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The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can facilitate the advancement of brain-computer interfaces (BCIs). However, existing research in this domain has grappled with the challenge of the efficient selection of features, resulting in the underutilization of the temporal richness of EEG and the spatial specificity of fNIRS data.To effectively address this challenge, this study proposed a deep learning architecture called the multimodal DenseNet fusion (MDNF) model that was trained on two-dimensional (2D) EEG data images, leveraging advanced feature extraction techniques. The model transformed EEG data into 2D images using a short-time Fourier transform, applied transfer learning to extract discriminative features, and consequently integrated them with fNIRS-derived spectral entropy features. This approach aimed to bridge existing gaps in EEG-fNIRS-based BCI research by enhancing classification accuracy and versatility across various cognitive and motor imagery tasks.Experimental results on two public datasets demonstrated the superiority of our model over existing state-of-the-art methods.Thus, the high accuracy and precise feature utilization of the MDNF model demonstrates the potential in clinical applications for neurodiagnostics and rehabilitation, thereby paving the method for patient-specific therapeutic strategies.

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Automatic seizure detection has significant value in epilepsy diagnosis and treatment. Although a variety of deep learning models have been proposed to automatically learn electroencephalography (EEG) features for seizure detection, the generalization performance and computational burden of such deep models remain the bottleneck of practical application. In this study, a novel lightweight model based on random convolutional kernel transform (ROCKET) is developed for EEG feature learning for seizure detection. Specifically, random convolutional kernels are embedded into the structure of a wavelet scattering network instead of original wavelet transform convolutions. Then the significant EEG features are selected from the scattering coefficients and convolutional outputs by analysis of variance (ANOVA) and minimum redundancy-maximum relevance (MRMR) methods. This model not only preserves the merits of the fast-training process from ROCKET, but also provides insight into seizure detection by retaining only the helpful channels. The extreme gradient boosting (XGboost) classifier was combined with this EEG feature learning model to build a comprehensive seizure detection system that achieved promising epoch-based results, with over 90% of both sensitivity and specificity on the scalp and intracranial EEG databases. The experimental comparisons showed that the proposed method outperformed other state-of-the-art methods for cross-patient and patient-specific seizure detection.

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Determining the presence and frequency of neural oscillations is essential to understanding dynamic brain function. Traditional methods that detect peaks over 1/f noise within the power spectrum fail to distinguish between the fundamental frequency and harmonics of often highly non-sinusoidal neural oscillations. To overcome this limitation, we define fundamental criteria that characterize neural oscillations and introduce the cyclic homogeneous oscillation (CHO) detection method. We implemented these criteria based on an autocorrelation approach to determine an oscillation's fundamental frequency. We evaluated CHO by verifying its performance on simulated non-sinusoidal oscillatory bursts and validated its ability to determine the fundamental frequency of neural oscillations in electrocorticographic (ECoG), electroencephalographic (EEG), and stereoelectroencephalographic (SEEG) signals recorded from 27 human subjects. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method's specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.

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Brain-computer interface (BCI) technology has gained recognition in various fields, including clinical applications, assistive technology, and human-computer interaction research. BCI enables communication, control, and monitoring of the affective/cognitive states of users. Recently, BCI has also found applications in the artistic field, enabling real-time art composition using brain activity signals, and engaging performers, spectators, or an entire audience with brain activity-based artistic environments. Existing techniques use specific features of brain activity, such as the P300 wave and SSVEPs, to control drawing tools, rather than directly reflecting brain activity in the output image. In this study, we present a novel approach that uses a latent diffusion model, a type of deep neural network, to generate images directly from continuous brain activity. We demonstrate this technology using local field potentials from the neocortex of freely moving rats. This system continuously converted the recorded brain activity into images. Our end-to-end method for generating images from brain activity opens new possibilities for creative expression and experimentation. Notably, our results show that the generated images successfully reflect the dynamic and stochastic nature of the underlying neural activity, providing a unique procedure for visualization of brain function.

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Aiming at the problem that the feature extraction ability of forehead single-channel electroencephalography (EEG) signals is insufficient, which leads to decreased fatigue detection accuracy, a fatigue feature extraction and classification algorithm based on supervised contrastive learning is proposed. Firstly, the raw signals are filtered by empirical modal decomposition to improve the signal-to-noise ratio. Secondly, considering the limitation of the one-dimensional signal in information expression, overlapping sampling is used to transform the signal into a two-dimensional structure, and simultaneously express the short-term and long-term changes of the signal. The feature extraction network is constructed by depthwise separable convolution to accelerate model operation. Finally, the model is globally optimized by combining the supervised contrastive loss and the mean square error loss. Experiments show that the average accuracy of the algorithm for classifying three fatigue states can reach 75.80%, which is greatly improved compared with other advanced algorithms, and the accuracy and feasibility of fatigue detection by single-channel EEG signals are significantly improved. The results provide strong support for the application of single-channel EEG signals, and also provide a new idea for fatigue detection research.

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BACKGROUND AND OBJECTIVES: Treatment-resistant depression is a leading cause of disability. Our center's trial for neurosurgical intervention for treatment-resistant depression involves a staged workup for implantation of a personalized, closed-loop neuromodulation device for refractory depression. The first stage ("stage 1") of workup involves implantation of 10 stereoelectroencephalography (SEEG) electrodes bilaterally into 5 anatomically defined brain regions and involves a specialized preoperative imaging and planning workup and a frame-based operating protocol. METHODS: We rely on diffusion tractography when planning stereotactic targets for 3 of 5 anatomic areas. We outline the rationale and fiber tracts that we focus on for targeting amygdala, ventral striatum and ventral capsule, and subgenual cingulate. We also outline frame-based stereotactic considerations for implantation of SEEG electrodes. EXPECTED OUTCOMES: Our method has allowed us to safely target all 5 brain areas in 3 of 3 trial participants in this ongoing study, with adequate fiber bundle contact in each of the 3 areas targeted using tractography. Furthermore, we ultimately used tractography data from our stage 1 workup to guide targeting near relevant fiber bundles for stage 2 (implantation of a responsive neuromodulation device). On completion of our data set, we will determine the overlap between volume of tissue activated for all electrodes and areas of interest defined by anatomy and tractography. DISCUSSION: Our protocol outlined for SEEG electrode implantation incorporates tractography and frame-based stereotaxy.

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Magnetoencephalography/electroencephalography (M/EEG) can provide insights into migraine pathophysiology and help develop clinically valuable biomarkers. To integrate and summarize the existing evidence on changes in brain function in migraine, we performed a systematic review and meta-analysis (PROSPERO CRD42021272622) of resting-state M/EEG findings in migraine. We included 27 studies after searching MEDLINE, Web of Science Core Collection, and EMBASE. Risk of bias was assessed using a modified Newcastle-Ottawa Scale. Semi-quantitative analysis was conducted by vote counting, and meta-analyses of M/EEG differences between people with migraine and healthy participants were performed using random-effects models. In people with migraine during the interictal phase, meta-analysis revealed higher power of brain activity at theta frequencies (3-8 Hz) than in healthy participants. Furthermore, we found evidence for lower alpha and beta connectivity in people with migraine in the interictal phase. No associations between M/EEG features and disease severity were observed. Moreover, some evidence for higher delta and beta power in the premonitory compared to the interictal phase was found. Strongest risk of bias of included studies arose from a lack of controlling for comorbidities and non-automatized or non-blinded M/EEG assessments. These findings can guide future M/EEG studies on migraine pathophysiology and brain-based biomarkers, which should consider comorbidities and aim for standardized, collaborative approaches.

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The world has a higher count of death rates as a result of Alcohol consumption. Identification is possible because Alcoholic EEG waves have a certain behavior that is totally different compared to the non-alcoholic individual. The available approaches take longer to provide the feedback because they analyze the data manually. For this reason, in the present paper we propose a novel approach applied to detect alcoholic EEG signals automatically by using deep learning methods. Our strategy has advantages as far as fast detection is concerned; hence people can help immediately when there is a need. The potential for a significant decrease in deaths from alcohol poisoning and improvement to public health is presented by this advancement. In order to create clusters and classify the alcoholic EEG signals, this research uses a cascaded process. To begin with, an initial clustering and feature extraction is done by LASSO regression. After that, a variety of meta-heuristics algorithms like Particle Swarm Optimization (PSO), Binary Coding Harmony Search (BCHS) as well as Binary Dragonfly Algorithm (BDA) are employed for feature minimization. When this method is used, normal and alcoholic EEG signals may be differentiated using non-linear features. PSO, BCHS, and BDA features allow for estimation of statistical parameters through t-test, Friedman statistic test, Mann-Whitney U test, and Z-Score with corresponding p-values for alcoholic EEG signals. Lastly, classification is done by the use of support vector machines (SVM) (including linear, polynomial, and Gaussian kernels), random forests, artificial neural networks (ANN), enhanced artificial neural networks (EANN), and LSTM models. Results showed that LASSO regression with BDA-based EANN proposed classifier have a classification accuracy of 99.59%, indicating that our method is highly accurate at classifying alcoholic EEG signals.

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BACKGROUND: Activation procedures (APs) are adopted during routine electroencephalography (rEEG) to provoke interictal epileptiform abnormalities (EAs). This study aimed to observe interictal and ictal (EAs) of different EEG patterns, provoked by various APs. METHODOLOGY: This cross-sectional study was performed in the neurology department of King Fahd hospital of university, Saudi Arabia. The EEGs and medical records of patients who presented for EEG recordings were screened initially, then 146 EEGs provoked EAs due to utilization of APs, were included for analysis. RESULTS: Among all EEGs with provoked EAs, Non-rapid eye movement sleep (NREM) provoked EAs in 93 (63.7%) patients with following patterns, focal spike wave discharges (FSWDs) 45 (P= 0.01), focal spike wave discharges with bilateral synchrony (FSWDBS) 27 (P=0.03) and generalized spike wave discharges (GSWDs) 46 (P=0.01). Intermittent photic stimulation (IPS) most significantly provoked FSWDs in 07 patient (P =0.01) and GSWDs in 30 patients (P=<0.001) 7 patients (P = 0.01) and GSWDs in 30 patients (P < 0.001). Hyperventilation (HV) was associated with a higher occurrence of GSWDs in 37 patients (P =0.01). Female sex 7 (P = 0.02), provoked GSWDs 3 (P = 0.03), NREM sleep 8 (P = 0.04), prolonged EEG record 3 (P = 0.02), clinical events during recording 5 (P ≤ 0.01), diagnosis of genetic 05 (P = 0.03), and immune-mediated epilepsies 2 (P = 0.001) were associated with the provocation of ictal EAs; however, in multiple logistic regression analysis, no statistically significant association of these variables (P ≥ 0.05 each) was noted. CONCLUSION: The provocation of EAs in rEEG with different APs varies according to circumstances, including seizure types, epilepsy etiology, and the type of AP applied. These clinical and procedural parameters affect the diagnostic yield of rEEG and need careful consideration during rEEG recordings. APs adopted during rEEG recording can induce FSWDs, FSWDBS, and GSWDs in the form of either interictal or ictal EAs in various etiologies of epilepsy. Ictal EAs may appear in the form of GSWDs, during NREM sleep, in prolonged EEG records; however, their independent association needs to be evaluated in larger sample studies. Further, prospective cohort studies with adequate sample sizes are warranted.

Résumé Contexte:Des procédures d'activation (AP) sont adoptées lors d'une électroencéphalographie de routine (rEEG) pour provoquer des anomalies épileptiformes (EA) intercritiques. Cette étude visait à observer les inter-critiques et critiques (EA) de différents modèles EEG, provoqués par divers PA.Méthodes:Cette étude transversale a été réalisée dans le département de neurologie de l'hôpital universitaire King Fahd de Khobar, en Arabie Saoudite. Les EEG et les dossiers médicaux des patients qui se sont présentés pour des enregistrements EEG ont été initialement examinés, puis 146 EEG avec des EA provoqués lors de l'utilisation des AP ont été inclus pour analyse.Résultats:Parmi tous les EEG avec des AE provoqués, le sommeil à mouvements oculaires non rapides (NREM) a provoqué des EA chez 93 (63,7 %) patients avec les schémas suivants : décharges d'ondes de pointe focales (FSWD) 45 (P = 0,01), onde de pointe focale avec bilatéral synchronisation (FSWBS) 27 (P = 0,03) et décharges d'ondes de pointe généralisées (GSWD) 46 (P = 0,01). La stimulation photique intermittente (IPS) a provoqué de manière plus significative des FSWD chez 07 patients (P = 0,01) et des GSWD chez 30 patients (P = < 0,001) 7 patients (P = 0,01) et des GSWD chez 30 patients (P < 0,001). L'hyperventilation (HV) était associée à une fréquence plus élevée de GSWD chez 37 patients (P = 0,01). Sexe féminin 07 (P = 0,02), GSWD provoqués 03 (P = 0,03), sommeil NREM 08 (P = 0,04), enregistrement EEG prolongé 03 (P = 0,02), événements cliniques lors de l'enregistrement 05 (P = < 0,01), diagnostic des épilepsies génétiques 05 (P = 0,03) et des épilepsies à médiation immunitaire 02 (P = 0,001) étaient associées à la provocation d'EA critiques, cependant, dans l'analyse de régression logistique multiple, aucune association statistiquement significative de ces variables (P = > 0,05 chacune) était noté.Conclusion:La provocation d'EA dans l'EEGr avec différents AP varie en fonction des circonstances, notamment des types de crises, de l'étiologie de l'épilepsie et du type d'AP appliqué. Ces paramètres cliniques et procéduraux affectent le rendement diagnostique du rEEG et doivent être soigneusement pris en compte lors des enregistrements rEEG. Les AP adoptés lors de l'enregistrement rEEG peuvent induire des FSWD, des FSWBS et des GSWD sous la forme d'EA inter-critiques ou critiques dans diverses étiologies de l'épilepsie. Les EA critiques peuvent apparaître sous forme de GSWD, pendant le sommeil NREM, dans les enregistrements EEG prolongés; cependant, leur association indépendante doit être évaluée dans des études sur un échantillon plus large. De plus, des études de cohortes prospectives avec des échantillons de taille adéquate sont justifiées.

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BACKGROUND: Electroencephalography (EEG) is a promising tool for identifying the physiological biomarkers of fibromyalgia (FM). Evidence suggests differences in power band and density between individuals with FM and healthy controls. EEG changes appear to be associated with pain intensity; however, their relationship with the quality of pain has not been examined. We aimed to investigate whether abnormal EEG in the frontal and central points of the 10-20 EEG system in individuals with FM is associated with pain's sensory-discriminative and affective-motivational dimensions. The association between EEG and the two dimensions of emotional disorders (depression and anxiety) was also investigated. METHODS: In this cross-sectional pilot study, pain experience (pain rating index [PRI]) and two dimensions of emotional disorders (depression and anxiety) were assessed using the McGill Pain Questionnaire (PRI-sensory and PRI-affective) and Hospital Anxiety and Depression Scale (HADS), respectively. In quantitative EEG analysis, the relative spectral power of each frequency band (delta, theta, alpha, and beta) was identified in the frontal and central points during rest. RESULTS: A negative correlation was found between the relative spectral power for the delta bands in the frontal (r= -0.656; p = 0.028) and central points (r= -0.624; p = 0.040) and the PRI-affective scores. A positive correlation was found between the alpha bands in the frontal (r = 0.642; p = 0.033) and central points (r = 0.642; p = 0.033) and the PRI-affective scores. A negative correlation between the delta bands in the central points and the anxiety subscale of the HADS (r = -0.648; p = 0.031) was detected. CONCLUSION: The affective-motivational dimension of pain and mood disorders may be related to abnormal patterns of electrical activity in patients with FM. TRIAL REGISTRATION: Retrospectively registered on ClinicalTrials.gov (NCT05962658).

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The characterisation of resting-state networks (RSNs) using neuroimaging techniques has significantly contributed to our understanding of the organisation of brain activity. Prior work has demonstrated the electrophysiological basis of RSNs and their dynamic nature, revealing transient activations of brain networks with millisecond timescales. While previous research has confirmed the comparability of RSNs identified by electroencephalography (EEG) to those identified by magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), most studies have utilised static analysis techniques, ignoring the dynamic nature of brain activity. Often, these studies use high-density EEG systems, which limit their applicability in clinical settings. Addressing these gaps, our research studies RSNs using medium-density EEG systems (61 sensors), comparing both static and dynamic brain network features to those obtained from a high-density MEG system (306 sensors). We assess the qualitative and quantitative comparability of EEG-derived RSNs to those from MEG, including their ability to capture age-related effects, and explore the reproducibility of dynamic RSNs within and across the modalities. Our findings suggest that both MEG and EEG offer comparable static and dynamic network descriptions, albeit with MEG offering some increased sensitivity and reproducibility. Such RSNs and their comparability across the two modalities remained consistent qualitatively but not quantitatively when the data were reconstructed without subject-specific structural MRI images.

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Injection molding is a common plastic processing technique that allows melted plastic to be injected into a mold through pressure to form differently shaped plastic parts. In injection molding, in-mold electronics (IME) can include various circuit components, such as sensors, amplifiers, and filters. These components can be injected into the mold to form a whole within the melted plastic and can therefore be very easily integrated into the molded part. The brain-computer interface (BCI) is a direct connection pathway between a human or animal brain and an external device. Through BCIs, individuals can use their own brain signals to control these components, enabling more natural and intuitive interactions. In addition, brain-computer interfaces can also be used to assist in medical treatments, such as controlling prosthetic limbs or helping paralyzed patients regain mobility. Brain-computer interfaces can be realized in two ways: invasively and noninvasively, and in this paper, we adopt a noninvasive approach. First, a helmet model is designed according to head shape, and second, a printed circuit film is made to receive EEG signals and an IME injection mold for the helmet plastic parts. In the electronic film, conductive ink is printed to connect each component. However, improper parameterization during the injection molding process can lead to node displacements and residual stress changes in the molded part, which can damage the circuits in the electronic film and affect its performance. Therefore, in this paper, the use of the BCI molding process to ensure that the node displacement reaches the optimal value is studied. Second, the multistrategy differential evolutionary algorithm is used to optimize the injection molding parameters in the process of brain-computer interface formation. The relationship between the injection molding parameters and the actual target value is investigated through Latin hypercubic sampling, and the optimized parameters are compared with the target parameters to obtain the optimal parameter combination. Under the optimal parameters, the node displacement can be optimized from 0.585 to 0.027 mm, and the optimization rate can reach 95.38%. Ultimately, by detecting whether the voltage difference between the output inputs is within the permissible range, the reliability of the brain-computer interface after node displacement optimization can be evaluated.

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Frequency analysis via electroencephalography (EEG) during general anesthesia is used to develop techniques for measuring anesthesia depth. Variational mode decomposition (VMD) enables mathematical optimization methods to decompose EEG signals into natural number intrinsic mode functions with distinct narrow bands. However, the analysis requires the a priori determination of hyperparameters, including the decomposition number (K) and the penalty factor (PF). In the VMD analysis of EEGs derived from a noninterventional and noninvasive retrospective observational study, we adapted the grey wolf optimizer (GWO) to determine the K and PF hyperparameters of the VMD. As a metric for optimization, we calculated the envelope function of the IMF decomposed via the VMD method and used its envelope entropy as the fitness function. The K and PF values varied in each epoch, with one epoch being the analytical unit of EEG; however, the fitness values showed convergence at an early stage in the GWO algorithm. The K value was set to 2 to capture the α wave enhancement observed during the maintenance phase of general anesthesia in intrinsic mode function 2 (IMF-2). This study suggests that using the GWO to optimize VMD hyperparameters enables the construction of a robust analytical model for examining the EEG frequency characteristics involved in the effects of general anesthesia.

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Working memory (WM) is crucial for adequate performance execution in effective decision-making, enabling individuals to identify patterns and link information by focusing on current and past situations. This work explored behavioral and electrophysiological (EEG) WM correlates through a novel decision-making task, based on real-life situations, assessing WM workload related to contextual variables. A total of 24 participants performed three task phases (encoding, retrieval, and metacognition) while their EEG activity (delta, theta, alpha, and beta frequency bands) was continuously recorded. From the three phases, three main behavioral indices were computed: Efficiency in complex Decision-making, Tolerance of Decisional Complexity, and Metacognition of Difficulties. Results showed the central role of alpha and beta bands during encoding and retrieval: decreased alpha/beta activity in temporoparietal areas during encoding might indicate activation of regions related to verbal WM performance and a load-related effect, while decreased alpha activity in the same areas and increased beta activity over posterior areas during retrieval might indicate, respectively, active information processing and focused attention. Evidence from correlational analysis between the three indices and EEG bands are also discussed. Integration of behavioral and metacognitive data gathered through this novel task and their interrelation with EEG correlates during task performance proves useful to assess WM workload during complex managerial decision-making.

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A brain-computer interface could control a bionic hand by interpreting electroencephalographic (EEG) signals associated with wrist extension (WE) and wrist flexion (WF) movements. Misinterpretations of the EEG may stem from variations in the force, speed and range of these movements. To address this, we designed, constructed and tested a novel dynamometer, the IsoReg, which regulates WE and WF movements during EEG recording experiments. The IsoReg restricts hand movements to isometric WE and WF, controlling their speed and range of motion. It measures movement force using a dual-load cell system that calculates the percentage of maximum voluntary contraction and displays it to help users control movement force. Linearity and measurement accuracy were tested, and the IsoReg's performance was evaluated under typical EEG experimental conditions with 14 participants. The IsoReg demonstrated consistent linearity between applied and measured forces across the required force range, with a mean accuracy of 97% across all participants. The visual force gauge provided normalised force measurements with a mean accuracy exceeding 98.66% across all participants. All participants successfully controlled the motor tasks at the correct relative forces (with a mean accuracy of 89.90%) using the IsoReg, eliminating the impact of inherent force differences between typical WE and WF movements on the EEG analysis. The IsoReg offers a low-cost method for measuring and regulating movements in future neuromuscular studies, potentially leading to improved neural signal interpretation.

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This paper comprehensively reviews hardware acceleration techniques and the deployment of convolutional neural networks (CNNs) for analyzing electroencephalogram (EEG) signals across various application areas, including emotion classification, motor imagery, epilepsy detection, and sleep monitoring. Previous reviews on EEG have mainly focused on software solutions. However, these reviews often overlook key challenges associated with hardware implementation, such as scenarios that require a small size, low power, high security, and high accuracy. This paper discusses the challenges and opportunities of hardware acceleration for wearable EEG devices by focusing on these aspects. Specifically, this review classifies EEG signal features into five groups and discusses hardware implementation solutions for each category in detail, providing insights into the most suitable hardware acceleration strategies for various application scenarios. In addition, it explores the complexity of efficient CNN architectures for EEG signals, including techniques such as pruning, quantization, tensor decomposition, knowledge distillation, and neural architecture search. To the best of our knowledge, this is the first systematic review that combines CNN hardware solutions with EEG signal processing. By providing a comprehensive analysis of current challenges and a roadmap for future research, this paper provides a new perspective on the ongoing development of hardware-accelerated EEG systems.

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Visual information affects static postural control, but how it affects dynamic postural control still needs to be fully understood. This study investigated the effect of proprioception weighting, influenced by the presence or absence of visual information, on dynamic posture control during voluntary trunk movements. We recorded trunk movement angle and angular velocity, center of pressure (COP), electromyographic, and electroencephalography signals from 35 healthy young adults performing a standing trunk flexion-extension task under two conditions (Vision and No-Vision). A random forest analysis identified the 10 most important variables for classifying the conditions, followed by a Wilcoxon signed-rank test. The results showed lower maximum forward COP displacement and trunk flexion angle, and faster maximum flexion angular velocity in the No-Vision condition. Additionally, the alpha/beta ratio of the POz during the switch phase was higher in the No-Vision condition. These findings suggest that visual deprivation affects cognitive- and sensory-integration-related brain regions during movement phases, indicating that sensory re-weighting due to visual deprivation impacts motor control. The effects of visual deprivation on motor control may be used for evaluation and therapeutic interventions in the future.

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Verifying schizophrenia (SZ) can be assisted by deep learning techniques and patterns in brain activity observed in alpha-EEG recordings. The suggested research provides evidence of the reliability of alpha-EEG rhythm in a Gated-Recurrent-Unit-based deep-learning model for investigating SZ. This study suggests Rudiment Densely-Coupled Convolutional Gated Recurrent Unit (RDCGRU) for the various EEG-rhythm-based (gamma, beta, alpha, theta, and delta) diagnoses of SZ. The model includes multiple 1-D-Convolution (Con-1-D) folds with steps greater than 1, which enables the model to programmatically and effectively learn how to reduce the incoming signal. The Con-1-D layers and numerous Gated Recurrent Unit (GRU) layers comprise the Exponential-Linear-Unit activation function. This powerful activation function facilitates in-deep-network training and improves classification performance. The Densely-Coupled Convolutional Gated Recurrent Unit (DCGRU) layers enable RDCGRU to address the training accuracy loss brought on by vanishing or exploding gradients, and this might make it possible to develop intense, deep versions of RDCGRU for more complex problems. The sigmoid activation function is implemented in the digital (binary) classifier's output nodes. The RDCGRU deep learning model attained the most excellent accuracy, 88.88 %, with alpha-EEG rhythm. The research achievements: The RDCGRU deep learning model's GRU cells responded superiorly to the alpha-EEG rhythm in EEG-based verification of SZ.

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Objective.Steady-state visual evoked potentials (SSVEPs) in response to flickering stimuli are popular in brain-computer interfacing but their implementation in virtual reality (VR) offers new opportunities also for clinical applications. While traditional SSVEP target selection relies on single-frequency stimulation of both eyes simultaneously, further called congruent stimulation, recent studies attempted to improve the information transfer rate by using dual-frequency-coded SSVEP where each eye is presented with a stimulus flickering at a different frequency, further called incongruent stimulation. However, few studies have investigated incongruent multifrequency-coded SSVEP (MultiIncong-SSVEP).Approach.This paper reports on a systematical investigation of incongruent dual-, triple-, and quadruple-frequency-coded SSVEP for use in VR, several of which are entirely novel, and compares their performance with that of congruent dual-frequency-coded SSVEP.Main results.We were able to confirm the presence of a summation effect when comparing monocular- and binocular single-frequency congruent stimulation, and a suppression effect when comparing monocular- and binocular dual-frequency incongruent stimulation, as both tap into the binocular vision capabilities which, when hampered, could signal amblyopia.Significance.In sum, our findings not only evidence the potential of VR-based binocularly incongruent SSVEP but also underscore the importance of paradigm choice and decoder design to optimize system performance and user comfort.

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