generic error-related potential classifier based on simulated subjects.

Error-related potentials (ErrPs) are brain signals known to be generated as a reaction to erroneous events. Several works have shown that not only self-made errors but also mistakes generated by external agents can elicit such event-related potentials. The possibility of reliably measuring ErrPs through non-invasive techniques has increased the interest in the brain-computer interface (BCI) community in using such signals to improve performance, for example, by performing error correction. Extensive calibration sessions are typically necessary to gather sufficient trials for training subject-specific ErrP classifiers. This procedure is not only time-consuming but also boresome for participants. In this paper, we explore the effectiveness of ErrPs in closed-loop systems, emphasizing their dependency on precise single-trial classification. To guarantee the presence of an ErrPs signal in the data we employ and to ensure that the parameters defining ErrPs are systematically varied, we utilize the open-source toolbox SEREEGA for data simulation. We generated training instances and evaluated the performance of the generic classifier on both simulated and real-world datasets, proposing a promising alternative to conventional calibration techniques. Results show that a generic support vector machine classifier reaches balanced accuracies of 72.9%, 62.7%, 71.0%, and 70.8% on each validation dataset. While performing similarly to a leave-one-subject-out approach for error class detection, the proposed classifier shows promising generalization across different datasets and subjects without further adaptation. Moreover, by utilizing SEREEGA, we can systematically adjust parameters to accommodate the variability in the ErrP, facilitating the systematic validation of closed-loop setups. Furthermore, our objective is to develop a universal ErrP classifier that captures the signal's variability, enabling it to determine the presence or absence of an ErrP in real EEG data.

A Neurotechnological Study to Quantify Differences in Brain Activity Using Game-Based Learning

This research delves into the comparative analysis of brain activity using gamification in the classroom versus traditional teaching. This study aims to employ neurotechnology to record and analyse the impact of active gamification methodology on relevant variables in the learning process within a traditional university education setting, presenting an innovative contribution to the existing literature. Neuroscience technology has been utilized to gauge cognitive processing of stimuli tailored for an academic experience in a university master's class. By scrutinizing brain recordings related to attention, interest, long term excitement, stress, relaxation, and engagement, the findings provide a quantitative assessment of key learning variables through brain signals. Gamification is the active methodology employed, and the application of neuroscience technologies facilitates an understanding of the variations in levels of brain activation among students, shedding light on the contributions of this active teaching methodology to the learning process.

Spatial-Temporal Dynamic Hypergraph Information Bottleneck for Brain Network Classification.

Recently, Graph Neural Networks (GNNs) have gained widespread application in automatic brain network classification tasks, owing to their ability to directly capture crucial information in non-Euclidean structures. However, two primary challenges persist in this domain. First, within the realm of clinical neuro-medicine, signals from cerebral regions are inevitably contaminated with noise stemming from physiological or external factors. The construction of brain networks heavily relies on set thresholds and feature information within brain regions, making it susceptible to the incorporation of such noises into the brain topology. Additionally, the static nature of the artificially constructed brain network's adjacent structure restricts real-time changes in brain topology. Second, mainstream GNN-based approaches tend to focus solely on capturing information interactions of nearest neighbor nodes, overlooking high-order topology features. In response to these challenges, we propose an adaptive unsupervised Spatial-Temporal Dynamic Hypergraph Information Bottleneck (ST-DHIB) framework for dynamically optimizing brain networks. Specifically, adopting an information theory perspective, Graph Information Bottleneck (GIB) is employed for purifying graph structure, and dynamically updating the processed input brain signals. From a graph theory standpoint, we utilize the designed Hypergraph Neural Network (HGNN) and Bi-LSTM to capture higher-order spatial-temporal context associations among brain channels. Comprehensive patient-specific and cross-patient experiments have been conducted on two available datasets. The results demonstrate the advancement and generalization of the proposed framework.

A Robust Automatic Epilepsy Seizure Detection Algorithm Based on Interpretable Features and Machine Learning

Epilepsy, as a serious neurological disorder, can be detected by analyzing the brain signals produced by neurons. Electroencephalogram (EEG) signals are the most important data source for monitoring these brain signals. However, these complex, noisy, nonlinear and nonstationary signals make detecting seizures become a challenging task. Feature-based seizure detection algorithms have become a dominant approach for automatic seizure detection. This study presents an algorithm for automatic seizure detection based on novel features with clinical and statistical significance. Our algorithms achieved the best results on two benchmark datasets, outperforming traditional feature-based methods and state-of-the-art deep learning algorithms. Accuracy exceeded 99.99% on both benchmark public datasets, with the 100% correct detection of all seizures on the second one. Due to the interpretability and robustness of our algorithm, combined with its minimal computational resource requirements and time consumption, it exhibited substantial potential value in the realm of clinical application. The coefficients of variation of datasets proposed by us makes the algorithm data-specific and can give theoretical guidance on the selection of appropriate random spectral features for different datasets. This will broaden the applicability scenario of our feature-based approach.

Reliability of variability and complexity measures for task and task-free BOLD fMRI.

Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.

1-Deoxynojirimycin attenuates pathological markers of Alzheimer's disease in the invitro model of neuronal insulin resistance.

Insulin resistance, the hallmark of type 2 diabetes mellitus (T2DM), has emerged as a pathological feature in Alzheimer's disease (AD). Given the shared role of insulin resistance in T2DM and AD, repurposing peripheral insulin sensitizers is a promising strategy to preserve neuronal insulin sensitivity and prevent AD. 1-Deoxynojirimycin (DNJ), a bioactive iminosugar, exhibited insulin-sensitizing effects in metabolic tissues and was detected in brain tissue post-oral intake. However, its impact on brain and neuronal insulin signaling has not been described. Here, we investigated the effect of DNJ treatment on insulin signaling and AD markers in insulin-resistant human SK-N-SH neuroblastoma, a cellular model of neuronal insulin resistance. Our findings show that DNJ increased the expression of insulin signaling genes and the phosphorylation status of key molecules implicated in insulin resistance (Y1146-pIRβ, S473-pAKT, S9-GSK3B) while also elevating the expression of glucose transporters Glut3 and Glut4, resulting in higher glucose uptake upon insulin stimuli. DNJ appeared to mitigate the insulin resistance-driven increase in phosphorylated tau and Aβ1-42 levels by promoting insulin-induced phosphorylation of GSK3B (a major tau kinase) and enhancing mRNA expression of the insulin-degrading enzyme (IDE) pivotal for insulin and Aβ clearance. Overall, our study unveils probable mechanisms underlying the potential benefits of DNJ for AD, wherein DNJ attenuates tau and amyloid pathologies by reversing neuronal insulin resistance. This provides a scientific basis for expanding the use of DNJ-containing products for neuroprotective purposes and prompts further research into compounds with similar mechanisms of action.

Dopamine and deep brain stimulation accelerate the neural dynamics of volitional action in Parkinson's disease.

The ability to initiate volitional action is fundamental to human behaviour. Loss of dopaminergic neurons in Parkinson's disease is associated with impaired action initiation, also termed akinesia. Both dopamine and subthalamic deep brain stimulation (DBS) can alleviate akinesia, but the underlying mechanisms are unknown. An important question is whether dopamine and DBS facilitate de novo build-up of neural dynamics for motor execution or accelerate existing cortical movement initiation signals through shared modulatory circuit effects. Answering these questions can provide the foundation for new closed-loop neurotherapies with adaptive DBS, but the objectification of neural processing delays prior to performance of volitional action remains a significant challenge. To overcome this challenge, we studied readiness potentials and trained brain signal decoders on invasive neurophysiology signals in 25 DBS patients (12 female) with Parkinson's disease during performance of self-initiated movements. Combined sensorimotor cortex electrocorticography and subthalamic local field potential recordings were performed OFF therapy (n = 22), ON dopaminergic medication (n = 18) and on subthalamic deep brain stimulation (n = 8). This allowed us to compare their therapeutic effects on neural latencies between the earliest cortical representation of movement intention as decoded by linear discriminant analysis classifiers and onset of muscle activation recorded with electromyography. In the hypodopaminergic OFF state, we observed long latencies between motor intention and motor execution for readiness potentials and machine learning classifications. Both, dopamine and DBS significantly shortened these latencies, hinting towards a shared therapeutic mechanism for alleviation of akinesia. To investigate this further, we analysed directional cortico-subthalamic oscillatory communication with multivariate granger causality. Strikingly, we found that both therapies independently shifted cortico-subthalamic oscillatory information flow from antikinetic beta (13-35 Hz) to prokinetic theta (4-10 Hz) rhythms, which was correlated with latencies in motor execution. Our study reveals a shared brain network modulation pattern of dopamine and DBS that may underlie the acceleration of neural dynamics for augmentation of movement initiation in Parkinson's disease. Instead of producing or increasing preparatory brain signals, both therapies modulate oscillatory communication. These insights provide a link between the pathophysiology of akinesia and its' therapeutic alleviation with oscillatory network changes in other non-motor and motor domains, e.g. related to hyperkinesia or effort and reward perception. In the future, our study may inspire the development of clinical brain computer interfaces based on brain signal decoders to provide temporally precise support for action initiation in patients with brain disorders.

Single Finger Trajectory Prediction From Intracranial Brain Activity Using Block-Term Tensor Regression With Fast and Automatic Component Extraction.

Multiway- or tensor-based decoding techniques for brain-computer interfaces (BCIs) are believed to better account for the multilinear structure of brain signals than conventional vector- or matrix-based ones. However, despite their outlook on significant performance gains, the used parameter optimization approach is often too computationally demanding so that conventional techniques are still preferred. We propose two novel tensor factorizations which we integrate into our block-term tensor regression (BTTR) algorithm and further introduce a marginalization procedure that guarantees robust predictions while reducing the risk of overfitting (generalized regression). BTTR accounts for the underlying (hidden) data structure in a fully automatic and computationally efficient manner, leading to a significant performance gain over conventional vector- or matrix-based techniques in a challenging real-world application. As a challenging real-world application, we apply BTTR to accurately predict single finger movement trajectories from intracranial recordings in human subjects. We compare the obtained performance with that of the state-of-the-art.

Biomarkers of attention bias during public speaking anxiety

The analysis of brain signals and their properties yields significant insights into the fundamental neural impairments associated with attention bias in individuals suffering from public speaking anxiety (PSA). This study aims to identify electroencephalogram (EEG) and performance biomarkers of attention bias in individuals with public speaking anxiety using the ex-Gaussian modeling technique, frontal alpha asymmetry (FAA) and delta-beta correlation (DBC). 12 subjects with high (H) PSA and 12 subjects with low (L) PSA performed the modified emotional stroop task. EEG data were captured using the low-cost 14-channel emotiv Epoc+. Results showed that the ex-Gaussian sigma was higher in the emotional condition in the high public speaking anxiety (HPSA) group, indicating attention bias. The study also found higher right FAA in HPSA compared to LPSA group. There was a negative correlation between σ and alpha power in the left region of the brain in the HPSA group, potentially related to attentional bias. Moreover, there was a notable trend towards significantly heightened DBC in the frontal and central regions of the brain among HPSA subjects. In conclusion, in biomedical engineering, the ex-Gaussian model, FAA and DBC are useful because they can identify EEG and performance biomarkers of attention bias in people with PSA.

Boosting brain-computer interface performance through cognitive training: A brain-centric approach

Previous efforts to boost the performance of brain-computer interfaces (BCIs) have predominantly focused on optimizing algorithms for decoding brain signals. However, the untapped potential of leveraging brain plasticity for optimization remains underexplored. In this study, we enhanced the temporal resolution of the human brain in discriminating visual stimuli by eliminating the attentional blink (AB) through color-salient cognitive training, and we confirmed that the mechanism was an attention-based improvement. Using the rapid serial visual presentation (RSVP)-based BCI, we evaluated the behavioral and electroencephalogram (EEG) decoding performance of subjects before and after cognitive training in high target percentage (with AB) and low target percentage (without AB) surveillance tasks, respectively. The results consistently demonstrated significant improvements in the trained subjects. Further analysis indicated that this improvement was attributed to the cognitively trained brain producing more discriminative EEG. Our work highlights the feasibility of cognitive training as a means of brain enhancement to boost BCI performance.

A Hybrid Deep Learning Framework Using Scaling‐Basis Chirplet Transform for Motor Imagery EEG Recognition in Brain–Computer Interface Applications

ABSTRACTThe emerging field of brain–computer interface has significantly facilitated the analysis of electroencephalogram signals required for motor imagery classification tasks. However, the accuracy of EEG classification models has been restricted by the low signal‐to‐noise ratio, nonlinear nature of brain signals, and a lack of sufficient EEG data for training. To address these challenges, this study proposes a new approach that combines time‐frequency analysis with a hybrid parallel–series attention‐based deep learning network for EEG signal classification. The proposed framework comprises three main elements: first, a scaling‐basis chirplet transform designed to effectively capture the characteristics of nonstationary EEG signals; second, a hybrid parallel–series attention‐based deep learning network to extract features. The serial information flow continuously expands the receptive fields of output neurons, whereas parallel information flow extracts features based on different regions. Finally, machine learning classifiers are utilized to predict the corresponding motor imagery state. The developed EEG‐based motor imagery classification framework is assessed by two open‐source datasets, BCI competition III, dataset IIIa and BCI competition IV, dataset IIa and has achieved the average classification accuracy of 95.55% on BCI competition III, dataset IIIa and 90.18% on BCI competition IV, dataset IIa. The experimental findings illustrate that this study has attained promising motor imagery discrimination performance, surpassing existing techniques in terms of classification accuracy and kappa coefficient.