Background music has been increasingly affecting people’s lives. The research on the influence of background music on working memory has become a hot topic in brain science. In this paper, an improved electroencephalography (EEG) experiment based on n-back paradigm was designed. Fifteen university students without musical training were randomly selected to participate in the experiment, and their behavioral data and the EEG data were collected synchronously in order to explore the influence of different types of background music on spatial positioning cognition working memory. The exact low-resolution brain tomography algorithm (eLORETA) was applied to localize the EEG sources and the cross-correlation method was used to construct the cortical brain function networks based on the EEG source signals. Then the characteristics of the networks under different conditions were analyzed and compared to study the effects of background music on people’s working memory. The results showed that the difference of peak periods after stimulated by different types of background music were mainly distributed in the signals of occipital lobe and temporal lobe (P < 0.05). The analysis results showed that the brain connectivity under the condition with background music were stronger than those under the condition without music. The connectivities in the right occipital and temporal lobes under the condition of rock music were significantly higher than those under the condition of classical music. The node degrees, the betweenness centrality and the clustering coefficients under the condition without music were lower than those under the condition with background music. The node degrees and clustering coefficients under the condition of classical music were lower than those under the condition of rock music. It indicates that music stimulation increases the brain activity and has an impact on the working memory, and the effect of rock music is more remarkable than that of classical music. The behavioral data showed that the response accuracy in the state of no music, classical music and rock music were 86.09% ± 0.090%, 80.96% ± 0.960% and 79.36% ± 0.360%, respectively. We conclude that background music has a negative impact on the working memory, for it takes up the cognitive resources and reduces the cognitive ability of spatial location.
ObjectiveTo explore the prognostic value of normal 24 hour video electroencephalography (VEEG) with different frequency on antiepileptic drugs (AEDs) withdrawal in cryptogenic epilepsy patients with three years seizure-free. MethodsA retrospective study was conducted in the Neurology outpatient and the Epilepsy Center of Xi Jing Hospital. The subject who had been seizure free more than 3 years were divided into continual normal twice group and once group according to the nomal frequence of 24 hour VEEG before discontinuation from January 2013 to December 2014, and then followed up to replase or to December 2015. The recurrence and cumulative recurrence rate of the two group after withdrawal AEDs were compared with chi-square or Fisher's exact test and Kaplan-Meier survival curve. A Cox proportional hazard model was used for multivariate analysis to identify the risk factors for seizure recurrence after univariate analysis. P value < 0.05 was considered significant, and all P values were two-tailed. Results95 epilepsy patients with cause unknown between 9 to 45 years old were recruited (63 in normal twice group and 32 in normal once group). The cumulated recurrence rates in continual two normal VEEG group vs one normal VEEG group were 4.8% vs 21.9% (P=0.028), 4.8% vs 25% (P=0.006) and 7.9% vs 25%(P=0.03) at 18 months, 24 months and endpoint following AEDs withdrawal and there was statistically difference between the two groups. Factors associated with increased risk were adolescent onset epilepsy (HR=2.404), history of withdrawal recurrence (HR=7.186) and abnormal VEEG (epileptic-form discharge) (HR=8.222) during or after withdrawal AEDs. The recurrence rate of each group in which abnormal VEEG vs unchanged VEEG during or after withdrawal AEDs was respectively 100% vs 4.92% (P=0.005), 80% vs 19.23%(P=0.009). ConclusionsContinual normal 24h VEEG twice before withdrawal AEDs had higher predicting value of seizure recurrence and it could guide physicians to make the withdrawal decision. Epileptic patients with adolescent onset epilepsy, history of seizure recurrence and abnormal VEEG (epileptic-form discharge) during or after withdrawal AEDs had high risk of replase, especially patients with the presence of VEEG abnormalities is associated with a high probability of seizures occurring. Discontinuate AEDs should be cautious.
ObjectiveTo investigate the application of stereoelectroencephalography (SEEG) in the refractory epilepsy related to periventricular nodular heterotopia (PNH). MethodsTen patients with drug-resistant epilepsy related to PNHs from Guangdong Sanjiu Brain Hospital and the First Affiliated Hospital of Jinan University from April 2017 to February 2021 were studied. Electrodes were implanted based on non-invasive preoperative evaluation. Then long-term monitoring of SEEG was carried out. The patterns of epileptogenic zone (EZ) were divided into four categories based on the ictal SEEG: A. only the nodules started; B. nodules and cortex synchronous initiation; C. the cortex initiation with early spreading to nodules; D. only cortex initiation. All patients underwent SEEG-guided radiofrequency thermocoagulation (RFTC), with a follow-up of at least 12 months. ResultsAll cases were multiple nodules. Four cases were unilateral and six bilateral. Eight cases were distributed in posterior pattern, and one in anterior pattern and one in diffused pattern, respectively. Seven patients had only PNH (pure PNH) and three patients were associated with other overlying cortex malformations (PNH plus). The EZ patterns of all cases were confirmed by the ictal SEEG: six patients were in pure type A, two patients were in pure type B, one patient in type A+B and one in type A+B+C, respectively. In eight patients SEEG-guided RF-TC was targeted only to PNHs; and in two patients RFTC was directed to both heterotopias and related cortical regions. The mean follow up was (33.4±14.0) months (12 ~ 58 months). Eight patients (in pure type A or type A included) were seizure free. Two patients were effective. None of the patients had significant postoperative complications or sequelae. ConclusionThe epileptic network of Epilepsy associated with nodular heterotopia may be individualized. Not all nodules are always epileptogenic, the role of each nodule in the epileptic network may be different. And multiple epileptic patterns may occur simultaneously in the same patient. SEEG can provide individualized diagnosis and treatment, be helpful to prognosis.
Objective To research clinical manifestations, electrophysiological characteristics of epileptic seizures arising from diagonal sulci (DS), to improve the level of the diagnosis and treatment of frontal epilepsy. MethodsWe reviewed all the patients underwent a detailed presurgical evaluation, including 5 patients with seizures to be proved originating from diagonal sulci by Stereo-electroencephalography (SEEG). All the 5 patients with detailed medical history, head Magnetic resonance (MRI), the Positron emission computered tomography (PET-CT) and psychological evaluation, habitual seizures were recorded by Video-electroencephalography (VEEG) and SEEG, we review the intermittent VEEG and ictal VEEG, analyzing the symptoms of seizures. Results 5 patients were divided into 2 groups by SEEG, group 1 including 3 patients with seizures arising from the bottom of DS, group 2 including 2 patients with seizures arising from the surface of DS, all the tow groups with seizures characterized by both having tonic and complex motors, tonic seizures were prominent in seizures from left DS, and tonic seizures may absent in seizures from right DS. Intermittent discharges with group1 were diffused, and intermittent discharges with group 2 were focal, but both brain areas of frontal and temporal were infected. Ictal EEG findings were consistent with the characteristics of neocortical seizures, the onset EEG shows voltage attenuation, seizures from bottom of DS with diffused EEG onset, and seizures from surface of DS with more focal EEG onset, but both frontal and anterior temporal regions were involved. Conclusionthe symptom of seizures arising from DS characterized by tonic and complex motor, can be divided into seizures arising from the bottom of DS and seizures from the surface of DS, with different electrophysiological characters.
ObjectiveTo evaluate the application of stereotactic electrode implantation on precise epileptogenic zone localization. MethodRetrospectively studied 140 patients with drug-resist epilepsy from March 2012 to June 2015, who undergone a procedure of intracranial stereotactic electrode for localized epileptogenic zone. ResultsIn 140 patients who underwent the ROSA navigated implantation of intracranial electrode, 109 are unilateral implantation, 31 are bilateral; 3 patients experienced an intracranial hematoma caused by the implantation. Preserved time of electrodes, on average, 8.4days (range 2~35 days); Obseved clinical seizures, on average, 10.8 times per pt (range 0~98 times); There were no cerebrospinal fluid leak, intracranial hematoma, electrodes fracture or patient death, except 2 pt's scalp infection (1.43%, scalp infection rate); 131 pts' seizure onset area was precisely localized; 71 pts underwent SEEG-guide resections and were followed up for more than 6 months. In the group of 71 resection pts, 56 pts were reached Engel I class, 2 were Engel Ⅱ, 3 was Engel Ⅲ and 10 were Engel IV class. ConclusionTo intractable epilepsy, when non-invasive assessments can't find the epileptogenic foci, intracranial electrode implantation combined with long-term VEEG is an effective method to localize the epileptogenic foci, especially the ROSA navigated stereotactic electrode implantation, which is a micro-invasive, short-time, less-complication, safe-guaranteed, and precise technique.
ObjectiveTo study the therapeutic efficacy of stereoelectroencephalography (SEEG)-guided radiofrequency thermo-coagulation ablation (RF-TC) in the treatment of tuberous sclerosis (TSC) related epilepsy and to investigate the prediction of the therapeutic response to SEEG-guided RF-TC for the efficacy of the subsequent surgical treatment. MethodsWe retrospectively analyze TSC patients who underwent SEEG phase II evaluation from January 2014 to January 2023, and to select patients who underwent RF-TC after completion of SEEG monitoring, study the seizure control of patients after RF-TC, and classify patients into effective and ineffective groups for RF-TC treatment according to the results of RF-TC treatment, compare the surgical outcomes of patients in the two groups after SEEG, to explore the prediction of surgical outcome by RF-TC treatment. Results59 patients with TSC were enrolled, 53 patients (89.83%) were genetic detection, of which 28 (52.83%) were TSC1-positive, 21 (39.62%) were TSC2-positive, and 4 (7.54%) were negative, with 33 (67.34%) de novo mutations. The side of the SEEG electrode placement: left hemisphere in 9 cases, right hemisphere in 13 cases, and bilateral hemisphere in 37 cases. 37 patients (62.71%) were seizure-free at 3 months, 31 patients (52.54%) were seizure-free at 6 months, 29 patients (49.15%) were seizure-free at 12 months, and 20 patients (39.21%) were seizure-free at 24 months or more. 11 patients had a seizure reduction of more than 75% after RF-TC, and the remaining 11 patients showed no significant change after RF-TC. There were 48 patients (81.35%) in the effective group and 11 patients (18.65%) in the ineffective group. In the effective group, 22 patients were performed focal tuber resection laser ablation, 19 cases were seizure-free (86.36%). In the ineffective group, 10 patients were performed focal tuber resection laser ablation, only 5 cases were seizure-free (50%), which was a significant difference between the two groups (P<0.05). ConclusionsOur data suggest that SEEG guided RF-TC is a safe and effective both diagnostic and therapeutic treatment for TSC-related epilepsy, and can assist in guiding the development of future resective surgical strategies and determining prognosis.
Aiming at the problem that the feature extraction ability of forehead single-channel electroencephalography (EEG) signals is insufficient, which leads to decreased fatigue detection accuracy, a fatigue feature extraction and classification algorithm based on supervised contrastive learning is proposed. Firstly, the raw signals are filtered by empirical modal decomposition to improve the signal-to-noise ratio. Secondly, considering the limitation of the one-dimensional signal in information expression, overlapping sampling is used to transform the signal into a two-dimensional structure, and simultaneously express the short-term and long-term changes of the signal. The feature extraction network is constructed by depthwise separable convolution to accelerate model operation. Finally, the model is globally optimized by combining the supervised contrastive loss and the mean square error loss. Experiments show that the average accuracy of the algorithm for classifying three fatigue states can reach 75.80%, which is greatly improved compared with other advanced algorithms, and the accuracy and feasibility of fatigue detection by single-channel EEG signals are significantly improved. The results provide strong support for the application of single-channel EEG signals, and also provide a new idea for fatigue detection research.
Stereo-electroencephalography (SEEG) is widely used to record the electrical activity of patients' brain in clinical. The SEEG-based epileptogenic network can better describe the origin and the spreading of seizures, which makes it an important measure to localize epileptogenic zone (EZ). SEEG data from six patients with refractory epilepsy are used in this study. Five of them are with temporal lobe epilepsy, and the other is with extratemporal lobe epilepsy. The node outflow (out-degree) and inflow (in-degree) of information are calculated in each node of epileptic network, and the overlay between selected nodes and resected nodes is analyzed. In this study, SEEG data is transformed to bipolar montage, and then the epileptic network is established by using independent effective coherence (iCoh) method. The SEEG segments at onset, middle and termination of seizures in Delta, Theta, Alpha, Beta, and Gamma rhythms are used respectively. Finally, the K-means clustering algorithm is applied on the node values of out-degree and in-degree respectively. The nodes in the cluster with high value are compared with the resected regions. The final results show that the accuracy of selected nodes in resected region in the Delta, Alpha and Beta rhythm are 0.90, 0.88 and 0.89 based on out-degree values in temporal lobe epilepsy patients respectively, while the in-degree values cannot differentiate them. In contrast, the out-degree values are higher outside the temporal lobe in the patient with extratemporal lobe epilepsy. Based on the out-degree feature in low-frequency epileptic network, this study provides a potential quantitative measure for identifying patients with temporal lobe epilepsy in clinical.
In recent years, it has become a new direction in the field of neuroscience to explore the mode characteristics, functional significance and interaction mechanism of resting spontaneous electroencephalography (EEG) and task-evoked EEG. This paper introduced the basic characteristics of spontaneous EEG and task-evoked EEG, and summarized the core role of spontaneous EEG in shaping the adaptability of the nervous system. It focused on how the spontaneous EEG interacted with the task-evoked EEG in the process of task processing, and emphasized that the spontaneous EEG could significantly affect the performance of tasks such as perception, cognition and movement by regulating neural activities and predicting external stimuli. These studies provide an important theoretical basis for in-depth understanding of the principle and mechanism of brain information processing in resting and task states, and point out the direction for further exploring the complex relationship between them in the future.
Sleep stage scoring is a hotspot in the field of medicine and neuroscience. Visual inspection of sleep is laborious and the results may be subjective to different clinicians. Automatic sleep stage classification algorithm can be used to reduce the manual workload. However, there are still limitations when it encounters complicated and changeable clinical cases. The purpose of this paper is to develop an automatic sleep staging algorithm based on the characteristics of actual sleep data. In the proposed improved K-means clustering algorithm, points were selected as the initial centers by using a concept of density to avoid the randomness of the original K-means algorithm. Meanwhile, the cluster centers were updated according to the 'Three-Sigma Rule' during the iteration to abate the influence of the outliers. The proposed method was tested and analyzed on the overnight sleep data of the healthy persons and patients with sleep disorders after continuous positive airway pressure (CPAP) treatment. The automatic sleep stage classification results were compared with the visual inspection by qualified clinicians and the averaged accuracy reached 76%. With the analysis of morphological diversity of sleep data, it was proved that the proposed improved K-means algorithm was feasible and valid for clinical practice.