Preventive Detection of Driver Drowsiness from EEG Signals using Fuzzy Expert Systems / Detección Preventiva de la Somnolencia del Conductor a partir de Señales EEG Mediante Sistemas Expertos Difusos

Abstract Currently, the percentage of traffic accidents has increased, and according to statistics, this percentage will continue to increase every year, so it is necessary to develop new technologies to prevent this kind of accidents. This paper presents a drowsiness detection system based on electroencephalogram (EEG) signals using a pair of channels (Fp1 and Fp2) applied to drivers before entering their vehicles. First, this model detects the relationship between the area under the curve (AUC) of alpha brain waves, an effective parameter for detecting drowsiness. Then, the extracted information is passed to a fuzzy expert system (FES) that classifies the subject's state as "alert" or "sleepy"; the criterion used was a threshold and training with subjective levels. The proposed system was compared with neural network models, such as support vector machine (SVM), K nearest neighbors (KNN), and random forest (RF). Measurements of one hundred and twenty minutes were performed on each of the ten drivers for two days to test the system. The tests confirm that this system is suitable for preventive measures and that the fuzzy system is superior to traditional neural network methods.

Resumen Actualmente, el porcentaje de accidentes de tráfico ha aumentado, y según las estadísticas, este porcentaje seguirá aumentando cada año, por lo que es necesario desarrollar nuevas tecnologías para prevenir este tipo de accidentes. Este trabajo presenta un sistema de detección de somnolencia basado en señales de electroencefalograma (EEG) utilizando un par de canales (Fp1 y Fp2) aplicado a los conductores antes de entrar en sus vehículos. En primer lugar, este modelo detecta la relación entre el área bajo la curva (AUC) de las ondas cerebrales alfa, un parámetro eficaz para detectar la somnolencia. A continuación, la información extraída se pasa a un sistema experto difuso (FES) que clasifica el estado del sujeto como "alerta" o "somnoliento"; el criterio utilizado fue un umbral y el entrenamiento con niveles subjetivos. El sistema propuesto se comparó con modelos de redes neuronales, como la máquina de vectores de soporte (SVM), K vecinos más cercanos (KNN) y el bosque aleatorio (RF). Se realizaron mediciones de ciento veinte minutos en cada uno de los diez conductores durante dos días para probar el sistema. Las pruebas confirman que este sistema es adecuado para las medidas preventivas y que el sistema difuso es superior a los métodos tradicionales de redes neuronales.

Study of the Length of time Window in Emotion Recognition based on EEG Signals / Estudio de la Longitud de Ventana de Tiempo en el Reconocimiento de Emociones Basado en Señales EEG

Abstract The objective of this research is to present a comparative analysis using various lengths of time windows (TW) during emotion recognition, employing machine learning techniques and the portable wireless sensing device EPOC+. In this study, entropy will be utilized as a feature to evaluate the performance of different classifier models across various TW lengths, based on a dataset of EEG signals extracted from individuals during emotional stimulation. Two types of analyses were conducted: between-subjects and within-subjects. Performance measures such as accuracy, area under the curve, and Cohen's Kappa coefficient were compared among five supervised classifier models: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Decision Trees (DT). The results indicate that, in both analyses, all five models exhibit higher performance in TW ranging from 2 to 15 seconds, with the 10 seconds TW particularly standing out for between-subjects analysis and the 5-second TW for within-subjects; furthermore, TW exceeding 20 seconds are not recommended. These findings provide valuable guidance for selecting TW in EEG signal analysis when studying emotions.

Resumen El objetivo de esta investigación es presentar un análisis comparativo empleando diversas longitudes de ventanas de tiempo (VT) durante el reconocimiento de emociones, utilizando técnicas de aprendizaje automático y el dispositivo de sensado inalámbrico portátil EPOC+. En este estudio, se utilizará la entropía como característica para evaluar el rendimiento de diferentes modelos clasificadores en diferentes longitudes de VT, basándose en un conjunto de datos de señales EEG extraídas de individuos durante la estimulación de emociones. Se llevaron a cabo dos tipos de análisis: entre sujetos e intra-sujetos. Se compararon las medidas de rendimiento, tales como la exactitud, el área bajo la curva y el coeficiente de Cohen's Kappa, de cinco modelos clasificadores supervisados: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF) y Decision Trees (DT). Los resultados indican que, en ambos análisis, los cinco modelos presentan un mayor rendimiento en VT de 2 a 15 segundos, destacándose especialmente la VT de 10 segundos para el análisis entre los sujetos y 5 segundos intrasujetos; además, no se recomienda utilizar VT superiores a 20 segundos. Estos hallazgos ofrecen una orientación valiosa para la elección de las VT en el análisis de señales EEG al estudiar las emociones.

The early prediction of umbilical cord blood S100β protein and lactate combined with amplitude integrated electroencephalogram in small for gestational age preterm infants with brain injury / 国际儿科学杂志

Objective:To explore the early predictive value of umbilical cord blood S100β protein and lactate combined with amplitude integrated electroencephalogram（aEEG）in small for gestational age（SGA）preterm infants with brain injury.Methods:One hundred and six cases of SGA preterm infants were enrolled in this study in Neonatology Department of Inner Mongolia People's Hospital from January 2019 to December 2021. Umbilical cord blood serum S100β protein and lactate at birth of All SGA preterm infants were tested，and aEEG was monitored at 6h and 72 h after birth，corrected gestational age of 32 weeks and 37 weeks. According to the diagnostic criteria of brain injury in preterm infants，SGA preterm infants were divided into brain injury group（45 cases）and non-brain injury group（61 cases），and compared the differences of S100β protein，lactate and the designated time aEEG between the two groups.SGA preterm infants with brain injury were further divided into symmetrical group（28 cases）and non-symmetrical group（15 cases）. The differences of umbilical cord blood S100β protein and lactate level between the two groups were compared，and the diagnostic value in different types of SGA preterm infants with brain injury was also compared.Results:SGA preterm infants in the brain injury group had significantly higher levels of umbilical cord blood S100β protein［（0.826±0.218）μg/L vs（0.397±0.196）μg/L， t=8.316， P<0.05］and lactate［（8.5±1.3）mmol/L vs（3.8±0.9）mmol/L， t=3.281， P<0.05］than those in non-brain injury group.Symmetric SGA group had higher level of S100β protein than the asymmetric SGA group［（0.924±0.205）μg/L vs（0.438±0.196）μg/L， t=5.734， P<0.05］.But there was no statistically significant difference in lactate levels［（5.6±1.4）mmol/L vs（3.9±1.2）mmol/L， t=0.932， P>0.05］between symmetric SGA group and asymmetric SGA group. The abnormal rates of aEEG in brain injury group and non-brain injury group were respectively 100%（45/45）vs 22.95%（14/61）at 6 h after birth，95.56%（43/45）vs 16.39%（10/61）at 72 h after birth，62.22%（28/45）vs 6.56%（4/61）at 32 weeks of corrected gestational age，22.22%（10/45）vs 3.28%（2/61）at 37 weeks of corrected gestational age. The abnormal rate of brain injury group was higher than the non-brain injury group in the same nodal time，and the differences were statistically significant（ χ 2 value respectively 62.292，64.913，38.074，9.257，all P<0.05）. Conclusion:There were significant value in umbilical cord blood S100β protein，lactate level and aEEG monitoring in the early diagnosis in preterm infants SGA with brain injury. The combination of the three might be more helpful for the early diagnosis and timely treatment of brain injury in SGA preterm infants.

Suggestions for revision of the epilepsy items in the military disability appraisal norms / 解放军医学杂志

Epilepsy is a common neurological disease,has the characteristics of recurrent attacks and long-term treatment,thus bringing great pressure to patients and their families.Therefore,it is particularly important to do a good job of disability assessment.In recent years,with the development of the discipline,academic organizations such as the International League Against Epilepsy(ILAE)and China Association Against Epilepsy(CAAE)have successively updated the definition and diagnostic criteria of epilepsy and seizures.However,some items of epilepsy in the current Criteria for Disability Rating of Military Personnel(Trial)issued by People's Liberation Army(PLA)in 2011 can no longer meet the latest guidelines at home and abroad.Therefore,we suggest that the items related to epilepsy in the Criteria for Disability Rating of Military Personnel(Trial)should be revised to ensure that the disability evaluation being completed fairly and successfully.

Central mechanism of perioperative neurocognitive disorders:research progress based on electroen-cephalogram and magnetic resonance imaging / 临床麻醉学杂志

Perioperative neurocognitive disorders(PND)are common perioperative diseases,which bring heavy burden to patients and society.Due to complex pathogenesis of PND and the lack of relia-ble diagnosis and intervention means,and electroencephalography(EEG)and magnetic resonance imaging(MRI)have the advantage of providing objective indicators,so their application in the study of PND has gradually become a hot topic.In this review,the intraoperative processed EEG indices,EEG spectral analy-sis,EEG functional connectivity analysis,EEG nonlinear dynamics analysis,and perioperative MRI analysis in patients with PND are reviewed,aiming to explore the clinical value of EEG and MRI in predic-ting and diagnosing PND.

Application of brain-computer interface technology based on electroencephalogram in upper limb motor function rehabilitation of stroke patients / 中国组织工程研究

BACKGROUND:Current rehabilitation programs are effective in treating post-stroke sequelae,but the treatment cycle is long and the labor cost is high.Brain-computer interface technology can be used for the treatment of post-stroke patients by extracting signals from the brain's neural activity through special equipment and converting this signal into commands that can be recognized by a computer. OBJECTIVE:To analyze and summarize the application of brain-computer interface technology in the upper limb motor function rehabilitation of stroke patients in recent years and to explore the clinical value of brain-computer interface technology in the upper limb function rehabilitation of stroke patients. METHODS:CNKI and PubMed were retrieved for relevant literature published from 2000 to 2022.The keywords were"stroke,electroencephalogram,brain-computer interface,upper limb,virtual reality technology,functional electrical stimulation,exoskeleton"in Chinese and"stroke,brain-computer interface,computer assistance,upper limb,virtual reality technology,functional electrical stimulation,exoskeleton"in English. RESULTS AND CONCLUSION:The brain-computer interface has shown promise for the restoration of upper limb motor function in stroke patients and has been shown to produce results that are unattainable with conventional treatments,and is well worth further research and promotion,but the mechanisms have not been fully elucidated.Also the ability to accurately decode all degrees of freedom of upper limb movements to provide flexible and natural control remains a challenge from the perspective of brain-computer interface systems that capture electroencephalogram signals from patients.Future research should focus on clarifying the specific neural mechanisms by which brain-computer interface technology facilitates upper limb motor recovery after stroke and identifying rehabilitation options such as brain-computer interfaces combined with external devices to facilitate upper limb motor function recovery in stroke patients.

Drug-resistant epileptic rat models of cognitive impairment established by different ways: a comparative study / 中华神经医学杂志

Objective:To investigate the behavioral, electroencephalographic, and cognitive functional differences in drug-resistant epileptic rat models of cognitive impairment prepared by intraperitoneal injection of lithium chloride-pilocarpine followed by intracranial injection of pilocarpine or carbamylcholine.Methods:One hundred and sixty adult male SD rats were randomly divided into normal control group ( n=10), lithium chloride-pilocarpine group (establishing epileptic rat models by intraperitoneal injection of lithium chloride-pilocarpine, n=50), pilocarpine-pilocarpine group (intracranial injection of pilocarpine after intraperitoneal injection of lithium chloride-pilocarpine, n=50)and pilocarpine-carbamylcholine group (intracranial injection of carbamylcholine after intraperitoneal injection of lithium chloride-pilocarpine, n=50). Frequency and duration of spontaneously recurrent seizures (SRSs) were observed by video monitoring system, and 2 weeks after that, phenobarbital and phenytoin sodium were injected intraperitoneally to screen drug-resistant models. Frequency and amplitude of the epileptic waves in EEG were recorded by BL-420 Bio-signal Acquisition and Processing System. Novel object recognition experiment was used to detect the novel exploration, Y-maze free exploration experiment and new and different arm experiment were used to detect the spatial recognition and memory ability, and Morris water maze experiment was used to detect the spatial memory ability. Results:(1) Twenty-four rats (48.00%) survived in the lithium chloride-pilocarpine group, 25 (78.00%) in the pilocarpine-pilocarpine group, and 21 (65.62%) in the pilocarpine-carbamylcholine group; and ultimately 7, 9, and 8 drug-resistant epileptic rat models were identified, respectively; frequency and duration of SRSs in the pilocarpine-pilocarpine group and pilocarpine-carbamylcholine group were significantly higher/longer than those in the lithium chloride-pilocarpine group ( P<0.05). (2) The pilocarpine-pilocarpine group and pilocarpine-carbamylcholine group had significantly higher amplitude of the epileptic waves in EEG compared with the lithium chloride-pilocarpine group ( P<0.05); the frequency of the epileptic waves in EEG increased gradually in the lithium chloride-pilocarpine group, pilocarpine-pilocarpine group, and pilocarpine-carbamylcholine group ( P<0.05). (3) Discrimination index, accuracy, ratio of distance traveled in novel arm to total distance, and time of novel arm entries gradually decreased in the normal control group, lithium chloride-pilocarpine group, pilocarpine-pilocarpine group, and pilocarpine-carbamylcholine group, with significant differences ( P<0.05). (4) Compared with the normal control group, the pilocarpine-pilocarpine group and pilocarpine-carbamylcholine group had significantly decreased frequency in crossing the original platform ( P<0.05); compared with the normal control group, lithium-pilocarpine chloride group and pilocarpine-pilocarpine group, the pilocarpine-carbamylcholine group had statistically shorter distance of target quadrant activity ( P<0.05); number of entries in the target quadrant gradually decreased in the normal control group, lithium chloride-pilocarpine group, pilocarpine-pilocarpine group, and pilocarpine-carbamylcholine group, with significant differences ( P<0.05). Conclusion:Drug-resistant epileptic rat models established by intracranial injection of carbamylcholine after intraperitoneal injection of lithium chloride-pilocarpine have high survival rate, high SRSs rate, and severe cognitive impairment, which is suitable for studying drug-resistant epilepsy combined with cognitive impairment.

Effect of high-frequency repetitive transcranial magnetic stimulation in M1 region combined with dorsolateral prefrontal cortex on electroencephalogram θ frequency band amplitude of patients with neuropathic pain after spinal cord injury / 中国康复理论与实践

ObjectiveTo explore the efficacy of high-frequency repetitive transcranial magnetic stimulation (rTMS) in M1 region combined with dorsolateral prefrontal cortex (DLPFC) on electroencephalogram (EEG) θ frequency band amplitude of patients with neuropathic pain (NP) after spinal cord injury. MethodsFrom June, 2022 to June, 2023, 50 NP patients after SCI in Qingdao University Affiliated Hospital were included and divided into M1 region stimulation group (n = 25) and M1 region combined with DLPFC stimulation group (the combined stimulation group, n = 25). M1 region stimulation group received 10 Hz rTMS in the left M1 region, while the combined stimulation group received same stimulation in left M1 region combined with DLPFC, for three weeks. Before and after intervention, the pain was assessed with Short Form of McGill Pain Questionnaire (SF-MPQ), the depression and anxiety status were evaluated using Hamilton Depression Scale (HAMD) and Hamilton Anxiety Scale (HAMA), and the EEG θ frequency band amplitude was recorded to detect the changes of brain electrophysiological activity. ResultsFour cases in M1 region stimulation group, and two cases in the combined stimulation group were dropped. After intervention, the total score of SF-MPQ and the scores of the subscales, the scores of HMMD and HAMA decreased in both groups (|t| > 2.523, P < 0.05). The EEG θ frequency band amplitude significantly reduced in the prefrontal and frontal regions in M1 region stimulation group (|t| > 5.243, P < 0.001), and it also significantly reduced in the prefrontal, frontal regions, central and parietal regions in the combined stimulation group (|t| > 4.630, P < 0.001). All the scores were lower (|t| > 2.270, Z = -1.973, P < 0.05), and the EEG θ frequency band amplitude in the prefrontal, frontal regions, central and parietal regions were lower (P < 0.05) in the combined stimulation group than in M1 region stimulation group. ConclusionHigh frequency rTMS is an effective analgesic method on NP after SCI, which can improve their depression and anxiety symptoms and reduce the EEG θ frequency band amplitude. Compared with M1 region rTMS stimulation, the combination of M1 region and DLPFC rTMS is more effective.

Multisensory Conflict Impairs Cortico-Muscular Network Connectivity and Postural Stability: Insights from Partial Directed Coherence Analysis / 神经科学通报·英文版

Sensory conflict impacts postural control, yet its effect on cortico-muscular interaction remains underexplored. We aimed to investigate sensory conflict's influence on the cortico-muscular network and postural stability. We used a rotating platform and virtual reality to present subjects with congruent and incongruent sensory input, recorded EEG (electroencephalogram) and EMG (electromyogram) data, and constructed a directed connectivity network. The results suggest that, compared to sensory congruence, during sensory conflict: (1) connectivity among the sensorimotor, visual, and posterior parietal cortex generally decreases, (2) cortical control over the muscles is weakened, (3) feedback from muscles to the cortex is strengthened, and (4) the range of body sway increases and its complexity decreases. These results underline the intricate effects of sensory conflict on cortico-muscular networks. During the sensory conflict, the brain adaptively decreases the integration of conflicting information. Without this integrated information, cortical control over muscles may be lessened, whereas the muscle feedback may be enhanced in compensation.

Advances on sleep electroencephalogram in the subtyping and treatment of insomnia disorder / 中华行为医学与脑科学杂志

Insomnia disorder is a common clinical mental disorder.Currently, clinical subtyping of insomnia disorder relies primarily on symptomatic descriptions, lacking objective measures and subtyping-based treatment approaches. In recent years, increasing attention has been drawn to sleep electroencephalography (EEG) as a valuable tool for observing abnormal sleep architecture and continuity of insomnia disorder. Sleep EEG analysis holds the potential to elucidate the underlying biological mechanisms of insomnia disorder, facilitating data-driven subtyping and enhancing personalized therapeutic strategies.Five types of sleep EEG subtypes of insomnia disorder were systematically searched and summarized: classifications derived from objective sleep duration, power spectral characteristics, cyclic alternating pattern, spindle and microarousal.EEG characteristics of each subtype and clinical outcomes are discussed.This review aims to provide evidence-based insights for clinical subtyping and personalized treatment of insomnia disorder.

Advances in application of EEG-fNIRS technology in researches on neuropsychiatric disorders / 中国医学物理学杂志

Currently,electroencephalogram(EEG),functional near-infrared spectroscopy(fNIRS),and functional magnetic resonance imaging have been widely studied and applied to neuropsychiatric disorders.In recent years,the devices which can realize the simultaneous acquisition of EEG and fNIRS has been developed and gradually applied in the studies on neuropsychiatric disorders.The review provides an introduction of the techniques of synchronized detection and data analysis for EEG-fNIRS,summarizes the analysis methods and new findings of the recent studies of stroke,epilepsy,and other neuropsychiatric disorders using EEG-fNIRS,and also discusses the future research directions.

Correlation between serum homocysteine and quantitative electroencephalogram and prognosis of cerebral hemorrhage / 中国综合临床

Objective:To explore the predictive value of admission serum homocysteine levels and quantitative electroencephalogram (qEEG) indicators for adverse outcomes in patients with cerebral hemorrhage.Methods:A retrospective study was conducted on 89 patients, who were collected as the study objects with hemorrhagic stroke treated in the neurology intensive care unit at Kailuan General Hospital from January 2017 to December 2022. Patients were categorized into two groups based on modified Rankin Scale (mRS) scores at discharge: a good prognosis group (mRS≤2) and a poor prognosis group (mRS 3-6). Clinical data and qEEG monitoring of various brain regions were collected. The impact factors of hemorrhagic prognosis were analyzed using multifactorial logistic regression. ROC curve analysis was performed to assess the predictive value of qEEG and admission homocysteine levels for adverse outcomes in hemorrhagic stroke patients.Results:(1) The age of the poor prognosis group was higher than that of the good prognosis group((66.51+13.64) to (60.53+11.69), t=2.15, P=0.034) and admission serum homocysteine levels were significantly higher in the poor prognosis group than in the good prognosis group (17.28(15.52,24.72)mmol/L to 14.50(10.28,16.00)mmol/L, Z=4.14, P<0.001). (2) In the poor prognosis group, power values of δ brain waves in leads Fp1-2, F4, C4, P4, F8, and T4 were higher than those in the good prognosis group (87.99(41.57,196.69) to 50.67(26.64,54.75), Z=2.76, P=0.006); (79.17(40.71,200.00) to 45.06(20.22,61.00), Z=2.10, P=0.036); (72.64(34.97,219.78) to 34.42(19.81,63.4), Z=2.03, P=0.043); (65.06(33.36,177.45) to 28.12(15.88,63.36), Z=2.08, P=0.038); (52.92(25.64,187.91) to 23.61(11.67,43.26), Z=2.21, P=0.027); (66.67(32.56,180.76) to 36.31(17.2,53.78), Z=2.46, P=0.014); (57.30(25.24,127.04) to 29.57(11.91,41.89), Z=2.26, P=0.024). Power values of θ brain waves in leads Fp1-2, F3, F4, C3, C4, P3-4, O1, F7-8, and T3-4 were higher in the poor prognosis group(77.45(47.63,138.72)比35.88(20.92,44.81), Z=3.50, P<0.001); (77.05(35.16,120.22) to 38.74(19.86,58.09), Z=2.27, P=0.023); (85.24(52.53,147.90) to 35.42(14.7,52.59), Z=2.61, P=0.009); (75.81(37.90,124.97) to 36.85(17.92,55.43), Z=2.30, P=0.021); (72.00(43.92,123.54) to 28.37(14.02,51.9), Z=2.22, P=0.027); (67.08(32.01,104.05) to 31.32(17.98,45.28), Z=2.10, P=0.035); (55.33(32.29,94.30) to 25.64(11.87,34.01), Z=2.24, P=0.025); (48.84(20.64,96.28) to 19.85(9.83,28.58), Z=2.30, P=0.022);(48.46(25.06,81.78) to 23.95(8.80,29.16), Z=2.51, P=0.012); (64.46(39.38,112.44) to 26.85(15.74,39.58), Z=2.80, P=0.005); (65.68(31.78,102.00) to 31.09(15.98,46.96), Z=2.38, P=0.017); (45.26(28.34,73.14) to 21.45(10.57,36.59), Z=2.04, P=0.042); (43.50(22.58,78.67) to 25.45(11.91,32.26), Z=2.22, P=0.027). Power values of slow-wave index in leads Fp1-2, F3-4, C3-4, P4, F7-8, and T4, as well as the overall brain average, were higher in the poor prognosis group (6.64(2.98,10.42) to 3.65(2.31,4.30), Z=2.65, P=0.01); (6.53(3.96,11.65) to 3.53(2.56,4.51), Z=2.30, P=0.022); (7.38(4.62,13.12) to 3.83(1.70,4.71), Z=2.38, P=0.017); (5.88(4.02,12.15) to 3.18(2.21,4.46), Z=2.29, P=0.022); (6.13(3.83,11.22) to 2.97(1.53,4.58), Z=2.01, P=0.044); (6.07(3.53,9.39) to 2.74(2.00,3.81), Z=2.40, P=0.016);(4.11(2.51,9.23) to 2.18(1.37,2.82), Z=2.25, P=0.024); (5.71(3.81,10.44) to 3.22(1.86,4.04), Z=2.28, P=0.023); (6.00(3.65,10.37) to 3.04(2.00,4.00), Z=2.39, P=0.017); (4.08(2.56,8.33) to 2.08(1.60,3.14), Z=2.50, P=0.013), with significant statistical differences noted (5.45(3.31,10.08) to 3.17(2.02,4.88), Z=3.62, P=0.005). (3) Logistic regression results showed that admission homocysteine levels ( OR 1.311,95% CI 1.008-1.705, P=0.044), admission NIHSS scores ( OR 1.588,95% CI 1.074-2.349, P=0.020), and overall brain average slow-wave index were influencing factors for poor prognosis in cerebral hemorrhage ( OR 8.596,95% CI 1.088-67.889, P=0.041). (4) ROC curve analysis revealed that the AUC for predicting adverse outcomes in cerebral hemorrhage was 0.768 (95% CI (0.665, 0.872)) for admission homocysteine levels, 0.743 (95% CI (0.634, 0.852)) for the overall brain average slow-wave index, and 0.896 (95% CI (0.827, 0.965)) for admission NIHSS. The cutoff values were 15.67, 3.62, and 8.5, respectively. Sensitivity was 77.8%, 71.1%, and 68.9%, and specificity was 59.4%, 68.7%, and 100%, respectively. The Youden indices were 0.372, 0.398, and 0.689. Conclusion:In the acute phase of cerebral hemorrhage, electroencephalographic physiological changes manifest shows an increase in the δ, θ, and slow-wave index throughout the entire brain. Higher admission homocysteine levels suggest a worse prognosis in patients with cerebral hemorrhage. Admission homocysteine levels and overall brain average slow-wave index have certain predictive value for adverse outcomes in acute cerebral hemorrhage.

Application and Challenges of EEG Signals in Fatigue Driving Detection / 生物化学与生物物理进展

People frequently struggle to juggle their work, family, and social life in today’s fast-paced environment, which can leave them exhausted and worn out. The development of technologies for detecting fatigue while driving is an important field of research since driving when fatigued poses concerns to road safety. In order to throw light on the most recent advancements in this field of research, this paper provides an extensive review of fatigue driving detection approaches based on electroencephalography (EEG) data. The process of fatigue driving detection based on EEG signals encompasses signal acquisition, preprocessing, feature extraction, and classification. Each step plays a crucial role in accurately identifying driver fatigue. In this review, we delve into the signal acquisition techniques, including the use of portable EEG devices worn on the scalp that capture brain signals in real-time. Preprocessing techniques, such as artifact removal, filtering, and segmentation, are explored to ensure that the extracted EEG signals are of high quality and suitable for subsequent analysis. A crucial stage in the fatigue driving detection process is feature extraction, which entails taking pertinent data out of the EEG signals and using it to distinguish between tired and non-fatigued states. We give a thorough rundown of several feature extraction techniques, such as topology features, frequency-domain analysis, and time-domain analysis. Techniques for frequency-domain analysis, such wavelet transform and power spectral density, allow the identification of particular frequency bands linked to weariness. Temporal patterns in the EEG signals are captured by time-domain features such autoregressive modeling and statistical moments. Furthermore, topological characteristics like brain area connection and synchronization provide light on how the brain’s functional network alters with weariness. Furthermore, the review includes an analysis of different classifiers used in fatigue driving detection, such as support vector machine (SVM), artificial neural network (ANN), and Bayesian classifier. We discuss the advantages and limitations of each classifier, along with their applications in EEG-based fatigue driving detection. Evaluation metrics and performance assessment are crucial aspects of any detection system. We discuss the commonly used evaluation criteria, including accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curves. Comparative analyses of existing models are conducted, highlighting their strengths and weaknesses. Additionally, we emphasize the need for a standardized data marking protocol and an increased number of test subjects to enhance the robustness and generalizability of fatigue driving detection models. The review also discusses the challenges and potential solutions in EEG-based fatigue driving detection. These challenges include variability in EEG signals across individuals, environmental factors, and the influence of different driving scenarios. To address these challenges, we propose solutions such as personalized models, multi-modal data fusion, and real-time implementation strategies. In conclusion, this comprehensive review provides an extensive overview of the current state of fatigue driving detection based on EEG signals. It covers various aspects, including signal acquisition, preprocessing, feature extraction, classification, performance evaluation, and challenges. The review aims to serve as a valuable resource for researchers, engineers, and practitioners in the field of driving safety, facilitating further advancements in fatigue detection technologies and ultimately enhancing road safety.

A Study on Brain Functional Connectivity in Patients With Disorders of Consciousness Based on Auditory Stimulation / 生物化学与生物物理进展

Objective At present, the grading evaluation of patients with disorders of consciousness (DOC) is still a focus and difficulty in related fields. Electroencephalogram (EEG) can directly read and continuously reflect scalp electrical activity generated by brain tissue structure, with high temporal resolution. Auditory stimulation is easy to operate and has broad application prospects in clinical detection of DOC. The causal network can intuitively reflect the direction of information transmission through the causal relationship between time series, helping us better understand the information interaction between different regions of the brain of patients. This paper combines EEG and causal networks to explore the differences in brain functional connectivity between patients with unresponsive arousal syndrome (VS) and those with minimum state of consciousness (MCS) under auditory stimulation. MethodsA total of 23 DOC patients were included, including 11 MCS patients and 12 VS patients. Based on the Oddball paradigm, auditory naming stimulation was performed on DOC patients and EEG signals of DOC patients were synchronously collected. The brain functional networks were constructed using multivariate Granger causality method, and the differences in node degree, clustering coefficient, global efficiency, and causal flow of the brain networks between MCS patients and VS patients were calculated. The differences in network characteristics of patients with different levels of consciousness under auditory stimulation were compared from the perspective of cooperation between brain regions. ResultsThe causal connectivity between most brain regions in MCS patients was stronger than that in VS patients, and MCS patients had more brain network connectivity edges than VS patients. The average degree (P<0.05), average clustering coefficient, and global efficiency (P<0.05) of MCS patients under naming stimulation were higher than those of VS patients. The difference in out-degree between each node of VS patients was larger, and the difference in in-degree between each node of MCS patients was smaller. The difference in in-degree of MCS patients was more significant than that of VS patients, and the inflow and outflow of information in the brain functional network of MCS patients were stronger than those of VS patients. MCS and VS patients had differences of causal flow in the frontal and temporal lobes, the direction of information transmission in the parietal lobe and central region was not the same, and MCS patients had more electrodes as causal sources than VS patients. ConclusionThe information transmission ability of MCS patients is stronger than that of VS patients under auditory naming stimulation. Compared with VS patients, MCS patients have an increase in the number of electrode channels as the causal source, an increase in information output to other brain regions, and also an increase in the information output within brain regions, which may indicate a better state of consciousness in patients. MCS patients have more electrode channels for information output in the frontal lobe than VS patients, and the number of electrode channels for changing the direction of information transmission in the frontal lobe is the highest. The frontal lobe is closely related to the level of consciousness in patients with consciousness disorders. This study can provide a theoretical basis for the grading evaluation of consciousness levels in DOC patients.

Research progress on electroencephalography in the prediction of efficacy and therapeutic mechanism for anxiety disorders / 四川精神卫生

Anxiety disorders are characterized by high prevalence and recurrence rate. Selective serotonin reuptake inhibitors （SSRIs） and cognitive behavioral therapy （CBT） are recommended as first-line treatments for anxiety disorders， while some patients do not response to either of these treatments. Therefore， exploring the neurobiological mechanisms associated with treatment response and valuable prognostic marker is of great value in guiding clinical decision making. Previous studies have reported an altered electroencephalogram （EEG） pattern in patients with anxiety disorders after treatment， and revealed a correlation between baseline EEG and treatment response， suggesting that EEG is of great value in predicting the treatment response in anxiety disorders. The purpose of this article is to delineate findings from a systematic review of the literature investigating the EEG signal in prognostic prediction and exploration of neurobiological mechanisms， so as to provide electrophysiological evidence for individualized treatment of anxiety disorders. Results of this review show that patients responding more strongly to negative emotional stimuli before treatment are more likely to benefit from SSRIs and CBT. After the CBT， no statistical difference is found in the amplitude of error-related negativity （ERN） and P1 component between pre- and post- procedure measurements， suggesting that CBT may not reduce anxiety symptoms by improving attention bias and behavioral monitoring. EEG indicators related to emotion perception under negative emotional stimuli at baseline， such as late positive potential （LPP）， may be promising markers for predicting response to treatment in anxiety disorders. ［Funded by the Science and Technology Innovation 2030-Major Project of "Brain Science and Brain-like Research" （number， 2021ZD0202004）； Capital Health Development Scientific Research Project （number， 2020-1-2121）］

Marcadores Neurales como Indicadores de Funciones Predictivas en Pacientes con Traumatismo Cerebral Moderado a Severo / Neural Markers as Indicators of Predictive Functions in Patients with Moderate to Severe Brain Trauma

Introducción. El traumatismo encéfalo craneano moderado a severo (TEC-MS) es una condición compleja que cambia la estructura y función del cerebro, afectando a personas de distintas edades. Los problemas cognitivos y motores son la mayor causa de discapacidad en individuos con TEC-MS crónico. Sin embargo, muchas de estas dificultades no son visibles de inmediato clasificándose como una "Epidemia silenciosa". Las principales alteraciones reportadas por los pacientes tienen relación con problemas de la memoria, atención y lentitud psicomotora, los cuales tienen un impacto en su independencia y funcionalidad. Objetivo. Este estudio tiene por objetivo discutir y revisar la evidencia disponible acerca de la capacidad de los pacientes crónicos con TEC-MS para generar predicciones en diferentes niveles de procesamiento cerebral. Métodos. Para esto, utilizamos desde las neurociencias el modelo teórico del código predictivo para explicar las respuestas neurofisiológicas adquiridas bajo un paradigma de predicción auditiva. Esta información es complementada con el reporte de datos preliminares de sujetos con TEC-MS y sujetos control, con el fin de ilustrar los aspectos teóricos discutidos. Conclusiones. Esto podría contribuir a una mejor comprensión de los mecanismos neurales detrás de los déficits cognitivos en esta población, aportando una perspectiva que nos oriente al desarrollo de nuestras estrategias terapéuticas.

Background. Moderate to severe traumatic brain injury (TBI-MS) is a complex condition that changes the structure and function of the brain, affecting people of different ages. Cognitive and motor problems are the major cause of disability in individuals with chronic ECT-MS. However, many of these difficulties are not immediately visible, classifying them as a "Silent Epidemic." The main alterations reported by patients are related to problems with memory, attention and psychomotor slowness, which have an impact on their independence and functionality. Objetive. This study aims to discuss and review the available evidence about the ability of chronic ECT-MS patients to generate predictions at different levels of brain processing. Methods. For this, we use the theoretical model of the predictive code from neuroscience to explain the neurophysiological responses acquired under an auditory prediction paradigm. This information is complemented with the report of preliminary data from subjects with ECT-MS and control subjects, in order to illustrate the theoretical aspects discussed. Conclusions. This could contribute to a better understanding of the neural mechanisms behind cognitive deficits in this population, providing a perspective that guides us in the development of our therapeutic strategies.

Electroencefalografía básica para monitores de profundidad anestésica / Basic electroencephalography for anesthesia depth monitors

Resumen: Los monitores de profundidad anestésica permiten guiar el estado hipnótico del paciente durante la anestesia general. Debido a su sencillez, tradicionalmente se han empleado índices de profundidad anestésica, obtenidos a través del procesamiento del electroencefalograma mediante algoritmos matemáticos, para orientar la monitorización del nivel de consciencia. Sus beneficios han sido ampliamente recogidos en la literatura científica; sin embargo, no están exentos de importantes limitaciones. No todos los anestésicos actúan en las mismas dianas moleculares ni dichos índices tienen en cuenta las características propias del paciente (comorbilidades, edades extremas, etcétera). Estas limitaciones podrían reducirse si interpretamos directamente toda la información que nos ofrecen los monitores. Presentamos una revisión que describe los conceptos básicos necesarios para su valoración directa, así como su correlación con los estados de profundidad anestésica del paciente.

Abstract: Anesthesia depth monitors allow to guide the patient's hypnotic state during general anesthesia. Traditionally, anesthetic depth indices have been used due to their simplicity to guide the monitoring of the level of consciousness. They have been obtained by processing the electroencephalogram using mathematical algorithms and their benefits have been widely reported in the scientific literature. However, they are not exempt from important limitations. Neither all anesthetics act on the same molecular targets, nor these mentioned indices take into account the patient's own characteristics (comorbidities, extreme ages, etc.). These limitations could be far reduced if we are able to understand all the information provided by the monitors. We present a review describing the basic concepts necessary for its direct assessment, as well as their correlation with the patient's anesthetic depth states.

Effect of acupuncture on children with attention deficit hyperactivity disorder / 中国康复理论与实践

ObjectiveTo observe the effect of acupuncture on children with attention deficit hyperactivity disorder (ADHD). MethodsFrom August to December, 2022, 54 children with ADHD from the outpatient clinic of the Fourth People's Affiliated Hospital of Jiangsu University were randomly divided into control group (n = 27) and observation group (n = 27). All the patients accepted electroencephalogram (EEG) neurofeedback training, while the observation group accepted acupuncture in addition, for twelve weeks. They were assessed with Integrated Visual and Auditory Continuous Performance Test (IVA-CPT), Conners Parent Symptom Questionnaire (PSQ), and EEG before and after treatment. ResultsThe comprehensive control quotient, comprehensive attention quotient and hyperactivity quotient of IVA-CPT increased in the observation group after treatment (t > 3.889, P < 0.001), and they were more in the observation group than in the control group (t > 2.040, P < 0.05); while all the factors of PSQ reduced in the observation group (t > 6.630, P < 0.001), and they were less in the observation group than in the control group (t > 3.871, P < 0.001); the mean frequency of β and sensorimotor rhythms increased in the observation group (t > 12.432, P < 0.001), and they were more in the observation group than in the control group (t > 7.561, P < 0.001); the mean frequency of θ reduced in the observation group (t = 9.966, P < 0.001), and it was less in the observation group than in the control group (t = 7.257, P < 0.001). ConclusionCombination of acupuncture is more effective on EEG in children with ADHD to improve attention, control and core symptoms.

Resting-state electroencephalogram relevance state recognition of Parkinson's disease based on dynamic weighted symbolic mutual information and k-means clustering / 生物医学工程学杂志

At present, the incidence of Parkinson's disease (PD) is gradually increasing. This seriously affects the quality of life of patients, and the burden of diagnosis and treatment is increasing. However, the disease is difficult to intervene in early stage as early monitoring means are limited. Aiming to find an effective biomarker of PD, this work extracted correlation between each pair of electroencephalogram (EEG) channels for each frequency band using weighted symbolic mutual information and k-means clustering. The results showed that State1 of Beta frequency band ( P = 0.034) and State5 of Gamma frequency band ( P = 0.010) could be used to differentiate health controls and off-medication Parkinson's disease patients. These findings indicated that there were significant differences in the resting channel-wise correlation states between PD patients and healthy subjects. However, no significant differences were found between PD-on and PD-off patients, and between PD-on patients and healthy controls. This may provide a clinical diagnosis reference for Parkinson's disease.

Automated detection of sleep-arousal using multi-scale convolution and self-attention mechanism / 生物医学工程学杂志

In clinical, manually scoring by technician is the major method for sleep arousal detection. This method is time-consuming and subjective. This study aimed to achieve an end-to-end sleep-arousal events detection by constructing a convolutional neural network based on multi-scale convolutional layers and self-attention mechanism, and using 1 min single-channel electroencephalogram (EEG) signals as its input. Compared with the performance of the baseline model, the results of the proposed method showed that the mean area under the precision-recall curve and area under the receiver operating characteristic were both improved by 7%. Furthermore, we also compared the effects of single modality and multi-modality on the performance of the proposed model. The results revealed the power of single-channel EEG signals in automatic sleep arousal detection. However, the simple combination of multi-modality signals may be counterproductive to the improvement of model performance. Finally, we also explored the scalability of the proposed model and transferred the model into the automated sleep staging task in the same dataset. The average accuracy of 73% also suggested the power of the proposed method in task transferring. This study provides a potential solution for the development of portable sleep monitoring and paves a way for the automatic sleep data analysis using the transfer learning method.