Decreased brain iron deposition based on quantitative susceptibility mapping correlates with reduced neurodevelopmental status in children with autism spectrum disorder.

To investigate potential correlations between the susceptibility values of certain brain regions and the severity of disease or neurodevelopmental status in children with autism spectrum disorder (ASD), 18 ASD children and 15 healthy controls (HCs) were recruited. The neurodevelopmental status was assessed by the Gesell Developmental Schedules (GDS) and the severity of the disease was evaluated by the Autism Behavior Checklist (ABC). Eleven brain regions were selected as regions of interest and the susceptibility values were measured by quantitative susceptibility mapping. To evaluate the diagnostic capacity of susceptibility values in distinguishing ASD and HC, the receiver operating characteristic (ROC) curve was computed. Pearson and Spearman partial correlation analysis were used to depict the correlations between the susceptibility values, the ABC scores, and the GDS scores in the ASD group. ROC curves showed that the susceptibility values of the left and right frontal white matter had a larger area under the curve in the ASD group. The susceptibility value of the right globus pallidus was positively correlated with the GDS-fine motor scale score. These findings indicated that the susceptibility value of the right globus pallidus might be a viable imaging biomarker for evaluating the neurodevelopmental status of ASD children.

The effect of EEG microstate on the characteristics of TMS-EEG.

OBJECTIVE: Differences in neural states at the time of transcranial magnetic stimulation (TMS) can lead to variations in the effectiveness of TMS stimulation. Strategies that aim to lock neural activity states and improve the precision of stimulation timing in TMS optimization should gradually receive attention. One feasible approach is to utilize microstate locking for TMS stimulation, and understanding the impact of microstates at the time of stimulation on TMS response forms the foundation of this approach. APPROACH: TMS-EEG data were extracted from 21 healthy subjects through experiments. Based on the different microstates at the time of stimulation, the trials were classified into four datasets. TMS-evoked potential (TEP), topographical distribution, and natural frequency, were computed for each dataset to explore the differences in TMS-EEG characteristics across different microstates. MAIN RESULTS: The N100 component of microstate C group (-2.376 µV) was significantly higher (p = 0.003) than of microstate D group (-1.739 µV), and the P180 component of microstate D group (2.482 µV) was significantly higher (p = 0.024) than of microstate B group (1.766 µV) and slightly higher (p = 0.058) than of microstate C group (1.863 µV) by calculating the ROI. The topographical distribution of TEP components during microstate C and microstate D still retained the template characteristics of the microstate at the time of stimulation, and the natural frequencies did not differ among the four classical microstates. SIGNIFICANCE: This study showed the potential for future closed-loop TMS based on microstates and would guiding the development of microstate-based closed-loop TMS techniques.

Identification of autism spectrum disorder based on electroencephalography: A systematic review.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social communication and repetitive and stereotyped behaviors. According to the World Health Organization, about 1 in 100 children worldwide has autism. With the global prevalence of ASD, timely and accurate diagnosis has been essential in enhancing the intervention effectiveness for ASD children. Traditional ASD diagnostic methods rely on clinical observations and behavioral assessment, with the disadvantages of time-consuming and lack of objective biological indicators. Therefore, automated diagnostic methods based on machine learning and deep learning technologies have emerged and become significant since they can achieve more objective, efficient, and accurate ASD diagnosis. Electroencephalography (EEG) is an electrophysiological monitoring method that records changes in brain spontaneous potential activity, which is of great significance for identifying ASD children. By analyzing EEG data, it is possible to detect abnormal synchronous neuronal activity of ASD children. This paper gives a comprehensive review of the EEG-based ASD identification using traditional machine learning methods and deep learning approaches, including their merits and potential pitfalls. Additionally, it highlights the challenges and the opportunities ahead in search of more effective and efficient methods to automatically diagnose autism based on EEG signals, which aims to facilitate automated ASD identification.

Long-term HD-tDCS modulates dynamic changes of brain activity on patients with disorders of consciousness: A resting-state EEG study.

OBJECTIVE: High-definition transcranial direct current stimulation (HD-tDCS) has been an effective neurostimulation method in the treatment of disorders of consciousness (DOC). However, the effects and mechanism of HD-tDCS are still unclear. METHODS: This study recruited 8 DOC patients and applied 20-min sessions of 2 mA HD-tDCS (central anode electrode at Pz) for 14 consecutive days. We record DOC patients' EEG data and Coma Recovery Scale-Revised (CRS-R) values at four time point: baseline (T0), after 1 day's and 7,14 days' parietal HD-tDCS treatment (T1, T2, T3). Power spectral density (PSD), relative power (RP), spectral entropy and spectral exponent were calculated to evaluate the EEG dynamic changes of DOC patients during long-term parietal HD-tDCS. At last, we calculated the correlation between changes of EEG features and changes of CRS-R values. RESULT: After 1 day's parietal HD-tDCS, DOC patients' CRS-R value had not changed (8.25 ± 1.91). HD-tDCS improved DOC patients' CRS-R value at T2 (9.75 ± 1.91, p < 0.05) and at T3 (11.38 ± 2.77, p < 0.05), compared with that at T0 (8.25 ± 1.91). As the treatment time increased, the EEG PSD decayed more slowly. Specifically, the delta frequency band RP decreased, while the alpha, beta, and gamma frequency bands RP increased. EEG oscillation characteristics changed but not significant at T1 (p > 0.05), and showed significant changes at T2 and T3 (p < 0.05). The spectral entropy continuously increased and the spectral exponent continuously decreased from T0 to T3. Specifically, the spectral entropy and spectral exponent of the parietal and occipital regions were significantly higher at T2 and T3 than that at T0 (p < 0.05). In addition, The changes in EEG features of the parietal and occipital lobes were correlated with changes in CRS-R value, especially between T2 and T0. CONCLUSION: Long-term parietal HD-tDCS can improve the consciousness level and brain activity in DOC patients. Resting-state EEG can evaluate the dynamic changes of brain activity in DOC patients during HD-tDCS. EEG oscillation and non-oscillatory activity might be used to explain the mechanism of HD-tDCS on DOC patients.

D348N Mutation of BFSP1 Gene in Congenital Cataract: it Does Matter.

Beaded filament structural protein 1 (BFSP1) gene plays important role in the development of congenital cataract. We aimed to investigate and analyze the molecular mechanism of congenital cataract caused by D348N mutation of BFSP1 gene, and to provide evidence for the intervention of congenital cataract. BFSP1 and CP49 genes were cloned, wild type and mutant expression plasmids of BFSP1 were constructed and transfected into 293T cells. The BFSP1 wild type and mutant (D348N) gene sequence (NM_001195) were constructed into pEGFP-N1 vector by the restriction site NheI/KpnI. The effect of mutation on cell proliferation and apoptosis was analyzed. There was no significant change between the expression site of BFSP1 D348N mutation and the wild type. The expression of BFSP1 protein in wild group was higher than that in mutant group. CCK8 detection showed that the proliferation ability of 293T cells in mutant group was weaker than that in BFSP1 group. The mutation led to an increase in apoptosis. BFSP1 mutation significantly decreases the expression of BFSP1 protein, weakened the ability of cell proliferation and increased apoptosis. BFSP1 D348N mutation may be closely associated with congenital cataract and is of great significance to the investigations of the mechanism and intervention of congenital cataract.

Preliminary study on the efficacy of lacrimal duct reconstruction with pedicled conjunctival flap in the treatment of severe lacrimal canalicular obstruction with conjunctivochalasis.

AIM: To determine the efficacy and safety of pedicled conjunctival lacrimal duct reconstruction in the treatment of severe obstruction of superior and inferior lacrimal canaliculi with conjunctivochalasis. METHODS: This study was performed as a retrospective analysis of patients who received conjunctival dacryocystorhinostomy with pedicled conjunctival flap reconstruction combined with tube intubation due to severe superior and inferior lacrimal canalicular obstruction with conjunctivochalasis from January 2019 to October 2019. The clinical data included the degree of preoperative epiphora and postoperative relief, preoperative examination of lacrimal duct computed tomography and ultrasound biomicroscopy, postoperative evaluation of lacrimal duct function by chloramphenicol taste and fluorescein dye disappearance test, etc. Syringing was carried out to determine the reconstruction and patency of the lacrimal duct. RESULTS: All 9 patients (9 eyes) had severe canalicular obstruction with conjunctivochalasis. The patients included 4 males and 5 females aged between 47-65y with an average age of 52.2±6.7y. At 3mo follow-up, the tube was removed and the patients were followed for a further 3mo. After tube removal, 6 patients showed no epiphora. These patients also had positive chloramphenicol tastes and normal fluorescein dye disappearance test results. Two patientshad epiphora. Also, syringing showed partial patency of the reconstructed lacrimal duct. One patient had no improvement in epiphora with negative chloramphenicol taste and fluorescein dye disappearance test results and obstruction of the reconstructed lacrimal duct. The total effective rate of the operation was 8/9, with no serious complications. CONCLUSION: Pedicled conjunctival lacrimal duct reconstruction conjunctival dacryocystorhinostomy is safe and effective for superior and inferior canalicular obstruction with conjunctivochalasis.

Task-independent auditory probes reveal changes in mental workload during simulated quadrotor UAV training.

Objective: The event-related potential (ERP) methods based on laboratory control scenes have been widely used to measure the level of mental workload during operational tasks. In this study, both task difficulty and test time were considered. Auditory probes (ignored task-irrelevant background sounds) were used to explore the changes in mental workload of unmanned aerial vehicle (UAV) operators during task execution and their ERP representations. Approach: 51 students participated in a 10-day training and test of simulated quadrotor UAV. During the experiment, background sound was played to induce ERP according to the requirements of oddball paradigm, and the relationship between mental workload and the amplitudes of N200 and P300 in ERP was explored. Main results: Our study shows that the mental workload during operational task training is multi-dimensional, and its changes are affected by bottom-up perception and top-down cognition. The N200 component of the ERP evoked by the auditory probe corresponds to the bottom-up perceptual part; while the P300 component corresponds to the top-down cognitive part, which is positively correlated with the improvement of skill level. Significance: This paper describes the relationship between ERP induced by auditory probes and mental workload from the perspective of multi-resource theory and human information processing. This suggests that the auditory probe can be used to reveal the mental workload during the training of operational tasks, which not only provides a possible reference for measuring the mental workload, but also provides a possibility for identifying the development of the operator's skill level and evaluating the training effect.

SRY-box transcription factor 9 modulates Müller cell gliosis in diabetic retinopathy by upregulating TXNIP transcription.

Diabetic retinopathy (DR), a common complication of diabetes, involves excessive proliferation and inflammation of Muller cells and ultimately leads to vision loss and blindness. SRY-box transcription factor 9 (SOX9) has been reported to be highly expressed in Müller cells in light-induced retinal damage rats, but the functional role of SOX9 in DR remains unclear. To explore this issue, the DR rat model was successfully constructed via injection with streptozotocin (65 mg/kg) and the retinal thicknesses and blood glucose levels were evaluated. Müller cells were treated with 25 mmol/l glucose to create a cell model in vitro. The results indicated that SOX9 expression was significantly increased in DR rat retinas and in Müller cells stimulated with a high glucose (HG) concentration. HG treatment promoted the proliferation and migration capabilities of Müller cells, whereas SOX9 knockdown reversed those behaviors. Moreover, SOX9 knockdown provided protection against an HG-induced inflammatory response, as evidenced by reduced tumor necrosis factor-α, IL-1ß, and IL-6 levels in serum and decreased NLRP3 inflammasome activation. Notably, SOX9 acted as a transcription factor that positively regulated thioredoxin-interacting protein (TXNIP), a positive regulator of Müller cells gliosis under HG conditions. A dual-luciferase assay demonstrated that SOX9 could enhance TXNIP expression at the transcriptional level through binding to the promoter of TXNIP. Moreover, TXNIP overexpression restored the effects caused by SOX9 silencing. In conclusion, these findings demonstrate that SOX9 may accelerate the progression of DR by promoting glial cell proliferation, metastasis, and inflammation, which involves the transcriptional regulation of TXNIP, providing new theoretical fundamentals for DR therapy.

A Multimodal Approach for Identifying Autism Spectrum Disorders in Children.

Identification of autism spectrum disorder (ASD) in children is challenging due to the complexity and heterogeneity of ASD. Currently, most existing methods mainly rely on a single modality with limited information and often cannot achieve satisfactory performance. To address this issue, this paper investigates from internal neurophysiological and external behavior perspectives simultaneously and proposes a new multimodal diagnosis framework for identifying ASD in children with fusion of electroencephalogram (EEG) and eye-tracking (ET) data. Specifically, we designed a two-step multimodal feature learning and fusion model based on a typical deep learning algorithm, stacked denoising autoencoder (SDAE). In the first step, two SDAE models are designed for feature learning for EEG and ET modality, respectively. Then, a third SDAE model in the second step is designed to perform multimodal fusion with learned EEG and ET features in a concatenated way. Our designed multimodal identification model can automatically capture correlations and complementarity from behavior modality and neurophysiological modality in a latent feature space, and generate informative feature representations with better discriminability and generalization for enhanced identification performance. We collected a multimodal dataset containing 40 ASD children and 50 typically developing (TD) children to evaluate our proposed method. Experimental results showed that our proposed method achieved superior performance compared with two unimodal methods and a simple feature-level fusion method, which has promising potential to provide an objective and accurate diagnosis to assist clinicians.

Appearance-Based Gaze Estimation for ASD Diagnosis.

Biomarkers, such as magnetic resonance imaging (MRI) and electroencephalogram have been used to help diagnose autism spectrum disorder (ASD). However, the diagnosis needs the assist of specialized medical equipment in the hospital or laboratory. To diagnose ASD in a more effective and convenient way, in this article, we propose an appearance-based gaze estimation algorithm-AttentionGazeNet, to accurately estimate the subject's 3-D gaze from a raw video. The experimental results show its competitive performance on the MPIIGaze dataset and the improvement of 14.7% for static head pose and 46.7% for moving head pose on the EYEDIAP dataset compared with the state-of-the-art gaze estimation algorithms. After projecting the obtained gaze vector onto the screen coordinate, we apply accumulated histogram to taking into account both spatial and temporal information of estimated gaze-point and head-pose sequences. Finally, classification is conducted on our self-collected autistic children video dataset (ACVD), which contains 405 videos from 135 different ASD children, 135 typically developing (TD) children in a primary school, and 135 TD children in a kindergarten. The classification results on ACVD shows the effectiveness and efficiency of our proposed method, with the accuracy 94.8%, the sensitivity 91.1% and the specificity 96.7% for ASD.

Application Value of Flexible Endoscopic Examination of Swallowing in Acute Stoke Patients With Dysphagia.

BACKGROUND: The aim was to study the application value of flexible endoscopic examination of swallowing (FEES) for the aspiration screening, the diagnosis of dysphagia and evaluation of the therapeutic effect in acute stoke patients with dysphagia. METHODS: A total of 525 patients with acute stoke who were hospitalized from October 2015 to January 2021 in the Rehabilitation Medicine Department of our hospital underwent FEES for analyzing the characteristic performance. Twenty-one cases of them were examined by video fluoroscopic swallow study and compared with the results of FEES for evaluating the reliability of the FEES, the reliability of diagnosis of dysphagia, and the consistency of the 2 methods. The effect of rehabilitation was evaluated by comparing the FEES test results before and after treatment. RESULTS: In 525 patients, the FEES revealed 378 cases of aspiration (139 cases were silent aspiration), showing a higher detection rate than water swallow test. Patients with potential cricopharyngeus achalasia got the same results through both of examinations. FEES can provide more positive indicators, guide clinical rehabilitation treatment and objectively assess the effect of rehabilitation. CONCLUSIONS: Acute stoke patients with dysphagia have characteristic pharyngeal and laryngeal performance. FEES is simple to operate and has high application value in the diagnosis and treatment of dysphagia.

Lower Small-Worldness of Intrinsic Brain Networks Facilitates the Cognitive Protection of Intellectual Engagement in Elderly People Without Dementia: A Near-Infrared Spectroscopy Study.

OBJECTIVE: Lifetime intellectual engagement may be associated with cognitive ability late in life. However, the current evidence on whether cognitive activities will improve and/or maintain cognitive function is heterogeneous. Drawing on knowledge of the brain's intrinsic small-world organization which combines regional specialization and efficient global information transfer, we aimed to explore that whether individual differences in the small-worldness of resting-state functional connectivity (rsFC) networks would explain the variability in the strength of the association between intellectual engagement and cognitive functioning. METHODS: Sixty-five elderly people without dementia were enrolled and scanned with a 52-channel near-infrared spectroscopy system. The number, frequency, and participation hours of intellectual activities were investigated to measure intellectual engagement. Global cognition was assessed by the Montreal Cognitive Assessment. The general linear models and the simple slope analysis were employed to measure the modulatory role of network properties. RESULTS: The small-worldness of the brain network emerged as a moderator of the association between intellectual engagement and cognition. Exclusively among elderly people with lower small-worldness, greater intellectual engagement, including the frequency and participation hours of activities, was associated with greater global cognitive function. Furthermore, we observed that elderly people with lower small-worldness exhibited decreased rsFC across the bilateral frontopolar areas and increased rsFC across the bilateral parietal cortex. CONCLUSION: The individual differences in the small-worldness of rsFC networks might explain the varying strength of the association between intellectual engagement and cognitive functioning. Our findings imply that the intrinsic small-worldness of the brain network might be a potential neurobiological contributor that interacts with the intellectual engagement in enhancing the cognitive ability in late life.

Closed-loop transcranial magnetic stimulation of real-time EEG based on the AR mode method.

OBJECTIVE: Transcranial magnetic stimulation (TMS) as a safe, noninvasive brain regulation technology has been gradually applied to clinical treatment. Traditional TMS devices do not adjust output based on real-time brain activity information when regulating the cerebral cortex, but the current activity information from the brain, especially the EEG phase, may affect the stimulation effect. It is necessary to calculate the synchronous EEG phase during TMS. APPROACH: In this study, a set of closed-loop TMS device a fast EEG phase prediction algorithm based on the AR model was designed to meet the demand. EEG data for twenty-seven healthy college students were collected to verify the accuracy of the algorithm. MAIN RESULTS: The calculation results showed that the prediction accuracy of the AR model algorithm is better than that of the conventional algorithm when the model order is lower, and the prediction accuracy will increase with improvements in the signal quality. SIGNIFICANCE: When the experimental environment is good, the EEG data with a high SNR can be recorded, and when the order of the AR model is properly set, the prediction algorithm can make correct judgments most of the time and the stimulation pulse can be output when the EEG phase reaches a set value.

Transcranial Direct Current Stimulation (tDCS) Can Modulate EEG Complexity of Children With Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder which affects the developmental trajectory in several behavioral domains, including impairments of social communication, cognitive and language abilities. Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique, and it was used for modulating the brain disorders. In this paper, we enrolled 13 ASD children (11 males and 2 females; mean ± SD age: 6.5 ± 1.7 years) to participate in our trial. Each patient received 10 treatments over the dorsolateral prefrontal cortex (DLPFC) once every 2 days. Also, we enrolled 13 ASD children (11 males and 2 females; mean ± SD age: 6.3 ± 1.7 years) waiting to receive therapy as controls. A maximum entropy ratio (MER) method was adapted to measure the change of complexity of EEG series. It was found that the MER value significantly increased after tDCS. This study suggests that tDCS may be a helpful tool for the rehabilitation of children with ASD.

Identifying ADHD children using hemodynamic responses during a working memory task measured by functional near-infrared spectroscopy.

OBJECTIVE: Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting children and adults. Previous studies found that functional near-infrared spectroscopy (fNIRS) can reveal significant group differences in several brain regions between ADHD children and healthy controls during working memory tasks. This study aimed to use fNIRS activation patterns to identify ADHD children from healthy controls. APPROACH: FNIRS signals from 25 ADHD children and 25 healthy controls performing the n-back task were recorded; then, multivariate pattern analysis was used to discriminate ADHD individuals from healthy controls, and classification performance was evaluated for significance by the permutation test. MAIN RESULTS: The results showed that 86.0% ([Formula: see text]) of participants can be correctly classified in leave-one-out cross-validation. The most discriminative brain regions included the bilateral dorsolateral prefrontal cortex, inferior medial prefrontal cortex, right posterior prefrontal cortex, and right temporal cortex. SIGNIFICANCE: This study demonstrated that, in a small sample, multivariate pattern analysis can effectively identify ADHD children from healthy controls based on fNIRS signals, which argues for the potential utility of fNIRS in future assessments.

Disrupted Brain Network in Children with Autism Spectrum Disorder.

Alterations in brain connectivity have been extensively reported in autism spectrum disorder (ASD), while their effects on the topology of brain network are still unclear. This study investigated whether and how the brain networks in children with ASD were abnormally organized with resting state EEG. Temporal synchronization analysis was first applied to capture the aberrant brain connectivity. Then brain network topology was characterized by three graph analysis methods including the commonly-used weighted and binary graph, as well as minimum spanning tree (MST). Whole brain connectivity in ASD group was found to be significantly reduced in theta and alpha band compared to typically development children (TD). Weighted graph found significantly decreased path length together with marginally significantly decreased clustering coefficient in ASD in alpha band, indicating a loss of small-world architecture to a random network. Such abnormal network topology was also demonstrated in the binary graph. In MST analysis, children with ASD showed a significant lower leaf fractions with a decrease trend of tree hierarchy in the alpha band, suggesting a shift towards line-like decentralized organization in ASD. The altered brain network may offer an insight into the underlying pathology of ASD and possibly serve as a biomarker that may aid in diagnosis of ASD.

Development of Brain Network in Children with Autism from Early Childhood to Late Childhood.

Extensive studies have indicated brain function connectivity abnormalities in autism spectrum disorder (ASD). However, there is a lack of longitudinal or cross-sectional research focused on tracking age-related developmental trends of autistic children at an early stage of brain development or based on a relatively large sample. The present study examined brain network changes in a total of 186 children both with and without ASD from 3 to 11 years, an early and key development period when significant changes are expected. The study aimed to investigate possible abnormal connectivity patterns and topological properties of children with ASD from early childhood to late childhood by using resting-state electroencephalographic (EEG) data. The main findings of the study were as follows: (1) From the connectivity analysis, several inter-regional synchronizations with reduction were identified in the younger and older ASD groups, and several intra-regional synchronization increases were observed in the older ASD group. (2) From the graph analysis, a reduced clustering coefficient and enhanced mean shortest path length in specific frequencies was observed in children with ASD. (3) Results suggested an age-related decrease of the mean shortest path length in the delta and theta bands in TD children, whereas atypical age-related alteration was observed in the ASD group. In addition, graph measures were correlated with ASD symptom severity in the alpha band. These results demonstrate that abnormal neural communication is already present at the early stages of brain development in autistic children and this may be involved in the behavioral deficits associated with ASD.

Complexity analysis of fNIRS signals in ADHD children during working memory task.

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children. Neuroimaging studies have revealed abnormalities of neural activities in some brain regions, including the frontal cortex, striatum, cerebellum, and occipital cortex. Recently, some investigators have demonstrated that nonlinear complexity analysis of neural activity may provide a new index to indicate ADHD. In the present study, we used the permutation entropy (PE) to measure the complexity of functional near-infrared spectroscopy (fNIRS) signals in children with and without ADHD during a working memory task, it was aimed to investigate the relationship between the PE values and the cortical activations, and the different PE values between the children with and without ADHD. We found that PE values exhibited significantly negative correlation with the cortical activations (r = -0.515, p = 0.003), and the PE values of right dorsolateral prefrontal cortex in ADHD children were significantly larger than those in normal controls (p = 0.027). In addition, the PE values of right dorsolateral prefrontal cortex were positively correlated to the ADHD index (r = 0.448, p = 0.012). These results suggest that complexity analysis of fNIRS signals could be a promising tool in diagnosing children with ADHD.

An EEMD-ICA Approach to Enhancing Artifact Rejection for Noisy Multivariate Neural Data.

As neural data are generally noisy, artifact rejection is crucial for data preprocessing. It has long been a grand research challenge for an approach which is able: 1) to remove the artifacts and 2) to avoid loss or disruption of the structural information at the same time, thus the risk of introducing bias to data interpretation may be minimized. In this study, an approach (namely EEMD-ICA) was proposed to first decompose multivariate neural data that are possibly noisy into intrinsic mode functions (IMFs) using ensemble empirical mode decomposition (EEMD). Independent component analysis (ICA) was then applied to the IMFs to separate the artifactual components. The approach was tested against the classical ICA and the automatic wavelet ICA (AWICA) methods, which were dominant methods for artifact rejection. In order to evaluate the effectiveness of the proposed approach in handling neural data possibly with intensive noises, experiments on artifact removal were performed using semi-simulated data mixed with a variety of noises. Experimental results indicate that the proposed approach continuously outperforms the counterparts in terms of both normalized mean square error (NMSE) and Structure SIMilarity (SSIM). The superiority becomes even greater with the decrease of SNR in all cases, e.g., SSIM of the EEMD-ICA can almost double that of AWICA and triple that of ICA. To further examine the potentials of the approach in sophisticated applications, the approach together with the counterparts were used to preprocess a real-life epileptic EEG with absence seizure. Experiments were carried out with the focus on characterizing the dynamics of the data after artifact rejection, i.e., distinguishing seizure-free, pre-seizure and seizure states. Using multi-scale permutation entropy to extract feature and linear discriminant analysis for classification, the EEMD-ICA performed the best for classifying the states (87.4%, about 4.1% and 8.7% higher than that of AWICA and ICA respectively), which was closest to the results of the manually selected dataset (89.7%).

Using Relevance Feedback to Distinguish the Changes in EEG During Different Absence Seizure Phases.

We carried out a series of statistical experiments to explore the utility of using relevance feedback on electroencephalogram (EEG) data to distinguish between different activity states in human absence epilepsy. EEG recordings from 10 patients with absence epilepsy are sampled, filtered, selected, and dissected from seizure-free, preseizure, and seizure phases. A total of 112 two-second 19-channel EEG epochs from 10 patients were selected from each phase. For each epoch, multiscale permutation entropy of the EEG data was calculated. The feature dimensionality was reduced by linear discriminant analysis to obtain a more discriminative and compact representation. Finally, a relevance feedback technique, that is, direct biased discriminant analysis, was applied to 68 randomly selected queries over nine iterations. This study is a first attempt to apply the statistical analysis of relevance feedback to the distinction of different EEG activity states in absence epilepsy. The average precision in the top 10 returned results was 97.5%, and the standard deviation suggested that embedding relevance feedback can effectively distinguish different seizure phases in absence epilepsy. The experimental results indicate that relevance feedback may be an effective tool for the prediction of different activity states in human absence epilepsy. The simultaneous analysis of multichannel EEG signals provides a powerful tool for the exploration of abnormal electrical brain activity in patients with epilepsy.