Here, we administered repeated-pulse transcranial magnetic stimulation to healthy people at the left Guangming （GB37） and a mock point, and calculated the sample entropy of electroencephalo- gram signals using nonli...Here, we administered repeated-pulse transcranial magnetic stimulation to healthy people at the left Guangming （GB37） and a mock point, and calculated the sample entropy of electroencephalo- gram signals using nonlinear dynamics. Additionally, we compared electroencephalogram sample entropy of signals in response to visual stimulation before, during, and after repeated-pulse tran- scranial magnetic stimulation at the Guangming. Results showed that electroencephalogram sample entropy at left （F3） and right （FP2） frontal electrodes were significantly different depending on where the magnetic stimulation was administered. Additionally, compared with the mock point, electroencephalogram sample entropy was higher after stimulating the Guangming point. When visual stimulation at Guangming was given before repeated-pulse transcranial magnetic stimula- tion, significant differences in sample entropy were found at five electrodes （C3, Cz, C4, P3, T8） in parietal cortex, the central gyrus, and the right temporal region compared with when it was given after repeated-pulse transcranial magnetic stimulation, indicating that repeated-pulse transcranial magnetic stimulation at Guangming can affect visual function. Analysis of electroencephalogram revealed that when visual stimulation preceded repeated pulse transcranial magnetic stimulation, sample entropy values were higher at the C3, C4, and P3 electrodes and lower at the Cz and T8 electrodes than visual stimulation followed preceded repeated pulse transcranial magnetic stimula- tion. The findings indicate that repeated-pulse transcranial magnetic stimulation at the Guangming evokes different patterns of electroencephalogram signals than repeated-pulse transcranial mag- netic stimulation at other nearby points on the body surface, and that repeated-pulse transcranial magnetic stimulation at the Guangrning is associated with changes in the complexity of visually evoked electroencephalogram signals in parietal regions, central gyrus, and temporal regions.展开更多
The precise classification for the electroencephalogram(EEG)in different mental tasks in the research on braincomputer interface(BCI)is the key for the design and clinical application of the system.In this paper,a ne...The precise classification for the electroencephalogram(EEG)in different mental tasks in the research on braincomputer interface(BCI)is the key for the design and clinical application of the system.In this paper,a new combination classification algorithm is presented and tested using the EEG data of right and left motor imagery experiments.First,to eliminate the low frequency noise in the original EEGs,the signals were decomposed by empirical mode decomposition(EMD)and then the optimal kernel parameters for support vector machine(SVM)were determined,the energy features of thefirst three intrinsic mode functions(IMFs)of every signal were extracted and used as input vectors of the employed SVM.The output of the SVM will be classification result for different mental task EEG signals.The study shows that mean identification rate of the proposed algorithm is 95%,which is much better than the present traditional algorithms.展开更多
Today,electroencephalography is used to measure brain activity by creating signals that are viewed on a monitor.These signals are frequently used to obtain information about brain neurons and may detect disorders that...Today,electroencephalography is used to measure brain activity by creating signals that are viewed on a monitor.These signals are frequently used to obtain information about brain neurons and may detect disorders that affect the brain,such as epilepsy.Electroencephalogram(EEG)signals are however prone to artefacts.These artefacts must be removed to obtain accurate and meaningful signals.Currently,computer-aided systems have been used for this purpose.These systems provide high computing power,problem-specific development,and other advantages.In this study,a new clinical decision support system was developed for individuals to detect epileptic seizures using EEG signals.Comprehensive classification results were obtained for the extracted filtered features from the time-frequency domain.The classification accuracies of the time-frequency features obtained from discrete continuous transform(DCT),fractional Fourier transform(FrFT),and Hilbert transform(HT)are compared.Artificial neural networks(ANN)were applied,and back propagation(BP)was used as a learning method.Many studies in the literature describe a single BP algorithm.In contrast,we looked at several BP algorithms including gradient descent with momentum(GDM),scaled conjugate gradient(SCG),and gradient descent with adaptive learning rate(GDA).The most successful algorithm was tested using simulations made on three separate datasets(DCT_EEG,FrFT_EEG,and HT_EEG)that make up the input data.The HT algorithm was the most successful EEG feature extractor in terms of classification accuracy rates in each EEG dataset and had the highest referred accuracy rates of the algorithms.As a result,HT_EEG gives the highest accuracy for all algorithms,and the highest accuracy of 87.38%was produced by the SCG algorithm.展开更多
Since the advent of imaging studies such as magnetic resonance imaging (MRI), the role of electroencephalograms (EEGs) has diminished. Simultaneously, computerized scanning and miniaturization of the EEG and its compo...Since the advent of imaging studies such as magnetic resonance imaging (MRI), the role of electroencephalograms (EEGs) has diminished. Simultaneously, computerized scanning and miniaturization of the EEG and its components have allowed us to obtain lengthier recordings in an ambulatory setting. We report on 261 ambulatory electroencephalograms performed consecutively in the two year period of 2011 and 2012 in a busy neurology and neuropsychiatry practice with predominantly geriatric patient population. 23% of these patients had abnormal AEEGs demonstrating clear-cut epileptogenic discharges. The role of these findings in clinical practice, especially in geriatric and psychiatric populations is discussed.展开更多
Since the advent of imaging studies such as magnetic resonance imaging (MRI), the role of electroencephalograms (EEGs) has diminished. Simultaneously, computerized scanning and miniaturization of the EEG and its compo...Since the advent of imaging studies such as magnetic resonance imaging (MRI), the role of electroencephalograms (EEGs) has diminished. Simultaneously, computerized scanning and miniaturization of the EEG and its components have allowed us to obtain lengthier recordings in an ambulatory setting. We report on 261 ambulatory electroencephalograms performed consecutively in the two year period of 2011 and 2012 in a busy neurology and neuropsychiatry practice with predominantly geriatric patient population. 23% of these patients had abnormal AEEGs demonstrating clear-cut epileptogenic discharges. The role of these findings in clinical practice, especially in geriatric and psychiatric populations is discussed.展开更多
During his first year as an entrepreneur,Han Bicheng washed his hair roughly three times a day-more than 800 times in a single year.He and his team had to repeatedly apply conductive gel to their scalps to capture the...During his first year as an entrepreneur,Han Bicheng washed his hair roughly three times a day-more than 800 times in a single year.He and his team had to repeatedly apply conductive gel to their scalps to capture the clean electroencephalogram signals needed for their experiments,a routine that kept them in a near-constant cycle of shampooing.展开更多
General anesthesia,pivotal for surgical procedures,requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments.Traditional assessment methods,relyin...General anesthesia,pivotal for surgical procedures,requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments.Traditional assessment methods,relying on physiological indicators or behavioral responses,fall short of accurately capturing the nuanced states of unconsciousness.This study introduces a machine learning-based approach to decode anesthesia depth,leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats.Our findings demonstrate the model’s robust predictive accuracy,underscored by a novel intrasubject dataset partitioning and a 5-fold cross-validation method.The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states,highlighting distinct EEG patterns and enhancing prediction accuracy.Moreover,the model’s ability to generalize across individuals suggests its potential for broad clinical application,distinguishing between anesthetic agents and their depths.Despite relying on rat EEG data,which poses questions about real-world applicability,our approach marks a significant advance in anesthesia monitoring.展开更多
Attempts have been made to modulate motor sequence learning(MSL)through repetitive transcranial magnetic stimulation,targeting different sites within the sensorimotor network.However,the target with the optimum modula...Attempts have been made to modulate motor sequence learning(MSL)through repetitive transcranial magnetic stimulation,targeting different sites within the sensorimotor network.However,the target with the optimum modulatory effect on neural plasticity associated with MSL remains unclarified.This study was therefore designed to compare the role of the left primary motor cortex and the left supplementary motor area proper(SMAp)in modulating MSL across different complexity levels and for both hands,as well as the associated neuroplasticity by applying intermittent theta burst stimulation together with the electroencephalogram and concurrent transcranial magnetic stimulation.Our data demonstrated the role of SMAp stimulation in modulating neural communication to support MSL,which is achieved by facilitating regional activation and orchestrating neural coupling across distributed brain regions,particularly in interhemispheric connections.These findings may have important clinical implications,particularly for motor rehabilitation in populations such as post-stroke patients.展开更多
Objective Repetitive transcranial magnetic stimulation(rTMS)has demonstrated efficacy in enhancing neurocognitive performance in Alzheimer’s disease(AD),but the neurobiological mechanisms linking synaptic pathology,n...Objective Repetitive transcranial magnetic stimulation(rTMS)has demonstrated efficacy in enhancing neurocognitive performance in Alzheimer’s disease(AD),but the neurobiological mechanisms linking synaptic pathology,neural oscillatory dynamics,and brain network reorganization remain unclear.This investigation seeks to systematically evaluate the therapeutic potential of rTMS as a non-invasive neuromodulatory intervention through a multimodal framework integrating clinical assessments,molecular profiling,and neurophysiological monitoring.Methods In this prospective double-blind trial,12 AD patients underwent a 14-day protocol of 20 Hz rTMS,with comprehensive multimodal assessments performed pre-and postintervention.Cognitive functioning was quantified using the mini-mental state examination(MMSE)and Montreal cognitive assessment(MOCA),while daily living capacities and neuropsychiatric profiles were respectively evaluated through the activities of daily living(ADL)scale and combined neuropsychiatric inventory(NPI)-Hamilton depression rating scale(HAMD).Peripheral blood biomarkers,specifically Aβ1-40 and phosphorylated tau(p-tau181),were analyzed to investigate the effects of rTMS on molecular metabolism.Spectral power analysis was employed to investigate rTMS-induced modulations of neural rhythms in AD patients,while brain network analyses incorporating topological properties were conducted to examine stimulus-driven network reorganization.Furthermore,systematic assessment of correlations between cognitive scale scores,blood biomarkers,and network characteristics was performed to elucidate cross-modal therapeutic associations.Results Clinically,MMSE and MOCA scores improved significantly(P<0.05).Biomarker showed that Aβ1-40 level increased(P<0.05),contrasting with p-tau181 reduction.Moreover,the levels of Aβ1-40 were positively correlated with MMSE and MOCA scores.Post-intervention analyses revealed significant modulations in oscillatory power,characterized by pronounced reductions in delta(P<0.05)and theta bands(P<0.05),while concurrent enhancements were observed in alpha,beta,and gamma band activities(all P<0.05).Network analysis revealed frequency-specific reorganization:clustering coefficients were significantly decreased in delta,theta,and alpha bands(P<0.05),while global efficiency improvement was exclusively detected in the delta band(P<0.05).The alpha band demonstrated concurrent increases in average nodal degree(P<0.05)and characteristic path length reduction(P<0.05).Further research findings indicate that the changes in the clinical scale HAMD scores before and after rTMS stimulation are negatively correlated with the changes in the blood biomarkers Aβ1-40 and p-tau181.Additionally,the changes in the clinical scales MMSE and MoCA scores were negatively correlated with the changes in the node degree of the alpha frequency band and negatively correlated with the clustering coefficient of the delta frequency band.However,the changes in MMSE scores are positively correlated with the changes in global efficiency of both the delta and alpha frequency bands.Conclusion 20 Hz rTMS targeting dorsolateral prefrontal cortex(DLPFC)significantly improves cognitive function and enhances the metabolic clearance ofβ-amyloid and tau proteins in AD patients.This neurotherapeutic effect is mechanistically associated with rTMS-mediated frequency-selective neuromodulation,which enhances the connectivity of oscillatory networks through improved neuronal synchronization and optimized topological organization of functional brain networks.These findings not only support the efficacy of rTMS as an adjunctive therapy for AD but also underscore the importance of employing multiple assessment methods—including clinical scales,blood biomarkers,and EEG——in understanding and monitoring the progression of AD.This research provides a significant theoretical foundation and empirical evidence for further exploration of rTMS applications in AD treatment.展开更多
The global incidence of Alzheimer's Disease(AD)is on a swift rise.The Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using ma...The global incidence of Alzheimer's Disease(AD)is on a swift rise.The Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using machine learning models.Analysis of AD using EEG involves multi-channel analysis.However,the use of multiple channels may impact the classification performance due to data redundancy and complexity.In this work,a hybrid EEG channel selection is proposed using a combination of Reptile Search Algorithm and Snake Optimizer(RSO)for AD and MCI detection based on decomposition methods.Empirical Mode Decomposition(EMD),Low-Complexity Orthogonal Wavelet Filter Banks(LCOWFB),Variational Mode Decomposition,and discrete-wavelet transform decomposition techniques have been employed for subbands-based EEG analysis.We extracted thirty-four features from each subband of EEG signals.Finally,a hybrid RSO optimizer is compared with five individual metaheuristic algorithms for effective channel selection.The effectiveness of this model is assessed by two publicly accessible AD EEG datasets.An accuracy of 99.22% was achieved for binary classification from RSO with EMD using 4(out of 16)EEG channels.Moreover,the RSO with LCOWFBs obtained 89.68%the average accuracy for three-class classification using 7(out of 19)channels.The performance reveals that RSO performs better than individual Metaheuristic algorithms with 60%fewer channels and improved accuracy of 4%than existing AD detection techniques.展开更多
Abuse of amphetamine-based stimulants is a primary public health concern.Recent studies have underscored a troubling escalation in the inappropriate use of prescription amphetamine-based stimulants.However,the neuroph...Abuse of amphetamine-based stimulants is a primary public health concern.Recent studies have underscored a troubling escalation in the inappropriate use of prescription amphetamine-based stimulants.However,the neurophysiological mechanisms underlying the impact of acute methamphetamine exposure(AME)on sleep homeostasis remain to be explored.This study employed non-human primates and electroencephalogram(EEG)sleep staging to evaluate the influence of AME on neural oscillations.The primary focus was on alterations in spindles,delta oscillations,and slow oscillations(SOs)and their interactions as conduits through which AME influences sleep stability.AME predominantly diminishes sleep-spindle waves in the non-rapid eye movement 2(NREM2)stage,and impacts SOs and delta waves differentially.Furthermore,the competitive relationships between SO/delta waves nesting with sleep spindles were selectively strengthened by methamphetamine.Complexity analysis also revealed that the SO-nested spindles had lost their ability to maintain sleep depth and stability.In summary,this finding could be one of the intrinsic electrophysiological mechanisms by which AME disrupted sleep homeostasis.展开更多
BACKGROUND Autism spectrum disorder(ASD)is a neurodevelopmental disorder characterized by difficulties in social communication,restricted interests,and repetitive stereotyped behaviors.In recent years,the prevalence o...BACKGROUND Autism spectrum disorder(ASD)is a neurodevelopmental disorder characterized by difficulties in social communication,restricted interests,and repetitive stereotyped behaviors.In recent years,the prevalence of ASD has continued to rise,with boys having a significantly higher incidence rate than girls.Children with ASD often have intellectual and language impairments,which seriously affect their social skills,emotional regulation,and daily life.Although traditional treatment methods have shown some effectiveness,they still have limitations in addressing social and emotional regulation.Neurobiofeedback therapy is a noninvasive,drug-free treatment method that helps individuals regulate physiological responses through feedback mechanisms,and it has shown potential in various psychological disorders and emotional regulation.However,there is limited research on the social skills and emotional regulation in children with ASD.Therefore,this study aims to explore the impact of neurobiofeedback technology on children with ASD through a retrospective cohort study,supplementing existing treatment methods and promoting a more comprehensive treatment of ASD.AIM To investigate the effects of neurobiofeedback therapy on social skills and emotional regulation in children with ASD.METHODS A retrospective study was conducted on 92 children with ASD who were admitted to our hospital from January 2023 to June 2024.According to their different trea-tment plans,they were divided into a conventional group(conventional rehabilitation treatment;n=43)and a combined group(conventional rehabilitation treatment combined with neurobiofeedback therapy;n=49).The general characteristics,Aberrant Behavior Checklist scores,Chinese version of the Psycho-Educational Profile,Third Edition scores,Social Responsiveness Scale scores,Emotion Regulation Checklist scores,Social Communication Questionnaire scores,and the incidence of adverse reactions were compared between groups.RESULTS After intervention,the Aberrant Behavior Checklist and Social Responsiveness Scale scores of the combined group were lower than those of the conventional group.In contrast,scores on the Chinese version of the Psycho-Educational Profile,Third Edition,Emotion Regulation Checklist,and Social Communication Questionnaire were significantly higher in the combined group than in the conventional group(all P<0.05).There was no significant difference in the incidence of adverse reactions between the two groups.CONCLUSION Neurobiofeedback therapy can effectively improve clinical symptoms,emotional regulation,and social skills in children with ASD.展开更多
BACKGROUND Mild cognitive impairment(MCI)is a high-risk precursor to Alzheimer’s disease characterized by declining memory or other progressive cognitive functions without compromising daily living abilities.AIM To i...BACKGROUND Mild cognitive impairment(MCI)is a high-risk precursor to Alzheimer’s disease characterized by declining memory or other progressive cognitive functions without compromising daily living abilities.AIM To investigate the efficacy of repetitive transcranial magnetic stimulation(rTMS)in patients with MCI.METHODS This retrospective analysis involved 180 patients with MCI who were admitted to The First Hospital of Shanxi Medical University from January 2021 to June 2023.Participants were allocated into the research(n=98,receiving rTMS)and control groups(n=82,receiving sham stimulation).Memory tests,cognitive function assessments,event-related potential–P300 tests,and electroencephalogram(EEG)examinations were conducted pre-treatment and post-treatment.Further,memory quotient(MQ),cognitive function scores,and EEG grading results were compared,along with adverse reaction incidences.RESULTS Pre-treatment MQ scores,long-term and short-term memory,as well as immediate memory scores,demonstrated no notable differences between the groups.Post-treatment,the research group exhibited significant increases in MQ scores,long-term memory,and short-term memory compared to baseline(P<0.05),with these improvements being statistically superior to those in the control group.However,immediate memory scores exhibited no significant change(P>0.05).Further,the research group demonstrated statistically better post-treatment scores on the Revised Wechsler Memory Scale than the control group.Furthermore,post-treatment P300 latency and amplitude improved significantly in the research group,surpassing the control group.EEG grading in the research group improved,and the incidence of adverse reactions was significantly lower than in the control group.CONCLUSION Patients with MCI receiving rTMS therapy demonstrated improved memory and cognitive functions and EEG grading and exhibited high safety with fewer adverse reactions.展开更多
Recent deep neural network(DNN)based blind image quality assessment(BIQA)approaches take mean opinion score(MOS)as ground-truth labels,which would lead to cross-datasets biases and limited generalization ability of th...Recent deep neural network(DNN)based blind image quality assessment(BIQA)approaches take mean opinion score(MOS)as ground-truth labels,which would lead to cross-datasets biases and limited generalization ability of the DNN-based BIQA model.This work validates the natural instability of MOS through investigating the neuropsychological characteristics inside the human visual system during quality perception.By combining persistent homology analysis with electroencephalogram(EEG),the physiologically meaningful features of the brain responses to different distortion levels are extracted.The physiological features indicate that although volunteers view exactly the same image content,their EEG features are quite varied.Based on the physiological results,we advocate treating MOS as noisy labels and optimizing the DNN based BIQA model with earlystop strategies.Experimental results on both innerdataset and cross-dataset demonstrate the superiority of our optimization approach in terms of generalization ability.展开更多
An instrumental assessment and volunteer subjective evaluation method was developed to synchronously measure the actual skin temperature and evaluate the cool sensation,conducting a quantitative analysis of the percei...An instrumental assessment and volunteer subjective evaluation method was developed to synchronously measure the actual skin temperature and evaluate the cool sensation,conducting a quantitative analysis of the perceived coolness.This method was used to evaluate the effect of a self-developed hot flash spray on reducing the skin temperature and inducing the cooling sensation of menopausal individuals.31 healthy menopausal volunteers were recruited as research subjects.Using infrared thermal imaging and electroencephalogram(EEG)measurements,the skin temperature and EEG data of the subjects’cheeks were simultaneously collected at baseline(BL)immediately after simulated hot flashes(HF),1 min(T1),3 mins(T3)and 5 min(T5)after the application of the test sample.The results showed that,compared with HF,the skin temperature of cheek was significantly reduced by 8.75%,8.75%and 6.41%at T1,T3 and T5(P<0.05),respectively.And alpha-1 value of EEG was increased significantly by 59.70%,58.44%,and 51.39%at T1,T3,and T5(P<0.05),respectively.The hot flash spray effectively reduces skin temperature while also provides subjects with a feeling of coolness,which can relieve hot flashes in menopausal women.展开更多
Physiological signals such as electroencephalogram(EEG)signals are often corrupted by artifacts during the acquisition and processing.Some of these artifacts may deteriorate the essential properties of the signal that...Physiological signals such as electroencephalogram(EEG)signals are often corrupted by artifacts during the acquisition and processing.Some of these artifacts may deteriorate the essential properties of the signal that pertains to meaningful information.Most of these artifacts occur due to the involuntary movements or actions the human does during the acquisition process.So,it is recommended to eliminate these artifacts with signal processing approaches.This paper presents two mechanisms of classification and elimination of artifacts.In the first step,a customized deep network is employed to classify clean EEG signals and artifact-included signals.The classification is performed at the feature level,where common space pattern features are extracted with convolutional layers,and these features are later classified with a support vector machine classifier.In the second stage of the work,the artifact signals are decomposed with empirical mode decomposition,and they are then eliminated with the proposed adaptive thresholding mechanism where the threshold value changes for every intrinsic mode decomposition in the iterative mechanism.展开更多
The classification of motor imagery electroencephalogram(MI-EEG)signals is one of the key challenges in brain-computer interface(BCI)technology.Existing Riemannian geometry-based methods for MI-EEG signal analysis,whi...The classification of motor imagery electroencephalogram(MI-EEG)signals is one of the key challenges in brain-computer interface(BCI)technology.Existing Riemannian geometry-based methods for MI-EEG signal analysis,which rely on a single symmetric positive definite(SPD)manifold,often provide a limited geometric structure,making it difficult to fully capture the complex geometric characteristics of the signals.To address this issue,this paper proposes an innovative classification method for MI-EEG signals based on multi-Riemannian kernel fusion features(MRKFF).This method extends the classical SPD manifold by incorporating the Gaussian SPD manifold and the Grassmann manifold,extracting more discriminative kernel features from these heterogeneous manifolds for fusion-based classification.The proposed method is validated on the OpenBMI binary classification dataset and the BCI Competition IV-2a four-class dataset,achieving average classification accuracies of 75.6%and 71.0%,with Kappa values of 0.50 and 0.61,respectively.The proposed MRKFF method provides a new perspective for the geometric analysis of MI-EEG signals,enabling a deeper understanding and analysis of the complex geometric structure of these signals,thereby achieving more accurate signal classification in BCI applications.展开更多
Deep learning methods have been widely applied in motor imagery(MI)-based brain-computer interfaces(BCI)for decoding electroencephalogram(EEG)signals.High temporal resolution and asymmetric spatial activation are fund...Deep learning methods have been widely applied in motor imagery(MI)-based brain-computer interfaces(BCI)for decoding electroencephalogram(EEG)signals.High temporal resolution and asymmetric spatial activation are fundamental properties of EEG during MI processes.However,due to the limited receptive field of convolutional kernels,traditional convolutional neural networks(CNNs)often focus only on local features,and are insufficient to cover neural processes across different frequency bands and duration scales.This limitation hinders the effective characterization of rhythmic activity changes in MI-EEG signals over time.Additionally,MI-EEG signals exhibit significant asymmetric activation between the left and right hemispheres.Traditional spatial feature extraction methods overlook the interaction between global and local regions at the spatial scale of EEG signals,resulting in inadequate spatial representation and ultimately limiting decoding accuracy.To address these limitations,in this study,a novel deep learning network that integrates multi-modal temporal features with spatially asymmetric feature modeling was proposed.The network first extracts multi-modal temporal information from EEG data channels,and then captures global and hemispheric spatial features in the spatial dimension and fuses them through an advanced fusion layer.Global dependencies are captured using a self-attention module,and a multi-scale convolutional fusion module is introduced to explore the relationships between the two types of temporal features.The fused features are classified through a classification layer to accomplish motor imagery task classification.To mitigate the issue of limited sample size,a data augmentation strategy based on signal segmentation and recombination is designed.Experimental results on the BCI Competition IV-2a(bbic-IV-2a)and BCI Competition IV-2b(bbic-IV-2a)datasets demonstrated that the proposed method achieved superior accuracy in multi-class motor imagery classification compared with existing models.On the BCI-IV-2a dataset,it attained an average classification accuracy of 84.36%,while also showing strong performance on the binary classification BCI-IV-2b dataset.These outcomes validate the capability of the proposed network to enhance MI-EEG classification accuracy.展开更多
The problem of automated seizure detection is treated using clinical electroencephalograms(EEG) and machine learning algorithms on the Temple University Hospital EEG Seizure Corpus(TUSZ).Performances on this complex d...The problem of automated seizure detection is treated using clinical electroencephalograms(EEG) and machine learning algorithms on the Temple University Hospital EEG Seizure Corpus(TUSZ).Performances on this complex data set are still not encountering expectations.The purpose of this work is to determine to what extent the use of larger amount of data can help to improve the performances.Two methods are explored:a standard partitioning on a recent and larger version of the TUSZ,and a leave-one-out approach used to increase the amount of data for the training set.XGBoost,a fast implementation of the gradient boosting classifier,is the ideal algorithm for these tasks.The performances obtained are in the range of what is reported until now in the literature with deep learning models.We give interpretation to our results by identifying the most relevant features and analyzing performances by seizure types.We show that generalized seizures tend to be far better predicted than focal ones.We also notice that some EEG channels and features are more important than others to distinguish seizure from background.展开更多
Objective To observe the effect of acupuncture stimulation of the sacral segment on the excitability of the cerebral cortex and the activity of the urinary bladder and the involvement of the cholinergic neurons in the...Objective To observe the effect of acupuncture stimulation of the sacral segment on the excitability of the cerebral cortex and the activity of the urinary bladder and the involvement of the cholinergic neurons in the laterodorsal tegmental （LDT） nucleus of the brainstem in acupuncture-induced electroencephalogram （EEG） changes. Methods A total of 109 SD rats were used in the present study. Under anesthesia （urethane）, a pair of stainless steel electrodes was separately implanted into the frontal and parietal bony sutures to record EEG. Glass microelectrodes were used to record extracellular discharges of single neuron of the LDT nucleus in the brainstem. Urinary bladder pressure was recorded through a catheter inserted in the bladder and the contraction was induced by infusion of normal saline. A filiform acupuncture needle was inserted into the sacral segment Ecorresponding to Zhongliao （中髎BL 33）] and rotated manually for 1 min. Results In 27 rats whose bladder was full of normal saline, acupuncture stimulation of the sacral region suppressed the contraction activity of the bladder, the fast EEG with lower amplitude and higher frequency tuned into slow EEG with higher amplitude and lower frequency in 6 cases （22.2%）. The inhibitory effect occurred from 45 s to 12 min after acupuncture manipulation. In 82 rats whose bladder was empty, acupuncture stimulation caused the fast EEG to turn into slow EEG in 71 cases （86.6%）. Simultaneously, LDT cholinergic neurons reduced their firing rates from （2.9±1.5） Hz to （1.2±0.6） Hz （n = 12, P〈0.05）, and the reduction of LDT neuronal discharge was earlier in time than the change of EEG. Conclusion Acupuncture stimulation of the sacral region can lower the excitability of the cerebral cortex and suppress bladder activity, which is closely associated with its resultant inhibitory effect on the electrical activity of LDT cholinergic neurons.展开更多
基金supported by the National Natural Science Foundation of China,No.31100711,51377045,31300818the Natural Science Foundation of Hebei Province,No.H2013202176
文摘Here, we administered repeated-pulse transcranial magnetic stimulation to healthy people at the left Guangming （GB37） and a mock point, and calculated the sample entropy of electroencephalo- gram signals using nonlinear dynamics. Additionally, we compared electroencephalogram sample entropy of signals in response to visual stimulation before, during, and after repeated-pulse tran- scranial magnetic stimulation at the Guangming. Results showed that electroencephalogram sample entropy at left （F3） and right （FP2） frontal electrodes were significantly different depending on where the magnetic stimulation was administered. Additionally, compared with the mock point, electroencephalogram sample entropy was higher after stimulating the Guangming point. When visual stimulation at Guangming was given before repeated-pulse transcranial magnetic stimula- tion, significant differences in sample entropy were found at five electrodes （C3, Cz, C4, P3, T8） in parietal cortex, the central gyrus, and the right temporal region compared with when it was given after repeated-pulse transcranial magnetic stimulation, indicating that repeated-pulse transcranial magnetic stimulation at Guangming can affect visual function. Analysis of electroencephalogram revealed that when visual stimulation preceded repeated pulse transcranial magnetic stimulation, sample entropy values were higher at the C3, C4, and P3 electrodes and lower at the Cz and T8 electrodes than visual stimulation followed preceded repeated pulse transcranial magnetic stimula- tion. The findings indicate that repeated-pulse transcranial magnetic stimulation at the Guangming evokes different patterns of electroencephalogram signals than repeated-pulse transcranial mag- netic stimulation at other nearby points on the body surface, and that repeated-pulse transcranial magnetic stimulation at the Guangrning is associated with changes in the complexity of visually evoked electroencephalogram signals in parietal regions, central gyrus, and temporal regions.
基金This work is supported by National Natural Science Foundation of China under Grant No.81071221.
文摘The precise classification for the electroencephalogram(EEG)in different mental tasks in the research on braincomputer interface(BCI)is the key for the design and clinical application of the system.In this paper,a new combination classification algorithm is presented and tested using the EEG data of right and left motor imagery experiments.First,to eliminate the low frequency noise in the original EEGs,the signals were decomposed by empirical mode decomposition(EMD)and then the optimal kernel parameters for support vector machine(SVM)were determined,the energy features of thefirst three intrinsic mode functions(IMFs)of every signal were extracted and used as input vectors of the employed SVM.The output of the SVM will be classification result for different mental task EEG signals.The study shows that mean identification rate of the proposed algorithm is 95%,which is much better than the present traditional algorithms.
基金This study was supported by The Scientific Technological Research Council of Turkey(TÜBITAK)under the Project No.118E682.
文摘Today,electroencephalography is used to measure brain activity by creating signals that are viewed on a monitor.These signals are frequently used to obtain information about brain neurons and may detect disorders that affect the brain,such as epilepsy.Electroencephalogram(EEG)signals are however prone to artefacts.These artefacts must be removed to obtain accurate and meaningful signals.Currently,computer-aided systems have been used for this purpose.These systems provide high computing power,problem-specific development,and other advantages.In this study,a new clinical decision support system was developed for individuals to detect epileptic seizures using EEG signals.Comprehensive classification results were obtained for the extracted filtered features from the time-frequency domain.The classification accuracies of the time-frequency features obtained from discrete continuous transform(DCT),fractional Fourier transform(FrFT),and Hilbert transform(HT)are compared.Artificial neural networks(ANN)were applied,and back propagation(BP)was used as a learning method.Many studies in the literature describe a single BP algorithm.In contrast,we looked at several BP algorithms including gradient descent with momentum(GDM),scaled conjugate gradient(SCG),and gradient descent with adaptive learning rate(GDA).The most successful algorithm was tested using simulations made on three separate datasets(DCT_EEG,FrFT_EEG,and HT_EEG)that make up the input data.The HT algorithm was the most successful EEG feature extractor in terms of classification accuracy rates in each EEG dataset and had the highest referred accuracy rates of the algorithms.As a result,HT_EEG gives the highest accuracy for all algorithms,and the highest accuracy of 87.38%was produced by the SCG algorithm.
文摘Since the advent of imaging studies such as magnetic resonance imaging (MRI), the role of electroencephalograms (EEGs) has diminished. Simultaneously, computerized scanning and miniaturization of the EEG and its components have allowed us to obtain lengthier recordings in an ambulatory setting. We report on 261 ambulatory electroencephalograms performed consecutively in the two year period of 2011 and 2012 in a busy neurology and neuropsychiatry practice with predominantly geriatric patient population. 23% of these patients had abnormal AEEGs demonstrating clear-cut epileptogenic discharges. The role of these findings in clinical practice, especially in geriatric and psychiatric populations is discussed.
文摘Since the advent of imaging studies such as magnetic resonance imaging (MRI), the role of electroencephalograms (EEGs) has diminished. Simultaneously, computerized scanning and miniaturization of the EEG and its components have allowed us to obtain lengthier recordings in an ambulatory setting. We report on 261 ambulatory electroencephalograms performed consecutively in the two year period of 2011 and 2012 in a busy neurology and neuropsychiatry practice with predominantly geriatric patient population. 23% of these patients had abnormal AEEGs demonstrating clear-cut epileptogenic discharges. The role of these findings in clinical practice, especially in geriatric and psychiatric populations is discussed.
文摘During his first year as an entrepreneur,Han Bicheng washed his hair roughly three times a day-more than 800 times in a single year.He and his team had to repeatedly apply conductive gel to their scalps to capture the clean electroencephalogram signals needed for their experiments,a routine that kept them in a near-constant cycle of shampooing.
基金supported by grants from the Shanghai Municipal Health Commission(2023ZDFC0203)the National Natural Science Foundation of China(32171044).
文摘General anesthesia,pivotal for surgical procedures,requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments.Traditional assessment methods,relying on physiological indicators or behavioral responses,fall short of accurately capturing the nuanced states of unconsciousness.This study introduces a machine learning-based approach to decode anesthesia depth,leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats.Our findings demonstrate the model’s robust predictive accuracy,underscored by a novel intrasubject dataset partitioning and a 5-fold cross-validation method.The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states,highlighting distinct EEG patterns and enhancing prediction accuracy.Moreover,the model’s ability to generalize across individuals suggests its potential for broad clinical application,distinguishing between anesthetic agents and their depths.Despite relying on rat EEG data,which poses questions about real-world applicability,our approach marks a significant advance in anesthesia monitoring.
基金supported by grants from the Zhejiang Provincial Natural Science Foundation(LGJ22H180001)Zhejiang Medical and Health Science and Technology Project(2021KY249)the National Key R&D Program of China(2017YFC1310000).
文摘Attempts have been made to modulate motor sequence learning(MSL)through repetitive transcranial magnetic stimulation,targeting different sites within the sensorimotor network.However,the target with the optimum modulatory effect on neural plasticity associated with MSL remains unclarified.This study was therefore designed to compare the role of the left primary motor cortex and the left supplementary motor area proper(SMAp)in modulating MSL across different complexity levels and for both hands,as well as the associated neuroplasticity by applying intermittent theta burst stimulation together with the electroencephalogram and concurrent transcranial magnetic stimulation.Our data demonstrated the role of SMAp stimulation in modulating neural communication to support MSL,which is achieved by facilitating regional activation and orchestrating neural coupling across distributed brain regions,particularly in interhemispheric connections.These findings may have important clinical implications,particularly for motor rehabilitation in populations such as post-stroke patients.
文摘Objective Repetitive transcranial magnetic stimulation(rTMS)has demonstrated efficacy in enhancing neurocognitive performance in Alzheimer’s disease(AD),but the neurobiological mechanisms linking synaptic pathology,neural oscillatory dynamics,and brain network reorganization remain unclear.This investigation seeks to systematically evaluate the therapeutic potential of rTMS as a non-invasive neuromodulatory intervention through a multimodal framework integrating clinical assessments,molecular profiling,and neurophysiological monitoring.Methods In this prospective double-blind trial,12 AD patients underwent a 14-day protocol of 20 Hz rTMS,with comprehensive multimodal assessments performed pre-and postintervention.Cognitive functioning was quantified using the mini-mental state examination(MMSE)and Montreal cognitive assessment(MOCA),while daily living capacities and neuropsychiatric profiles were respectively evaluated through the activities of daily living(ADL)scale and combined neuropsychiatric inventory(NPI)-Hamilton depression rating scale(HAMD).Peripheral blood biomarkers,specifically Aβ1-40 and phosphorylated tau(p-tau181),were analyzed to investigate the effects of rTMS on molecular metabolism.Spectral power analysis was employed to investigate rTMS-induced modulations of neural rhythms in AD patients,while brain network analyses incorporating topological properties were conducted to examine stimulus-driven network reorganization.Furthermore,systematic assessment of correlations between cognitive scale scores,blood biomarkers,and network characteristics was performed to elucidate cross-modal therapeutic associations.Results Clinically,MMSE and MOCA scores improved significantly(P<0.05).Biomarker showed that Aβ1-40 level increased(P<0.05),contrasting with p-tau181 reduction.Moreover,the levels of Aβ1-40 were positively correlated with MMSE and MOCA scores.Post-intervention analyses revealed significant modulations in oscillatory power,characterized by pronounced reductions in delta(P<0.05)and theta bands(P<0.05),while concurrent enhancements were observed in alpha,beta,and gamma band activities(all P<0.05).Network analysis revealed frequency-specific reorganization:clustering coefficients were significantly decreased in delta,theta,and alpha bands(P<0.05),while global efficiency improvement was exclusively detected in the delta band(P<0.05).The alpha band demonstrated concurrent increases in average nodal degree(P<0.05)and characteristic path length reduction(P<0.05).Further research findings indicate that the changes in the clinical scale HAMD scores before and after rTMS stimulation are negatively correlated with the changes in the blood biomarkers Aβ1-40 and p-tau181.Additionally,the changes in the clinical scales MMSE and MoCA scores were negatively correlated with the changes in the node degree of the alpha frequency band and negatively correlated with the clustering coefficient of the delta frequency band.However,the changes in MMSE scores are positively correlated with the changes in global efficiency of both the delta and alpha frequency bands.Conclusion 20 Hz rTMS targeting dorsolateral prefrontal cortex(DLPFC)significantly improves cognitive function and enhances the metabolic clearance ofβ-amyloid and tau proteins in AD patients.This neurotherapeutic effect is mechanistically associated with rTMS-mediated frequency-selective neuromodulation,which enhances the connectivity of oscillatory networks through improved neuronal synchronization and optimized topological organization of functional brain networks.These findings not only support the efficacy of rTMS as an adjunctive therapy for AD but also underscore the importance of employing multiple assessment methods—including clinical scales,blood biomarkers,and EEG——in understanding and monitoring the progression of AD.This research provides a significant theoretical foundation and empirical evidence for further exploration of rTMS applications in AD treatment.
文摘The global incidence of Alzheimer's Disease(AD)is on a swift rise.The Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using machine learning models.Analysis of AD using EEG involves multi-channel analysis.However,the use of multiple channels may impact the classification performance due to data redundancy and complexity.In this work,a hybrid EEG channel selection is proposed using a combination of Reptile Search Algorithm and Snake Optimizer(RSO)for AD and MCI detection based on decomposition methods.Empirical Mode Decomposition(EMD),Low-Complexity Orthogonal Wavelet Filter Banks(LCOWFB),Variational Mode Decomposition,and discrete-wavelet transform decomposition techniques have been employed for subbands-based EEG analysis.We extracted thirty-four features from each subband of EEG signals.Finally,a hybrid RSO optimizer is compared with five individual metaheuristic algorithms for effective channel selection.The effectiveness of this model is assessed by two publicly accessible AD EEG datasets.An accuracy of 99.22% was achieved for binary classification from RSO with EMD using 4(out of 16)EEG channels.Moreover,the RSO with LCOWFBs obtained 89.68%the average accuracy for three-class classification using 7(out of 19)channels.The performance reveals that RSO performs better than individual Metaheuristic algorithms with 60%fewer channels and improved accuracy of 4%than existing AD detection techniques.
基金supported by the National Natural Science Foundation of China(No.82271515)the SJTU Trans-Med Awards Research(No.2019015)+4 种基金the Scientific and Technological Innovation Action Plan of Shanghai(No.KY20211478)the Shanghai Municipal Science and Technology Major Project(No.2021SHZDZX)the Nursing Development Program of Shanghai Jiao Tong University School of Medicine(No.SJTUHLXK2022)the 2024 Shanghai Ruijin Hospital Nursing Research Fund(No.RJHK-2024-001)the Shanghai Nursing Association Funding(No.2024MS-B13),China。
文摘Abuse of amphetamine-based stimulants is a primary public health concern.Recent studies have underscored a troubling escalation in the inappropriate use of prescription amphetamine-based stimulants.However,the neurophysiological mechanisms underlying the impact of acute methamphetamine exposure(AME)on sleep homeostasis remain to be explored.This study employed non-human primates and electroencephalogram(EEG)sleep staging to evaluate the influence of AME on neural oscillations.The primary focus was on alterations in spindles,delta oscillations,and slow oscillations(SOs)and their interactions as conduits through which AME influences sleep stability.AME predominantly diminishes sleep-spindle waves in the non-rapid eye movement 2(NREM2)stage,and impacts SOs and delta waves differentially.Furthermore,the competitive relationships between SO/delta waves nesting with sleep spindles were selectively strengthened by methamphetamine.Complexity analysis also revealed that the SO-nested spindles had lost their ability to maintain sleep depth and stability.In summary,this finding could be one of the intrinsic electrophysiological mechanisms by which AME disrupted sleep homeostasis.
文摘BACKGROUND Autism spectrum disorder(ASD)is a neurodevelopmental disorder characterized by difficulties in social communication,restricted interests,and repetitive stereotyped behaviors.In recent years,the prevalence of ASD has continued to rise,with boys having a significantly higher incidence rate than girls.Children with ASD often have intellectual and language impairments,which seriously affect their social skills,emotional regulation,and daily life.Although traditional treatment methods have shown some effectiveness,they still have limitations in addressing social and emotional regulation.Neurobiofeedback therapy is a noninvasive,drug-free treatment method that helps individuals regulate physiological responses through feedback mechanisms,and it has shown potential in various psychological disorders and emotional regulation.However,there is limited research on the social skills and emotional regulation in children with ASD.Therefore,this study aims to explore the impact of neurobiofeedback technology on children with ASD through a retrospective cohort study,supplementing existing treatment methods and promoting a more comprehensive treatment of ASD.AIM To investigate the effects of neurobiofeedback therapy on social skills and emotional regulation in children with ASD.METHODS A retrospective study was conducted on 92 children with ASD who were admitted to our hospital from January 2023 to June 2024.According to their different trea-tment plans,they were divided into a conventional group(conventional rehabilitation treatment;n=43)and a combined group(conventional rehabilitation treatment combined with neurobiofeedback therapy;n=49).The general characteristics,Aberrant Behavior Checklist scores,Chinese version of the Psycho-Educational Profile,Third Edition scores,Social Responsiveness Scale scores,Emotion Regulation Checklist scores,Social Communication Questionnaire scores,and the incidence of adverse reactions were compared between groups.RESULTS After intervention,the Aberrant Behavior Checklist and Social Responsiveness Scale scores of the combined group were lower than those of the conventional group.In contrast,scores on the Chinese version of the Psycho-Educational Profile,Third Edition,Emotion Regulation Checklist,and Social Communication Questionnaire were significantly higher in the combined group than in the conventional group(all P<0.05).There was no significant difference in the incidence of adverse reactions between the two groups.CONCLUSION Neurobiofeedback therapy can effectively improve clinical symptoms,emotional regulation,and social skills in children with ASD.
文摘BACKGROUND Mild cognitive impairment(MCI)is a high-risk precursor to Alzheimer’s disease characterized by declining memory or other progressive cognitive functions without compromising daily living abilities.AIM To investigate the efficacy of repetitive transcranial magnetic stimulation(rTMS)in patients with MCI.METHODS This retrospective analysis involved 180 patients with MCI who were admitted to The First Hospital of Shanxi Medical University from January 2021 to June 2023.Participants were allocated into the research(n=98,receiving rTMS)and control groups(n=82,receiving sham stimulation).Memory tests,cognitive function assessments,event-related potential–P300 tests,and electroencephalogram(EEG)examinations were conducted pre-treatment and post-treatment.Further,memory quotient(MQ),cognitive function scores,and EEG grading results were compared,along with adverse reaction incidences.RESULTS Pre-treatment MQ scores,long-term and short-term memory,as well as immediate memory scores,demonstrated no notable differences between the groups.Post-treatment,the research group exhibited significant increases in MQ scores,long-term memory,and short-term memory compared to baseline(P<0.05),with these improvements being statistically superior to those in the control group.However,immediate memory scores exhibited no significant change(P>0.05).Further,the research group demonstrated statistically better post-treatment scores on the Revised Wechsler Memory Scale than the control group.Furthermore,post-treatment P300 latency and amplitude improved significantly in the research group,surpassing the control group.EEG grading in the research group improved,and the incidence of adverse reactions was significantly lower than in the control group.CONCLUSION Patients with MCI receiving rTMS therapy demonstrated improved memory and cognitive functions and EEG grading and exhibited high safety with fewer adverse reactions.
基金supported by the Medium and Long-term Science and Technology Plan for Radio,Television,and Online Audiovisuals(2023AC0200)the Public Welfare Technology Application Research Project of Zhejiang Province,China(No.LGF21F010001).
文摘Recent deep neural network(DNN)based blind image quality assessment(BIQA)approaches take mean opinion score(MOS)as ground-truth labels,which would lead to cross-datasets biases and limited generalization ability of the DNN-based BIQA model.This work validates the natural instability of MOS through investigating the neuropsychological characteristics inside the human visual system during quality perception.By combining persistent homology analysis with electroencephalogram(EEG),the physiologically meaningful features of the brain responses to different distortion levels are extracted.The physiological features indicate that although volunteers view exactly the same image content,their EEG features are quite varied.Based on the physiological results,we advocate treating MOS as noisy labels and optimizing the DNN based BIQA model with earlystop strategies.Experimental results on both innerdataset and cross-dataset demonstrate the superiority of our optimization approach in terms of generalization ability.
文摘An instrumental assessment and volunteer subjective evaluation method was developed to synchronously measure the actual skin temperature and evaluate the cool sensation,conducting a quantitative analysis of the perceived coolness.This method was used to evaluate the effect of a self-developed hot flash spray on reducing the skin temperature and inducing the cooling sensation of menopausal individuals.31 healthy menopausal volunteers were recruited as research subjects.Using infrared thermal imaging and electroencephalogram(EEG)measurements,the skin temperature and EEG data of the subjects’cheeks were simultaneously collected at baseline(BL)immediately after simulated hot flashes(HF),1 min(T1),3 mins(T3)and 5 min(T5)after the application of the test sample.The results showed that,compared with HF,the skin temperature of cheek was significantly reduced by 8.75%,8.75%and 6.41%at T1,T3 and T5(P<0.05),respectively.And alpha-1 value of EEG was increased significantly by 59.70%,58.44%,and 51.39%at T1,T3,and T5(P<0.05),respectively.The hot flash spray effectively reduces skin temperature while also provides subjects with a feeling of coolness,which can relieve hot flashes in menopausal women.
文摘Physiological signals such as electroencephalogram(EEG)signals are often corrupted by artifacts during the acquisition and processing.Some of these artifacts may deteriorate the essential properties of the signal that pertains to meaningful information.Most of these artifacts occur due to the involuntary movements or actions the human does during the acquisition process.So,it is recommended to eliminate these artifacts with signal processing approaches.This paper presents two mechanisms of classification and elimination of artifacts.In the first step,a customized deep network is employed to classify clean EEG signals and artifact-included signals.The classification is performed at the feature level,where common space pattern features are extracted with convolutional layers,and these features are later classified with a support vector machine classifier.In the second stage of the work,the artifact signals are decomposed with empirical mode decomposition,and they are then eliminated with the proposed adaptive thresholding mechanism where the threshold value changes for every intrinsic mode decomposition in the iterative mechanism.
基金Supported by the Natural Science Foundation of Shandong Province(No.ZR2022MF309)the Science and Technology Small and Mediumsized Enterprise Innovation Ability Enhancement Projec of Shandong Province(No.2022TSGC2554).
文摘The classification of motor imagery electroencephalogram(MI-EEG)signals is one of the key challenges in brain-computer interface(BCI)technology.Existing Riemannian geometry-based methods for MI-EEG signal analysis,which rely on a single symmetric positive definite(SPD)manifold,often provide a limited geometric structure,making it difficult to fully capture the complex geometric characteristics of the signals.To address this issue,this paper proposes an innovative classification method for MI-EEG signals based on multi-Riemannian kernel fusion features(MRKFF).This method extends the classical SPD manifold by incorporating the Gaussian SPD manifold and the Grassmann manifold,extracting more discriminative kernel features from these heterogeneous manifolds for fusion-based classification.The proposed method is validated on the OpenBMI binary classification dataset and the BCI Competition IV-2a four-class dataset,achieving average classification accuracies of 75.6%and 71.0%,with Kappa values of 0.50 and 0.61,respectively.The proposed MRKFF method provides a new perspective for the geometric analysis of MI-EEG signals,enabling a deeper understanding and analysis of the complex geometric structure of these signals,thereby achieving more accurate signal classification in BCI applications.
文摘Deep learning methods have been widely applied in motor imagery(MI)-based brain-computer interfaces(BCI)for decoding electroencephalogram(EEG)signals.High temporal resolution and asymmetric spatial activation are fundamental properties of EEG during MI processes.However,due to the limited receptive field of convolutional kernels,traditional convolutional neural networks(CNNs)often focus only on local features,and are insufficient to cover neural processes across different frequency bands and duration scales.This limitation hinders the effective characterization of rhythmic activity changes in MI-EEG signals over time.Additionally,MI-EEG signals exhibit significant asymmetric activation between the left and right hemispheres.Traditional spatial feature extraction methods overlook the interaction between global and local regions at the spatial scale of EEG signals,resulting in inadequate spatial representation and ultimately limiting decoding accuracy.To address these limitations,in this study,a novel deep learning network that integrates multi-modal temporal features with spatially asymmetric feature modeling was proposed.The network first extracts multi-modal temporal information from EEG data channels,and then captures global and hemispheric spatial features in the spatial dimension and fuses them through an advanced fusion layer.Global dependencies are captured using a self-attention module,and a multi-scale convolutional fusion module is introduced to explore the relationships between the two types of temporal features.The fused features are classified through a classification layer to accomplish motor imagery task classification.To mitigate the issue of limited sample size,a data augmentation strategy based on signal segmentation and recombination is designed.Experimental results on the BCI Competition IV-2a(bbic-IV-2a)and BCI Competition IV-2b(bbic-IV-2a)datasets demonstrated that the proposed method achieved superior accuracy in multi-class motor imagery classification compared with existing models.On the BCI-IV-2a dataset,it attained an average classification accuracy of 84.36%,while also showing strong performance on the binary classification BCI-IV-2b dataset.These outcomes validate the capability of the proposed network to enhance MI-EEG classification accuracy.
文摘The problem of automated seizure detection is treated using clinical electroencephalograms(EEG) and machine learning algorithms on the Temple University Hospital EEG Seizure Corpus(TUSZ).Performances on this complex data set are still not encountering expectations.The purpose of this work is to determine to what extent the use of larger amount of data can help to improve the performances.Two methods are explored:a standard partitioning on a recent and larger version of the TUSZ,and a leave-one-out approach used to increase the amount of data for the training set.XGBoost,a fast implementation of the gradient boosting classifier,is the ideal algorithm for these tasks.The performances obtained are in the range of what is reported until now in the literature with deep learning models.We give interpretation to our results by identifying the most relevant features and analyzing performances by seizure types.We show that generalized seizures tend to be far better predicted than focal ones.We also notice that some EEG channels and features are more important than others to distinguish seizure from background.
文摘Objective To observe the effect of acupuncture stimulation of the sacral segment on the excitability of the cerebral cortex and the activity of the urinary bladder and the involvement of the cholinergic neurons in the laterodorsal tegmental （LDT） nucleus of the brainstem in acupuncture-induced electroencephalogram （EEG） changes. Methods A total of 109 SD rats were used in the present study. Under anesthesia （urethane）, a pair of stainless steel electrodes was separately implanted into the frontal and parietal bony sutures to record EEG. Glass microelectrodes were used to record extracellular discharges of single neuron of the LDT nucleus in the brainstem. Urinary bladder pressure was recorded through a catheter inserted in the bladder and the contraction was induced by infusion of normal saline. A filiform acupuncture needle was inserted into the sacral segment Ecorresponding to Zhongliao （中髎BL 33）] and rotated manually for 1 min. Results In 27 rats whose bladder was full of normal saline, acupuncture stimulation of the sacral region suppressed the contraction activity of the bladder, the fast EEG with lower amplitude and higher frequency tuned into slow EEG with higher amplitude and lower frequency in 6 cases （22.2%）. The inhibitory effect occurred from 45 s to 12 min after acupuncture manipulation. In 82 rats whose bladder was empty, acupuncture stimulation caused the fast EEG to turn into slow EEG in 71 cases （86.6%）. Simultaneously, LDT cholinergic neurons reduced their firing rates from （2.9±1.5） Hz to （1.2±0.6） Hz （n = 12, P〈0.05）, and the reduction of LDT neuronal discharge was earlier in time than the change of EEG. Conclusion Acupuncture stimulation of the sacral region can lower the excitability of the cerebral cortex and suppress bladder activity, which is closely associated with its resultant inhibitory effect on the electrical activity of LDT cholinergic neurons.
