A robust multi-branch multi-attention-mechanism EEGNet for motor imagery BCI decoding.

BACKGROUND: Motor-Imagery-based Brain-Computer Interface (MI-BCI) is a promising technology to assist communication, movement, and neurological rehabilitation for motor-impaired individuals. Electroencephalography (EEG) decoding techniques using deep learning (DL) possess noteworthy advantages due to automatic feature extraction and end-to-end learning. However, the DL-based EEG decoding models tend to show large variations due to intersubject variability of EEG, which results from inconsistencies of different subjects' optimal hyperparameters. NEW METHODS: This study proposes a multi-branch multi-attention mechanism EEGNet model (MBMANet) for robust decoding. It applies the multi-branch EEGNet structure to achieve various feature extractions. Further, the different attention mechanisms introduced in each branch attain diverse adaptive weight adjustments. This combination of multi-branch and multi-attention mechanisms allows for multi-level feature fusion to provide robust decoding for different subjects. RESULTS: The MBMANet model has a four-classification accuracy of 83.18% and kappa of 0.776 on the BCI Competition IV-2a dataset, which outperforms other eight CNN-based decoding models. This consistently satisfactory performance across all nine subjects indicates that the proposed model is robust. CONCLUSIONS: The combine of multi-branch and multi-attention mechanisms empowers the DL-based models to adaptively learn different EEG features, which provides a feasible solution for dealing with data variability. It also gives the MBMANet model more accurate decoding of motion intentions and lower training costs, thus improving the MI-BCI's utility and robustness.

Heart rate detection method based on Ballistocardiogram signal of wearable device：Algorithm development and validation.

Background: Heart rate, as the four vital signs of human body, is a basic indicator to measure a person's health status. Traditional electrocardiography (ECG) measurement, which is routinely monitored, requires subjects to wear lead electrodes frequently, which undoubtedly places great restrictions on participants' activities during the normal test. At present, the boom of wearable devices has created hope for non-invasive, simple operation and low-cost daily heart rate monitoring, among them, Ballistocardiogram signal (BCG) is an effective heart rate measurement method, but in the actual acquisition process, the robustness of non-invasive vital sign collection is limited. Therefore, it is necessary to develop a method to improve the robustness of heart rate monitoring. Objective: Therefore, in view of the problem that the accuracy of untethered monitoring heart rate is not high, we propose a method aimed at detecting the heartbeat cycle based on BCG to accurately obtain the beat-to-beat heart rate in the sleep state. Methods: In this study, we implement an innovative J-wave detection algorithm based on BCG signals. By collecting BCG signals recorded by 28 healthy subjects in different sleeping positions, after preprocessing, the data feature set is formed according to the clustering of morphological features in the heartbeat interval. Finally, a J-wave recognition model is constructed based on bi-directional long short-term memory (BiLSTM), and then the number of J-waves in the input sequence is counted to realize real-time detection of heartbeat. The performance of the proposed heartbeat detection scheme is cross-verified, and the proposed method is compared with the previous wearable device algorithm. Results: The accuracy of J wave recognition in BCG signal is 99.67%, and the deviation rate of heart rate detection is only 0.27%, which has higher accuracy than previous wearable device algorithms. To assess consistency between method results and heart rates obtained by the ECG, seven subjects are compared using Bland-Altman plots, which show no significant difference between BCG and ECG results for heartbeat cycles. Conclusions: Compared with other studies, the proposed method is more accurate in J-wave recognition, which improves the accuracy and generalization ability of BCG-based continuous heartbeat cycle extraction, and provides preliminary support for wearable-based untethered daily monitoring.

Personalized motor imagery prediction model based on individual difference of ERP.

Objective. Motor imagery-based brain-computer interaction (MI-BCI) is a novel method of achieving human and external environment interaction that can assist individuals with motor disorders to rehabilitate. However, individual differences limit the utility of the MI-BCI. In this study, a personalized MI prediction model based on the individual difference of event-related potential (ERP) is proposed to solve the MI individual difference.Approach.A novel paradigm named action observation-based multi-delayed matching posture task evokes ERP during a delayed matching posture task phase by retrieving picture stimuli and videos, and generates MI electroencephalogram through action observation and autonomous imagery in an action observation-based motor imagery phase. Based on the correlation between the ERP and MI, a logistic regression-based personalized MI prediction model is built to predict each individual's suitable MI action. 32 subjects conducted the MI task with or without the help of the prediction model to select the MI action. Then classification accuracy of the MI task is used to evaluate the proposed model and three traditional MI methods.Main results.The personalized MI prediction model successfully predicts suitable action among 3 sets of daily actions. Under suitable MI action, the individual's ERP amplitude and event-related desynchronization (ERD) intensity are the largest, which helps to improve the accuracy by 14.25%.Significance.The personalized MI prediction model that uses the temporal ERP features to predict the classification accuracy of MI is feasible for improving the individual's MI-BCI performance, providing a new personalized solution for the individual difference and practical BCI application.

Transcranial magneto-acoustic stimulation improves spatial memory and modulates hippocampal neural oscillations in a mouse model of Alzheimer's disease.

Introduction: In our study, we applied transcranial magneto-acoustic stimulation (TMAS), a technique based on focused ultrasound stimulation within a static magnetic field, in the APP/PS1 mouse model of Alzheimer's disease (AD) to explore the feasibility of TMAS on improving AD related spatial memory deficits and abnormal neural oscillations. Methods: The mice treated with TMAS once daily for 21 days. We recorded local field potential signals in the hippocampal CA1 region of the mice after TMAS treatment with in-vivo electrophysiology and evaluated the neural rehabilitative effect of TMAS with sharp-wave ripple (SWR), gamma oscillations during SWRs, and phase-amplitude coupling (PAC). The spatial memory function of the mice was examined by the Morris water maze (MWM) task. Results: We found that TMAS improved the performance of MWM related spatial cognitive functions compared with AD group. Furthermore, our results implied that TMAS alleviated abnormalities in hippocampal SWRs, increased slow gamma power during SWRs, and promoted theta-slow gamma phase-amplitude coupling. These findings suggest that TMAS could have a positive influence on spatial memory through the modulation of neural oscillations. Discussion: This work emphasizes the potential of TMAS to serve as a non-invasive method for Alzheimer's disease rehabilitation and promote the application of TMAS for the treatment of more neurological and brain aging diseases in the future.

EEGNet-based multi-source domain filter for BCI transfer learning.

Deep learning has great potential on decoding EEG in brain-computer interface. While common deep learning algorithms cannot directly train models with data from multiple individuals because of the inter-individual differences in EEG. Collecting enough data for each subject to satisfy the training of deep learning would result in an increase in training cost. This study proposes a novel transfer learning, EEGNet-based multi-source domain filter for transfer learning (EEGNet-MDFTL), to reduce the amount of training data and improve the performance of BCI. The EEGNet-MDFTL uses bagging ensemble learning to learn domain-invariant features from the multi-source domain and utilizes model loss value to filter the multi-source domain. Compared with baseline methods, the accuracy of the EEGNet-MDFTL reaches 91.96%, higher than two state-of-the-art methods, which demonstrates source domain filter can select similar source domains to improve the accuracy of the model, and remains a high level even when the data amount is reduced to 1/8, proving that ensemble learning learns enough domain invariant features from the multi-source domain to make the model insensitive to data amount. The proposed EEGNet-MDFTL is effective in improving the decoding performance with a small amount of data, which is helpful to save the BCI training cost.

Antibacterial efficiency of the curcumin-mediated photodynamic inactivation coupled with L-arginine against Vibrio parahaemolyticus and its application on shrimp.

The aim of this study was to investigate the antibacterial potency of a novel photodynamic inactivation (PDI) system with an enhanced bactericidal ability against Vibrio parahaemolyticus in vitro and in vivo. The synergistically bactericidal action of curcumin (Cur) and L-arginine (L-Arg) was firstly investigated, and then a novel curcumin-mediated PDI coupled with L-Arg was developed. Meanwhile, its potent inactivation mechanism against V. parahaemolyticus and preservation effects on shrimp were explored. Results showed that L-Arg disrupted the cell membrane by binding to membrane phospholipids and disrupting iron homeostasis, which helped curcumin to damage DNA and interrupt protein synthesis. Once irradiated by blue LED, the curcumin-mediated PDI produced the reactive oxygen species (ROS) which reacted with L-Arg to generate NO, and the NO was converted to reactive nitrogen species (RNS) with a strong bactericidal ability by consuming ROS. On this basis, the curcumin-mediated PDI coupled with L-Arg potently killed >8.0 Log CFU/mL with 8 µM curcumin, 0.5 mg/mL L-Arg and 1.2 J/cm2 irradiation. Meanwhile, this PDI also effectively inhibited the colour and pH changes, lipids oxidation and protein degradation of shrimp. Therefore, this study proposes a new potent PDI system to control microbial contamination in the food industry.

[Study on effects of 40 Hz light flicker stimulation on spatial working memory in rats and its neural mechanism].

Alzheimer's disease (AD) is a neurodegenerative disease characterized by cognitive impairment, with the predominant clinical diagnosis of spatial working memory (SWM) deficiency, which seriously affects the physical and mental health of patients. However, the current pharmacological therapies have unsatisfactory cure rates and other problems, so non-pharmacological physical therapies have gradually received widespread attention. Recently, a novel treatment using 40 Hz light flicker stimulation (40 Hz-LFS) to rescue the cognitive function of model animals with AD has made initial progress, but the neurophysiological mechanism remains unclear. Therefore, this paper will explore the potential neural mechanisms underlying the modulation of SWM by 40 Hz-LFS based on cross-frequency coupling (CFC). Ten adult Wistar rats were first subjected to acute LFS at frequencies of 20, 40, and 60 Hz. The entrainment effect of LFS with different frequency on neural oscillations in the hippocampus (HPC) and medial prefrontal cortex (mPFC) was analyzed. The results showed that acute 40 Hz-LFS was able to develop strong entrainment and significantly modulate the oscillation power of the low-frequency gamma (lγ) rhythms. The rats were then randomly divided into experimental and control groups of 5 rats each for a long-term 40 Hz-LFS (7 d). Their SWM function was assessed by a T-maze task, and the CFC changes in the HPC-mPFC circuit were analyzed by phase-amplitude coupling (PAC). The results showed that the behavioral performance of the experimental group was improved and the PAC of θ-lγ rhythm was enhanced, and the difference was statistically significant. The results of this paper suggested that the long-term 40 Hz-LFS effectively improved SWM function in rats, which may be attributed to its enhanced communication of different rhythmic oscillations in the relevant neural circuits. It is expected that the study in this paper will build a foundation for further research on the mechanism of 40 Hz-LFS to improve cognitive function and promote its clinical application in the future.

[Effects of 50 Hz electromagnetic field on rat working memory and investigation of neural mechanisms].

With the widespread use of electrical equipment, cognitive functions such as working memory (WM) could be severely affected when people are exposed to 50 Hz electromagnetic fields (EMF) for long term. However, the effects of EMF exposure on WM and its neural mechanism remain unclear. In the present paper, 15 rats were randomly assigned to three groups, and exposed to an EMF environment at 50 Hz and 2 mT for a different duration: 0 days (control group), 24 days (experimental group I), and 48 days (experimental group II). Then, their WM function was assessed by the T-maze task. Besides, their local field potential (LFP) in the media prefrontal cortex (mPFC) was recorded by the in vivo multichannel electrophysiological recording system to study the power spectral density (PSD) of θ and γ oscillations and the phase-amplitude coupling (PAC) intensity of θ-γ oscillations during the T-maze task. The results showed that the PSD of θ and γ oscillations decreased in experimental groups I and II, and the PAC intensity between θ and high-frequency γ (hγ) decreased significantly compared to the control group. The number of days needed to meet the task criterion was more in experimental groups I and II than that of control group. The results indicate that long-term exposure to EMF could impair WM function. The possible reason may be the impaired communication between different rhythmic oscillations caused by a decrease in θ-hγ PAC intensity. This paper demonstrates the negative effects of EMF on WM and reveals the potential neural mechanisms from the changes of PAC intensity, which provides important support for further investigation of the biological effects of EMF and its mechanisms.

A study of brain networks associated with Freezing of gait in Parkinson's disease using transfer entropy analysis.

BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disease in the elderly. Freezing of Gait (FOG) is one of the common motor symptoms of PD, but the potential mechanism remains unclear. This study aimed to investigate the changes of brain functional network topology in PD patients with FOG. METHODS: The resting electroencephalogram (EEG) were acquired from15 PD patients with FOG (PD-FOG), 13 PD patients without FOG (PD-nFOG), and 16 healthy control (HC). Cognitive and motor functions were assessed using subjective scales. The whole-brain functional networks were constructed based on transfer entropy. Transfer entropy was used to analyse the information flow and causality in the network and the network connectivity was analyzed by graph theory. The characteristics of PD-FOG and PD-nFOG were compared by receiver operator characteristic (ROC) curve analysis. RESULTS: The θ bands brain network of PD-FOG, PD-nFOG and HC group was significantly different (P < 0.05). The average characteristic path length of the θ bands brain network was positively correlated with FOG Questionnaire (FOGQ). PD-FOG and PD-nFOG get high classification accuracy according to this feature. The information inflow in the frontal and occipital lobes and information outflow in the temporal lobe of PD-FOG patients in the θ bands increased significantly. CONCLUSIONS: The whole-brain functional network characteristics of PD-FOG in the θ bands can serve as potential biomarkers for early diagnosis of PD-FOG. Abnormal information flow of the frontal, occipital, and temporal lobes in the θ bands may be an important factor leading to FOG.

An Integrated Multi-Channel Deep Neural Network for Mesial Temporal Lobe Epilepsy Identification Using Multi-Modal Medical Data.

Epilepsy is a chronic brain disease with recurrent seizures. Mesial temporal lobe epilepsy (MTLE) is the most common pathological cause of epilepsy. With the development of computer-aided diagnosis technology, there are many auxiliary diagnostic approaches based on deep learning algorithms. However, the causes of epilepsy are complex, and distinguishing different types of epilepsy accurately is challenging with a single mode of examination. In this study, our aim is to assess the combination of multi-modal epilepsy medical information from structural MRI, PET image, typical clinical symptoms and personal demographic and cognitive data (PDC) by adopting a multi-channel 3D deep convolutional neural network and pre-training PET images. The results show better diagnosis accuracy than using one single type of medical data alone. These findings reveal the potential of a deep neural network in multi-modal medical data fusion.

[The inverse stochastic resonance in a small-world neuronal network under electromagnetic stimulation].

Electromagnetic stimulation is an important neuromodulation technique that modulates the electrical activity of neurons and affects cortical excitability for the purpose of modulating the nervous system. The phenomenon of inverse stochastic resonance is a response mechanism of the biological nervous system to external signals and plays an important role in the signal processing of the nervous system. In this paper, a small-world neural network with electrical synaptic connections was constructed, and the inverse stochastic resonance of the small-world neural network under electromagnetic stimulation was investigated by analyzing the dynamics of the neural network. The results showed that: the Levy channel noise under electromagnetic stimulation could cause the occurrence of inverse stochastic resonance in small-world neural networks; the characteristic index and location parameter of the noise had significant effects on the intensity and duration of the inverse stochastic resonance in neural networks; the larger the probability of randomly adding edges and the number of nearest neighbor nodes in small-world networks, the more favorable the anti-stochastic resonance was; by adjusting the electromagnetic stimulation parameters, a dual regulation of the inverse stochastic resonance of the neural network can be achieved. The results of this study provide some theoretical support for exploring the regulation mechanism of electromagnetic nerve stimulation technology and the signal processing mechanism of nervous system.

Effects of high-frequency transcranial magnetic stimulation on theta-gamma oscillations and coupling in the prefrontal cortex of rats during working memory task.

High-frequency rTMS has been widely used to improve working memory (WM) impairment; however, the underlying neurophysiological mechanisms are unclear. We evaluated the effect of high-frequency rTMS on behaviors relevant to WM as well as coupling between theta and gamma oscillations in the prefrontal cortex (PFC) of rats. Accordingly, Wistar rats received high-frequency rTMS daily for 14 days (5 Hz, 10 Hz, and 15 Hz stimulation; 600 pulses; n = 6 per group), whereas the control group received sham stimulation. Electrophysiological signals were recorded simultaneously to obtain the local field potential (LFP) from the PFC, while the rats performed T-maze tasks for the evaluation of WM. Phase-amplitude coupling (PAC) was utilized to determine the effect of high-frequency rTMS on the theta-gamma coupling of LFPs. We observed that rats in the rTMS groups needed a smaller number of training days to complete the WM task as compared to the control group. High-frequency rTMS reinforced the coupling connection strength in the PFC of rats. Notably, the effect of rTMS at 15 Hz was the most effective among the three frequencies, i.e., 5 Hz, 10 Hz, and 15 Hz. The results suggested that rTMS can improve WM impairment in rats by modulating the coupling of theta and gamma rhythms. Hence, the current study provides a scientific basis for the optimization of TMS models, which would be relevant for clinical application.

Cortical functional networks of transcutaneous electrical stimulation at acupoints on the pericardial meridian.

To explore the relationship between pericardial meridian acupoints and brain, the electroencephalogram (EEG) signals were collected synchronously during transcutaneous electrical stimulation at PC3, PC5, PC7, and PC8 on the pericardial meridian in 21 healthy subjects. The cerebral cortex functional networks were constructed by standard low-resolution electromagnetic tomography (sLORETA), phase-locking value (PLV) and complex network methods. The prefrontal cortex (BA10), the orbitofrontal cortex (BA11), the middle temporal gyrus (BA21), the temporal gyrus (BA22), the temporal pole (BA38), the triangular part (BA44), the dorsolateral prefrontal cortex (BA46), and the inferior frontal cortex (BA47) were activated by electrical stimulation at PC3, PC5, PC7, and PC8 on the pericardium meridian. These activated brain regions are able to modulate both local and remote emotion and cognitive networks. Acupoint stimulation of pericardium meridian mainly activated the frontal and the temporal lobes. Compared with non-acupoint stimulation, the node degree in the frontal lobe of electrical stimulation at PC3 (p < 0.05), PC5 (p < 0.05), PC7 (p < 0.01), PC8 (p < 0.05) and the temporal lobe of PC3 (p < 0.05), PC5 (p < 0.05), PC7 (p < 0.05), PC8 (p < 0.01) were significantly increased. The clustering coefficient in the frontal lobe of the stimulation at PC3 (p < 0.05), PC5 (p < 0.05), PC7 (p < 0.01), PC8 (p < 0.05) and the temporal lobe of PC3 (p < 0.05), PC5 (p < 0.05), PC7 (p < 0.01), PC8 (p < 0.05) were significantly increased. The characteristic path length decreased and the global efficiency increased during acupoint stimulation. The changes of functional network of stimulated pericardium meridian through cerebral cortex may provide theoretical support for the specificity of meridian and acupoints.

Dual stimuli interface with logical division using local move stimuli.

Improving information transfer rate is a key to prompt the speed of outputting instructions of the event-related potential-based brain-computer interface. Our previous study designed a dual-stimuli interface that simultaneously presents two types of different stimuli to improve the speed. While, adding more stimuli into this interface makes subject easily affected by "flanker effect" that decreases the accuracy of recognizing intention. To achieve high recognition accuracy with many stimuli, this study proposes a dual stimuli interface based on whole flash and local move (DS-WL) and two rules of stimulus arrangement to induce the brain signals. Twenty subjects participated in the experiment, and their signals are recognized by a back propagation neural network classifier. The local move induces larger and later signals of targets to help discriminate the two kinds of stimuli; the rules reduce the N200 and P300 amplitudes of non-target, which improves accuracy. This study demonstrates that the DS-WL is a useful way to shorten the instruction output cycle and speed up the instructions outputting by local move and rules.

Identification of DNA Damage Repair Gene Signature as a Novel Prognostic Marker in Glioblastoma Multiforme.

BACKGROUND: The efficacy of treatment of glioblastoma multiforme (GBM) is limited. The effect of DNA damage repair is an important factor. METHODS: Expression data were downloaded from The Cancer Genome Atlas (training dataset) and the Gene Expression Omnibus (validation dataset) databases. Univariate Cox regression analysis and the least absolute shrinkage and selection operator were used to construct a DNA damage response (DDR) gene signature. Receiver operating characteristic curve analysis and Kaplan-Meier curve analysis were used to estimate the prognostic value of the risk signature. Moreover, consensus clustering analysis was used to investigate the potential subtypes of GBM according to DDR expression. RESULTS: We constructed a 3-DDR-related gene signature through the survival analysis. The Kaplan-Meier curve analysis suggested that patients in the low-risk group have significantly better survival outcomes compared with the high-risk group in the training and external validation datasets. The results from the receiver operating characteristic curve analysis indicated that the risk model has high prognostic value in the training and external validation datasets. Moreover, 3 stable molecular subtypes were identified and validated in the Gene Expression Omnibus and The Cancer Genome Atlas databases according to the expression of the DNA repair gene. The microenvironment and immunity of GBM were further investigated and showed that cluster 2 had higher immunity and a higher immune score compared with clusters 1 and 3. CONCLUSIONS: The DNA damage repair-related gene signature was an independent and powerful prognostic biomarker in GBM. Knowledge of the GBM subtypes could have important implications in the subclassification of GBM.

[Effect of electroconvulsive therapy on brain functional network in major depressive disorder].

Electroconvulsive therapy (ECT) is an interventional technique capable of highly effective neuromodulation in major depressive disorder (MDD), but its antidepressant mechanism remains unclear. By recording the resting-state electroencephalogram (RS-EEG) of 19 MDD patients before and after ECT, we analyzed the modulation effect of ECT on the resting-state brain functional network of MDD patients from multiple perspectives: estimating spontaneous EEG activity power spectral density (PSD) using Welch algorithm; constructing brain functional network based on imaginary part coherence (iCoh) and calculate functional connectivity; using minimum spanning tree theory to explore the topological characteristics of brain functional network. The results show that PSD, functional connectivity, and topology in multiple frequency bands were significantly changed after ECT in MDD patients. The results of this study reveal that ECT changes the brain activity of MDD patients, which provides an important reference in the clinical treatment and mechanism analysis of MDD.

Effects of theta burst stimulation on the coherence of local field potential during working memory task in rats.

Theta burst stimulation (TBS), a highly efficient repetitive transcranial magnetic stimulation (rTMS) paradigm, has been widely used to modulate the working memory (WM) ability in experimental and clinical study. However, the underly neuroelectrophysiological mechanism remains unclear. The aim of this study was to compare the effect of iTBS, cTBS and rTMS on WM and explore the neural oscillatory communication changes in PFC involved in spatial WM task. 18 rats were treated by iTBS, cTBS and rTMS respectively (n = 6 each), while the rats in control group (n = 6) received no stimulation. T-maze WM task was used to assess the rats' performance of WM after stimulation. Local field potentials (LFPs) were recorded from a microelectrode array implanted in the medial prefrontal cortex (mPFC) while the rats were performing the WM task. Functional connectivity (FC) strength was assessed by LFP-LFP coherence calculations. The results showed that the rats from the rTMS group and iTBS group are able to reach criteria in less time than the control group's duration of the T-maze task. The power and the coherence value of rTMS and iTBS groups show a significant increase in the theta-band and gamma-band activity, wheras there are no significant differences of the energy and the coherence value between the cTBS group and the control group in theta-band. Furthermore, significantly positive correlations were observed between changes of memory performance during the WM task and the changes of the coherence value of the LFPs. In conclusion, these results indicate that rTMS and iTBS may improve the ability of WM by modulating the neural activity and connectivity in PFC.

Anti-Disturbance of Scale-Free Spiking Neural Network against Impulse Noise.

The bio-brain presents robustness function to external stimulus through its self-adaptive regulation and neural information processing. Drawing from the advantages of the bio-brain to investigate the robustness function of a spiking neural network (SNN) is conducive to the advance of brain-like intelligence. However, the current brain-like model is insufficient in biological rationality. In addition, its evaluation method for anti-disturbance performance is inadequate. To explore the self-adaptive regulation performance of a brain-like model with more biological rationality under external noise, a scale-free spiking neural network(SFSNN) is constructed in this study. Then, the anti-disturbance ability of the SFSNN against impulse noise is investigated, and the anti-disturbance mechanism is further discussed. Our simulation results indicate that: (i) our SFSNN has anti-disturbance ability against impulse noise, and the high-clustering SFSNN outperforms the low-clustering SFSNN in terms of anti-disturbance performance. (ii) The neural information processing in the SFSNN under external noise is clarified, which is a dynamic chain effect of the neuron firing, the synaptic weight, and the topological characteristic. (iii) Our discussion hints that an intrinsic factor of the anti-disturbance ability is the synaptic plasticity, and the network topology is a factor that affects the anti-disturbance ability at the level of performance.

[A research on epilepsy source localization from scalp electroencephalograph based on patient-specific head model and multi-dipole model].

Accurate source localization of the epileptogenic zone (EZ) is the primary condition of surgical removal of EZ. The traditional localization results based on three-dimensional ball model or standard head model may cause errors. This study intended to localize the EZ by using the patient-specific head model and multi-dipole algorithms using spikes during sleep. Then the current density distribution on the cortex was computed and used to construct the phase transfer entropy functional connectivity network between different brain areas to obtain the localization of EZ. The experiment result showed that our improved methods could reach the accuracy of 89.27% and the number of implanted electrodes could be reduced by (19.34 ± 7.15)%. This work can not only improve the accuracy of EZ localization, but also reduce the additional injury and potential risk caused by preoperative examination and surgical operation, and provide a more intuitive and effective reference for neurosurgeons to make surgical plans.

Optimal Design of Array Coils for Multi-Target Adjustable Electromagnetic Brain Stimulation System.

Temporal interference magnetic stimulation is a novel noninvasive deep brain neuromodulation technology that can solve the problem of balance between focus area and stimulation depth. However, at present, the stimulation target of this technology is relatively single, and it is difficult to realize the coordinated stimulation of multiple brain regions, which limits its application in the modulation of multiple nodes in the brain network. This paper first proposes a multi-target temporal interference magnetic stimulation system with array coils. The array coils are composed of seven coil units with an outer radius of 25 mm, and the spacing between coil units is 2 mm. Secondly, models of human tissue fluid and the human brain sphere are established. Finally, the relationship between the movement of the focus area and the amplitude ratio of the difference frequency excitation sources under time interference is discussed. The results show that in the case of a ratio of 1:5, the peak position of the amplitude modulation intensity of the induced electric field has moved 45 mm; that is, the movement of the focus area is related to the amplitude ratio of the difference frequency excitation sources. The conclusion is that multi-target temporal interference magnetic stimulation with array coils can simultaneously stimulate multiple network nodes in the brain region; rough positioning can be performed by controlling the conduction of different coils, fine-tuning the position by changing the current ratio of the conduction coils, and realizing accurate stimulation of multiple targets in the brain area.