BACKGROUND A growing number of recent studies have explored underlying activity in the brain by measuring electroencephalography(EEG)in people with depression.However,the consistency of findings on EEG microstates in ...BACKGROUND A growing number of recent studies have explored underlying activity in the brain by measuring electroencephalography(EEG)in people with depression.However,the consistency of findings on EEG microstates in patients with depression is poor,and few studies have reported the relationship between EEG microstates,cognitive scales,and depression severity scales.AIM To investigate the EEG microstate characteristics of patients with depression and their association with cognitive functions.METHODS A total of 24 patients diagnosed with depression and 32 healthy controls were included in this study using the Structured Clinical Interview for Disease for The Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition.We collected information relating to demographic and clinical characteristics,as well as data from the Repeatable Battery for the Assessment of Neuropsychological Status(RBANS;Chinese version)and EEG.RESULTS Compared with the controls,the duration,occurrence,and contribution of microstate C were significantly higher[depression(DEP):Duration 84.58±24.35,occurrence 3.72±0.56,contribution 30.39±8.59;CON:Duration 72.77±10.23,occurrence 3.41±0.36,contribution 24.46±4.66;Duration F=6.02,P=0.049;Occurrence F=6.19,P=0.049;Contribution F=10.82,P=0.011]while the duration,occurrence,and contribution of microstate D were significantly lower(DEP:Duration 70.00±15.92,occurrence 3.18±0.71,contribution 22.48±8.12;CON:Duration 85.46±10.23,occurrence 3.54±0.41,contribution 28.25±5.85;Duration F=19.18,P<0.001;Occurrence F=5.79,P=0.050;Contribution F=9.41,P=0.013)in patients with depression.A positive correlation was observed between the visuospatial/constructional scores of the RBANS scale and the transition probability of microstate class C to B(r=0.405,P=0.049).CONCLUSION EEG microstate,especially C and D,is a possible biomarker in depression.Patients with depression had a more frequent transition from microstate C to B,which may relate to more negative rumination and visual processing.展开更多
Attention constitutes a fundamental psychological feature guiding our mental effort toward specific objects, concurrent with processes such as memory, reasoning, and imagination. Visual attention, crucial for selectin...Attention constitutes a fundamental psychological feature guiding our mental effort toward specific objects, concurrent with processes such as memory, reasoning, and imagination. Visual attention, crucial for selecting surrounding information, often decreases in older adults and patients with cerebrovascular disorders. Effective methods to enhance attention are scarce. Here, we investigated whether color information influences visual attention and brain activity during task performance, utilizing EEG. We examined 13 healthy young adults (seven women and six men;mean age: 21.2 ± 0.58 years) using 19-electrode electroencephalograms to assess the impact of color information on visual attention. The Clinical Assessment for Attention cancellation test was conducted under the black, red, and blue color conditions. Wilcoxon’s signed-rank test was used to assess differences in task performance (task time and error) between conditions. Spearman’s rank correlation was utilized to examine the correlation in power levels between task performance and color conditions. Significant variations in total task errors were observed among color conditions. The black condition exhibited the highest error frequency (0.7 ± 0.9 times), followed by the red condition (0.5 ± 0.8 times), with the lowest error frequency occurring in the blue (0.2 ± 0.4 times) condition (black vs. red: P = 0.03;black vs. blue: P = 0.00;red vs. blue: P = 0.032). No time difference was observed. The black condition showed negative delta and high-gamma correlations in the central electrodes. The red condition revealed positive alpha and low-gamma correlations in the frontal and occipital areas. Although no correlations were observed in the blue condition, it enhanced attentional performance. Positive alpha and low-gamma waves might be crucial for spotting attentional errors in key areas. Our findings provide insights into the effects of color information on visual attention and potential neural correlates associated with attentional processes. In conclusion, our study implies a connection between color information and attentional task performance, with blue font associated with the most accurate performance.展开更多
Purpose: Implant therapy restores masticatory function by restoring lost tooth morphology. It has been shown that mastication contributes not only to food intake and digestion, but also to the improvement of overall h...Purpose: Implant therapy restores masticatory function by restoring lost tooth morphology. It has been shown that mastication contributes not only to food intake and digestion, but also to the improvement of overall health. However, there have been no studies on the effects of implant treatment on electroencephalography (EEG). In this study, we investigated the effects of restoration of masticatory function by implant treatment on EEG and stress. Methods: 13 subjects (6 males, 7 females, age 64.1 ± 5.8 years) who had lost masticatory function due to tooth loss and 11 healthy subjects (6 males, 5 females, age 47.6 ± 2.4 years) as a control group. EEG (θ, α, β waves, α/β ratio) and salivary cortisol were measured before immediate dental implant treatment and every month of treatment for 6 months. EEG (θ, α, β waves, α/β ratio) was measured with a simple electroencephalograph miniature DAQ terminal (Intercross-410, Intercross Co., Ltd., Japan) in a resting closed-eye condition, and salivary cortisol was measured using an ELISA kit. Results: Compared to the control group, the appearance of θ and α waves were significantly decreased and β waves were increased, and α/β ratio was significantly decreased. The cortisol level of the subject group was significantly higher compared with the control group. With the course of implant treatment, the appearance of θ and α waves of the subject group increased, while β waves decreased. However, no significant difference was observed. The α/β ratio of the subject group increased from the first month after implant treatment and increased significantly after 5 and 6 months (0 vs. 5 months: p < 0.05, 0 vs. 6 months: p < 0.01). The cortisol levels in the subject group decreased from the first month after implant treatment and significantly decreased after 3 or 4 months (0 vs. 3 months: p < 0.05, 0 vs. 4 months: p < 0.01). These results suggest that tooth loss causes mental stress, which decreases brain stimulation and affects function. Restoration of masticatory function by implants was suggested to alleviate the effects on brain function and stress.展开更多
The purpose of this paper is to analyze sleep stages accurately using fast and simple classifiers based on the frequency domain of electroencephalography(EEG) signal. To compare and evaluate system performance, the ru...The purpose of this paper is to analyze sleep stages accurately using fast and simple classifiers based on the frequency domain of electroencephalography(EEG) signal. To compare and evaluate system performance, the rules of Rechtschaffen and Kales(R&K rule) were used. Parameters were extracted from preprocessing process of EEG signal as feature vectors of each sleep stage analysis system through representatives of back propagation algorithm and support vector machine (SVM). As a result, SVM showed better performance as pattern recognition system for classification of sleep stages. It was found that easier analysis of sleep stage was possible using such simple system. Since accurate estimation of sleep state is possible through combination of algorithms, we could see the potential for the classifier to be used for sleep analysis system.展开更多
<span style="font-family:Verdana;">There are few EEG studies on finger movement directions because ocular artifacts also convey directional information, which makes it hard to separate the contribution...<span style="font-family:Verdana;">There are few EEG studies on finger movement directions because ocular artifacts also convey directional information, which makes it hard to separate the contribution of EEG from that of the ocular artifacts. To overcome this issue, we designed an experiment in which EEG’s temporal dynamics and spatial information are evaluated together to improve the performance of brain-computer interface (BCI) for classifying finger movement directions. Six volunteers participated in the study. We examined their EEG using decoding analyses. Independent components (ICs) that represented brain-source signals successfully classified the directions of the finger movements with higher rates than chance level. The weight analyses of the classifiers revealed that maximal performance of the classification was recorded at the latencies prior to the onset of finger movements. The weight analyses also revealed the relevant cortical areas including the right lingual, left posterior cingulate, left inferior temporal gyrus, and right precuneus, which indicated the involvement of the visuospatial processing. We concluded that combining spatial distribution and temporal dynamics of the scalp EEG may improve BCI performance.</span>展开更多
Background: Electroencephalography (EEG) is established for evaluating several acute and chronic medical conditions of neurological basis. In much of Nigeria and Africa, it is largely unavailable and underutilized due...Background: Electroencephalography (EEG) is established for evaluating several acute and chronic medical conditions of neurological basis. In much of Nigeria and Africa, it is largely unavailable and underutilized due to scarcity of neurologists and high costs of the equipment. It offers a relatively simple and efficient way to help manage many encephalopathies if well utilized in trained hands. Aim: This study aimed to determine how physicians practicing in Enugu perceive and utilize electroencephalography routinely. Method: Physicians attending a statewide meeting in Enugu in August 2018 were consecutively recruited and a pretested questionnaire was administered after obtaining prior consent. Sociodemographic data as well as their knowledge, attitude and practice of electroencephalography were documented and analyzed. Results: There were 486 respondents (males 335: females 151) and 345 (71%) were specialists in various disciplines while 141 (29%) were general practitioners. Only 7 doctors (1.4%) claimed ignorance of electroencephalography and 6 (1.2%) stated it was not useful. Majority, 333 doctors (69.1%) believed it had no impact on routine patient management. This perception was highest for Dental Surgery (100%) and lowest for Internal Medicine (23%) specialists. Most doctors (425, 87.4%) agreed that neurologists should analyze recordings. Most physicians had no access to electroencephalography (61.7%) and had no interest in acquiring the machine (50.8%). Conclusion: Electroencephalography is an underappreciated investigative modality amongst physicians in Enugu, despite a high burden of neurological diseases in the population. More education, training and awareness of its utility are needed for medical students and doctors to reverse the trend.展开更多
Objective To explore quantitative electroencephalography in unconscious patients after severe traumatic brain injury (TBI) to predict awakening. Methods All cases were divided into two groups(the awake group 19 cases ...Objective To explore quantitative electroencephalography in unconscious patients after severe traumatic brain injury (TBI) to predict awakening. Methods All cases were divided into two groups(the awake group 19 cases and the unfavourable prognosis group 22 cases).Two weeks after admission the original EEGs were preformed in 41 patients suffering from severe TBI with duration of disturbance of展开更多
Electroencephalogram(EEG)is one of the most important bioelectrical signals related to brain activity and plays a crucial role in clinical medicine.Driven by continuously expanding applications,the development of EEG ...Electroencephalogram(EEG)is one of the most important bioelectrical signals related to brain activity and plays a crucial role in clinical medicine.Driven by continuously expanding applications,the development of EEG materials and technology has attracted considerable attention.However,systematic analysis of the sustainable development of EEG materials and technology is still lacking.This review discusses the sustainable development of EEG materials and technology.First,the developing course of EEG is introduced to reveal its significance,particularly in clinical medicine.Then,the sustainability of the EEG materials and technology is discussed from two main aspects:integrated systems and EEG electrodes.For integrated systems,sustainability has been focused on the developing trend toward mobile EEG systems and big-data monitoring/analyzing of EEG signals.Sustainability is related to miniaturized,wireless,portable,and wearable systems that are integrated with big-data modeling techniques.For EEG electrodes and materials,sustainability has been comprehensively analyzed from three perspectives:performance of different material/structural categories,sustainablematerials for EEGelectrodes,and sustainable manufacturing technologies.In addition,sustainable applications of EEG have been presented.Finally,the sustainable development of EEG materials and technology in recent decades is summarized,revealing future possible research directions as well as urgent challenges.展开更多
Post-stroke depression(PSD)has negative impacts on the daily life of stroke survivors and delays their neuro-logical recovery.However,traditional post-stroke rehabilitation mainly focused on motor restoration,whereas ...Post-stroke depression(PSD)has negative impacts on the daily life of stroke survivors and delays their neuro-logical recovery.However,traditional post-stroke rehabilitation mainly focused on motor restoration,whereas little attention was given to the affective deficits.Effective management of PSD,including diagnosis,intervention,and follow-ups,is essential for post-stroke rehabilitation.As an objective measurement of the nervous system,electroencephalography(EEG)has been applied to the diagnosis and evaluation of PSD.In this paper,we re-viewed the literature most related to the clinical applications of EEG for PSD and offered a cross-section that is useful for selecting appropriate approaches in practice.This study aimed to gather EEG-based empirical ev-idence for PSD diagnosis,review interventions for managing PSD,and analyze the evaluation approaches.In total,33 diagnostic studies and 19 intervention studies related to PSD and depression were selected from the literature.It was found that the EEG features analyzed by both band-based and nonlinear dynamic approaches were capable of quantifying the abnormal neural responses on the cortical level for PSD diagnosis and interven-tion evaluation/prediction.Meanwhile,EEG-based machine learning has also been applied to the diagnosis and evaluation of depression to automate and speed up the process,and the results have been promising.Although brain-computer interface(BCI)interventions have been widely applied to post-stroke motor rehabilitation and cognitive training,BCI emotional training has not been directly used in PSD yet.This review showed the need for understanding the cortical responses of PSD to improve its diagnosis and precision treatment.It also revealed that future post-stroke rehabilitation plans should include training sessions for motor,affect,and cognitive functions and closely monitor their improvements.展开更多
Exposure to plants has been reported to promote health and reduce stress,and plant color has direct impacts on physical and mental health.We used images of common types of tended plant communities in Shenyang,China,wi...Exposure to plants has been reported to promote health and reduce stress,and plant color has direct impacts on physical and mental health.We used images of common types of tended plant communities in Shenyang,China,with combinations of yellow,green,and red foliage,as experimental stimuli.A total of 27 images were used as visual stimuli.We used electroencephalography to measureαwave activity(8-13 Hz)in 40 subjects while they viewed visual stimuli.These data were combined with subjective questionnaire data to analyze the relaxing effect of images of tended plant communities with different color types and proportions on people.The results revealed that,although there were slight differences between the electroencephalography and psychological findings,women were significantly more relaxed than men after viewing the images.Physiological and psychological responses varied with the types and proportions of colors in the tended plant communities:those of foliage with combinations of two or three colors induced stronger responses than images with a single color.Specifically,(1)for one-color plant communities,green or yellow plant communities induced a stronger relaxation effect than red plant communities;(2)for two-color plant communities,the optimal color proportion was 55%+45%,and the green+yellow and green+red color combinations induced a stronger relaxation effect;(3)for three-color plant communities,the relaxation effect was strongest when the color proportion was 55%green+25%yellow+20%red.These data would provide a plant color matching in future plant landscape design,which may be helpful for creating healthy and relaxing environments.展开更多
Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby c...Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby contributing to the advancement of camouflage evaluation.In this study,images with various camouflage effects were presented to observers to generate electroencephalography(EEG)signals,which were then used to construct a brain functional network.The topological parameters of the network were subsequently extracted and input into a machine learning model for training.The results indicate that most of the classifiers achieved accuracy rates exceeding 70%.Specifically,the Logistic algorithm achieved an accuracy of 81.67%.Therefore,it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability.The proposed method fully considers the aspects of human visual and cognitive processes,overcomes the subjectivity of human interpretation,and achieves stable and reliable accuracy.展开更多
Attentional issues may affect acquiring new information, task performance, and learning. Cortical network activities change during different functional brain states, including the default mode network (DMN) and attent...Attentional issues may affect acquiring new information, task performance, and learning. Cortical network activities change during different functional brain states, including the default mode network (DMN) and attention network. We investigated the neural mechanisms underlying attentional functions and correlations between DMN connectivity and attentional function using the Trail-Making Test (TMT)-A and -B. Electroencephalography recordings were performed by placing 19 scalp electrodes per the 10 - 20 system. The mean power level was calculated for each rest and task condition. Non-parametric Spearman’s rank correlation was used to examine the correlation in power levels between the rest and TMT conditions. The most significant correlations during TMT-A were observed in the high gamma wave, followed by theta and beta waves, indicating that most correlations were in the parietal lobe, followed by the frontal, central, and temporal lobes. The most significant correlations during TMT-B were observed in the beta wave, followed by the high and low gamma waves, indicating that most correlations were in the temporal lobe, followed by the parietal, frontal, and central lobes. Frontoparietal beta and gamma waves in the DMN may represent attentional functions.展开更多
Intracranial electroencephalography(i EEG)provides the best precision in estimating the location and boundary of an epileptogenic zone. Analysis of i EEG in the routine EEG frequency range(0.5–70 Hz) remains the basi...Intracranial electroencephalography(i EEG)provides the best precision in estimating the location and boundary of an epileptogenic zone. Analysis of i EEG in the routine EEG frequency range(0.5–70 Hz) remains the basis in clinical practice. Low-voltage fast activity is the most commonly reported ictal onset pattern in neocortical epilepsy, and low-frequency high-amplitude repetitive spiking is the most commonly reported ictal onset pattern in mesial temporal lobe epilepsy. Recent studies using wideband EEG recording have demonstrated that examining higher(80–1000 Hz) and lower(0.016–0.5 Hz) EEG frequencies can provide additional diagnostic information and help to improve the surgical outcome. In addition,novel computational techniques of i EEG signal analysis have provided new insights into the epileptic network.Here, we review some of these recent advances. Although these sophisticated and advanced techniques of i EEG analysis show promise in localizing the epileptogenic zone,their utility needs to be further validated in larger studies.展开更多
Background:To observe the development of neonatal sleep among healthy infants of different conceptional age(CA)by analyzing the amplitude-integrated electroencephalography(aEEG)of their sleep-wake cycles(SWC).Methods:...Background:To observe the development of neonatal sleep among healthy infants of different conceptional age(CA)by analyzing the amplitude-integrated electroencephalography(aEEG)of their sleep-wake cycles(SWC).Methods:Bedside aEEG monitoring was carried out for healthy newborns from 32 to 46 weeks CA between September 1,2011 and August 30,2012.For each aEEG tracing,mean duration of every complete SWC,number of SWC repetition within 12 hours,mean duration of each narrow and broadband of SWC,mean voltage of the upper edge and lower edge of SWC,mean bandwidth of SWC were counted and calculated.Analysis of the correlations between voltages or bandwidth of SWC and CA was performed to assess the developmental changes of central nervous system of newborns with different CA.Results:The SWC of different CA on aEEG showed clearly identifiable trend after 32 weeks of CA.The occurrence of SWC gradually increases from preterm to post-term infants;term infants had longer SWC duration.The voltage of upper edge of the broadband decreased at 39 weeks,while the lower edge voltage increases and the bandwidth of broadband declined along with the growing CA.The upper edge of the narrowband dropped while the lower edge rised gradually,especially in preterm stage.The width of the narrowband narrowed down while CA increased.Conclusions:The SWC on aEEG of 32-46 weeks infants showed a continuous,dynamic and developmental progress.The appearance of SWC and the narrowing bandwidth of narrowband is the main indicator to identify the CA-dependent SWC from the preterm to the late preterm period.The lower edge of the broadband identifi es the term to post-term period.展开更多
Importance:In children,anesthesia dosages are based on population pharmacokinetics and patient hemodynamics rather than patient-specific brain activity.Brain function is highly susceptible to the effects of anesthetic...Importance:In children,anesthesia dosages are based on population pharmacokinetics and patient hemodynamics rather than patient-specific brain activity.Brain function is highly susceptible to the effects of anesthetics.Objective:The primary objective of this retrospective pilot study was to assess the prevalence of electroencephalography(EEG)burst suppression-a sign of deep anesthesia-in children undergoing general anesthesia.Methods:We analyzed EEG in patients aged 1-36 months who received sevoflurane or propofol as the primary anesthetic.Patient enrollment was stratified into two age groups:1-12 months and 13-36 months.Burst suppression(voltage≤5.0 mV,lasting>0.5 seconds)was characterized by occurrence over anesthesia time.Associations with patient demographics and anesthetics were determined.Results:In total,54 patients(33 males and 21 females)were included in the study[age 11.0(5.0-19.5)months;weight 9.2(6.5-11.0)kg].The total prevalence of burst suppression was 56%(30/54).Thirty-three percent of patients experienced burst suppression during the surgical phase.The greatest proportion of burst suppression occurred during the induction phase.More burst suppression event occurrences(18/30)were observed in the patient under sevoflurane anesthesia(P=0.024).Virtually all patients who received propofol boluses had burst suppression(P=0.033).More burst suppression occurred in patients with hypotension(P<0.001).During the surgical phase,a younger age was associated with more burst suppression(P=0.002).Interpretation:EEG burst suppression was associated with younger age,inhalation anesthetics,propofol bolus,and lower arterial pressure.展开更多
High-frequency oscillations(HFOs)in the electroencephalography(EEG)have been extensively investigated as a potential biomarker of epileptogenic zones.The understanding of the role of HFOs in epilepsy has been advanced...High-frequency oscillations(HFOs)in the electroencephalography(EEG)have been extensively investigated as a potential biomarker of epileptogenic zones.The understanding of the role of HFOs in epilepsy has been advanced considerably over the past decade,and the use of scalp EEG facilitates recordings of HFOs.HFOs were initially applied in large scale in epilepsy surgery and are now being utilized in other applications.In this review,we summarize applications of HFOs in 3 subtopics:(1)HFOs as biomarkers to evaluate epilepsy treatment outcome;(2)HFOs as biomarkers to measure seizure propensity;(3)HFOs as biomarkers to reflect the pathological severity of epilepsy.Nevertheless,knowledge regarding the above clinical applications of HFOs remains limited at present.Further validation through prospective studies is required for its reliable application in the clinical management of individual epileptic patients.展开更多
Removing different types of artifacts from the electroencephalography(EEG)recordings is a critical step in performing EEG signal analysis and diagnosis.Most of the existing algorithms aim for removing single type of a...Removing different types of artifacts from the electroencephalography(EEG)recordings is a critical step in performing EEG signal analysis and diagnosis.Most of the existing algorithms aim for removing single type of artifacts,leading to a complex system if an EEG recording contains different types of artifacts.With the advancement in wearable technologies,it is necessary to develop an energy-efficient algorithm to deal with different types of artifacts for single-channel wearable EEG devices.In this paper,an automatic EEG artifact removal algorithm is proposed that effectively reduces three types of artifacts,i.e.,ocular artifact(OA),transmission-line/harmonic-wave artifact(TA/HA),and muscle artifact(MA),from a single-channel EEG recording.The effectiveness of the proposed algorithm is verified on both simulated noisy EEG signals and real EEG from CHB-MIT dataset.The experimental results show that the proposed algorithm effectively suppresses OA,MA and TA/HA from a single-channel EEG recording as well as physical movement artifact.展开更多
Patients with age-related hearing loss face hearing difficulties in daily life.The causes of age-related hearing loss are complex and include changes in peripheral hearing,central processing,and cognitive-related abil...Patients with age-related hearing loss face hearing difficulties in daily life.The causes of age-related hearing loss are complex and include changes in peripheral hearing,central processing,and cognitive-related abilities.Furthermore,the factors by which aging relates to hearing loss via changes in audito ry processing ability are still unclear.In this cross-sectional study,we evaluated 27 older adults(over 60 years old) with age-related hearing loss,21 older adults(over 60years old) with normal hearing,and 30 younger subjects(18-30 years old) with normal hearing.We used the outcome of the uppe r-threshold test,including the time-compressed thres h old and the speech recognition threshold in noisy conditions,as a behavioral indicator of auditory processing ability.We also used electroencephalogra p hy to identify presbycusis-related abnormalities in the brain while the participants were in a spontaneous resting state.The timecompressed threshold and speech recognition threshold data indicated significant diffe rences among the groups.In patients with age-related hearing loss,information masking(babble noise) had a greater effect than energy masking(speech-shaped noise) on processing difficulties.In terms of resting-state electroencephalography signals,we observed enhanced fro ntal lobe(Brodmann’s area,BA11) activation in the older adults with normal hearing compared with the younger participants with normal hearing,and greater activation in the parietal(BA7) and occipital(BA19) lobes in the individuals with age-related hearing loss compared with the younger adults.Our functional connection analysis suggested that compared with younger people,the older adults with normal hearing exhibited enhanced connections among networks,including the default mode network,sensorimotor network,cingulo-opercular network,occipital network,and frontoparietal network.These results suggest that both normal aging and the development of age-related hearing loss have a negative effect on advanced audito ry processing capabilities and that hearing loss accele rates the decline in speech comprehension,especially in speech competition situations.Older adults with normal hearing may have increased compensatory attentional resource recruitment represented by the to p-down active listening mechanism,while those with age-related hearing loss exhibit decompensation of network connections involving multisensory integration.展开更多
Epilepsy is a common neurological disorder that occurs at all ages.Epilepsy not only brings physical pain to patients,but also brings a huge burden to the lives of patients and their families.At present,epilepsy detec...Epilepsy is a common neurological disorder that occurs at all ages.Epilepsy not only brings physical pain to patients,but also brings a huge burden to the lives of patients and their families.At present,epilepsy detection is still achieved through the observation of electroencephalography(EEG)by medical staff.However,this process takes a long time and consumes energy,which will create a huge workload to medical staff.Therefore,it is particularly important to realize the automatic detection of epilepsy.This paper introduces,in detail,the overall framework of EEG-based automatic epilepsy identification and the typical methods involved in each step.Aiming at the core modules,that is,signal acquisition analog front end(AFE),feature extraction and classifier selection,method summary and theoretical explanation are carried out.Finally,the future research directions in the field of automatic detection of epilepsy are prospected.展开更多
Developments in biomedical science, signal processing technologies have led Electroencephalography (EEG) signals to be widely used in the diagnosis of brain disease and in the field of Brain-Computer Interface (BCI). ...Developments in biomedical science, signal processing technologies have led Electroencephalography (EEG) signals to be widely used in the diagnosis of brain disease and in the field of Brain-Computer Interface (BCI). The collected EEG signals are processed using Machine Learning-Random Forest and Naive Bayes- and Deep Learning-Recurrent Neural Network (RNN), Neural Network (NN) and Long Short Term Memory (LSTM)-Algorithms to obtain the recent mood of a person. The Algorithms mentioned above have been imposed on the data set in order to find out what the person is feeling at a particular moment. The following thesis is conducted to find out one of the following moods (happy, surprised, disgust, fear, anger and sadness) of a person at an instant, with an aim to obtain the result with least amount of time delay as the mood differs. It is pretty obvious that the accuracy of the output varies depending upon the algorithm used, time taken to process the data, so that it is easy for us to compare the reliability and dependency of a particular algorithm to another, prior to its practical implementation. The imbalance data sets that were used had an imbalanced class and thus, over fitting occurred. This problem was handled by generating Artificial Data sets with the use of SMOTE Oversampling Technique.展开更多
基金Supported by Suzhou Key Technologies Program,No.SKY2021063Suzhou Clinical Medical Center for Mood Disorders,No.Szlcyxzx202109+4 种基金Suzhou Clinical Key Disciplines for Geriatric Psychiatry,No.SZXK202116Jiangsu Province Social Development Project,No.BE2020764the Gusu Health Talents Project,No.GSWS2022091the Science and Technology Program of Suzhou,No.SKYD2022039 and No.SKY2023075the Doctoral Scientific Research Foundation of Suzhou Guangji Hospital,No.2023B01.
文摘BACKGROUND A growing number of recent studies have explored underlying activity in the brain by measuring electroencephalography(EEG)in people with depression.However,the consistency of findings on EEG microstates in patients with depression is poor,and few studies have reported the relationship between EEG microstates,cognitive scales,and depression severity scales.AIM To investigate the EEG microstate characteristics of patients with depression and their association with cognitive functions.METHODS A total of 24 patients diagnosed with depression and 32 healthy controls were included in this study using the Structured Clinical Interview for Disease for The Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition.We collected information relating to demographic and clinical characteristics,as well as data from the Repeatable Battery for the Assessment of Neuropsychological Status(RBANS;Chinese version)and EEG.RESULTS Compared with the controls,the duration,occurrence,and contribution of microstate C were significantly higher[depression(DEP):Duration 84.58±24.35,occurrence 3.72±0.56,contribution 30.39±8.59;CON:Duration 72.77±10.23,occurrence 3.41±0.36,contribution 24.46±4.66;Duration F=6.02,P=0.049;Occurrence F=6.19,P=0.049;Contribution F=10.82,P=0.011]while the duration,occurrence,and contribution of microstate D were significantly lower(DEP:Duration 70.00±15.92,occurrence 3.18±0.71,contribution 22.48±8.12;CON:Duration 85.46±10.23,occurrence 3.54±0.41,contribution 28.25±5.85;Duration F=19.18,P<0.001;Occurrence F=5.79,P=0.050;Contribution F=9.41,P=0.013)in patients with depression.A positive correlation was observed between the visuospatial/constructional scores of the RBANS scale and the transition probability of microstate class C to B(r=0.405,P=0.049).CONCLUSION EEG microstate,especially C and D,is a possible biomarker in depression.Patients with depression had a more frequent transition from microstate C to B,which may relate to more negative rumination and visual processing.
文摘Attention constitutes a fundamental psychological feature guiding our mental effort toward specific objects, concurrent with processes such as memory, reasoning, and imagination. Visual attention, crucial for selecting surrounding information, often decreases in older adults and patients with cerebrovascular disorders. Effective methods to enhance attention are scarce. Here, we investigated whether color information influences visual attention and brain activity during task performance, utilizing EEG. We examined 13 healthy young adults (seven women and six men;mean age: 21.2 ± 0.58 years) using 19-electrode electroencephalograms to assess the impact of color information on visual attention. The Clinical Assessment for Attention cancellation test was conducted under the black, red, and blue color conditions. Wilcoxon’s signed-rank test was used to assess differences in task performance (task time and error) between conditions. Spearman’s rank correlation was utilized to examine the correlation in power levels between task performance and color conditions. Significant variations in total task errors were observed among color conditions. The black condition exhibited the highest error frequency (0.7 ± 0.9 times), followed by the red condition (0.5 ± 0.8 times), with the lowest error frequency occurring in the blue (0.2 ± 0.4 times) condition (black vs. red: P = 0.03;black vs. blue: P = 0.00;red vs. blue: P = 0.032). No time difference was observed. The black condition showed negative delta and high-gamma correlations in the central electrodes. The red condition revealed positive alpha and low-gamma correlations in the frontal and occipital areas. Although no correlations were observed in the blue condition, it enhanced attentional performance. Positive alpha and low-gamma waves might be crucial for spotting attentional errors in key areas. Our findings provide insights into the effects of color information on visual attention and potential neural correlates associated with attentional processes. In conclusion, our study implies a connection between color information and attentional task performance, with blue font associated with the most accurate performance.
文摘Purpose: Implant therapy restores masticatory function by restoring lost tooth morphology. It has been shown that mastication contributes not only to food intake and digestion, but also to the improvement of overall health. However, there have been no studies on the effects of implant treatment on electroencephalography (EEG). In this study, we investigated the effects of restoration of masticatory function by implant treatment on EEG and stress. Methods: 13 subjects (6 males, 7 females, age 64.1 ± 5.8 years) who had lost masticatory function due to tooth loss and 11 healthy subjects (6 males, 5 females, age 47.6 ± 2.4 years) as a control group. EEG (θ, α, β waves, α/β ratio) and salivary cortisol were measured before immediate dental implant treatment and every month of treatment for 6 months. EEG (θ, α, β waves, α/β ratio) was measured with a simple electroencephalograph miniature DAQ terminal (Intercross-410, Intercross Co., Ltd., Japan) in a resting closed-eye condition, and salivary cortisol was measured using an ELISA kit. Results: Compared to the control group, the appearance of θ and α waves were significantly decreased and β waves were increased, and α/β ratio was significantly decreased. The cortisol level of the subject group was significantly higher compared with the control group. With the course of implant treatment, the appearance of θ and α waves of the subject group increased, while β waves decreased. However, no significant difference was observed. The α/β ratio of the subject group increased from the first month after implant treatment and increased significantly after 5 and 6 months (0 vs. 5 months: p < 0.05, 0 vs. 6 months: p < 0.01). The cortisol levels in the subject group decreased from the first month after implant treatment and significantly decreased after 3 or 4 months (0 vs. 3 months: p < 0.05, 0 vs. 4 months: p < 0.01). These results suggest that tooth loss causes mental stress, which decreases brain stimulation and affects function. Restoration of masticatory function by implants was suggested to alleviate the effects on brain function and stress.
文摘The purpose of this paper is to analyze sleep stages accurately using fast and simple classifiers based on the frequency domain of electroencephalography(EEG) signal. To compare and evaluate system performance, the rules of Rechtschaffen and Kales(R&K rule) were used. Parameters were extracted from preprocessing process of EEG signal as feature vectors of each sleep stage analysis system through representatives of back propagation algorithm and support vector machine (SVM). As a result, SVM showed better performance as pattern recognition system for classification of sleep stages. It was found that easier analysis of sleep stage was possible using such simple system. Since accurate estimation of sleep state is possible through combination of algorithms, we could see the potential for the classifier to be used for sleep analysis system.
文摘<span style="font-family:Verdana;">There are few EEG studies on finger movement directions because ocular artifacts also convey directional information, which makes it hard to separate the contribution of EEG from that of the ocular artifacts. To overcome this issue, we designed an experiment in which EEG’s temporal dynamics and spatial information are evaluated together to improve the performance of brain-computer interface (BCI) for classifying finger movement directions. Six volunteers participated in the study. We examined their EEG using decoding analyses. Independent components (ICs) that represented brain-source signals successfully classified the directions of the finger movements with higher rates than chance level. The weight analyses of the classifiers revealed that maximal performance of the classification was recorded at the latencies prior to the onset of finger movements. The weight analyses also revealed the relevant cortical areas including the right lingual, left posterior cingulate, left inferior temporal gyrus, and right precuneus, which indicated the involvement of the visuospatial processing. We concluded that combining spatial distribution and temporal dynamics of the scalp EEG may improve BCI performance.</span>
文摘Background: Electroencephalography (EEG) is established for evaluating several acute and chronic medical conditions of neurological basis. In much of Nigeria and Africa, it is largely unavailable and underutilized due to scarcity of neurologists and high costs of the equipment. It offers a relatively simple and efficient way to help manage many encephalopathies if well utilized in trained hands. Aim: This study aimed to determine how physicians practicing in Enugu perceive and utilize electroencephalography routinely. Method: Physicians attending a statewide meeting in Enugu in August 2018 were consecutively recruited and a pretested questionnaire was administered after obtaining prior consent. Sociodemographic data as well as their knowledge, attitude and practice of electroencephalography were documented and analyzed. Results: There were 486 respondents (males 335: females 151) and 345 (71%) were specialists in various disciplines while 141 (29%) were general practitioners. Only 7 doctors (1.4%) claimed ignorance of electroencephalography and 6 (1.2%) stated it was not useful. Majority, 333 doctors (69.1%) believed it had no impact on routine patient management. This perception was highest for Dental Surgery (100%) and lowest for Internal Medicine (23%) specialists. Most doctors (425, 87.4%) agreed that neurologists should analyze recordings. Most physicians had no access to electroencephalography (61.7%) and had no interest in acquiring the machine (50.8%). Conclusion: Electroencephalography is an underappreciated investigative modality amongst physicians in Enugu, despite a high burden of neurological diseases in the population. More education, training and awareness of its utility are needed for medical students and doctors to reverse the trend.
文摘Objective To explore quantitative electroencephalography in unconscious patients after severe traumatic brain injury (TBI) to predict awakening. Methods All cases were divided into two groups(the awake group 19 cases and the unfavourable prognosis group 22 cases).Two weeks after admission the original EEGs were preformed in 41 patients suffering from severe TBI with duration of disturbance of
基金National Natural Science Foundation of China,Grant/Award Number:62271458Sichuan Province Central Government Guides Local Science and Technology Development Project,Grant/Award Number:2023ZYD0015。
文摘Electroencephalogram(EEG)is one of the most important bioelectrical signals related to brain activity and plays a crucial role in clinical medicine.Driven by continuously expanding applications,the development of EEG materials and technology has attracted considerable attention.However,systematic analysis of the sustainable development of EEG materials and technology is still lacking.This review discusses the sustainable development of EEG materials and technology.First,the developing course of EEG is introduced to reveal its significance,particularly in clinical medicine.Then,the sustainability of the EEG materials and technology is discussed from two main aspects:integrated systems and EEG electrodes.For integrated systems,sustainability has been focused on the developing trend toward mobile EEG systems and big-data monitoring/analyzing of EEG signals.Sustainability is related to miniaturized,wireless,portable,and wearable systems that are integrated with big-data modeling techniques.For EEG electrodes and materials,sustainability has been comprehensively analyzed from three perspectives:performance of different material/structural categories,sustainablematerials for EEGelectrodes,and sustainable manufacturing technologies.In addition,sustainable applications of EEG have been presented.Finally,the sustainable development of EEG materials and technology in recent decades is summarized,revealing future possible research directions as well as urgent challenges.
基金funded by the National Natural Science Foundation of China (NSFC 81771959)the University Grants Committee Research Grants Council,Hong Kong (GRF 15207120)the Science and Tech-nology Innovation Committee of Shenzhen,China (2021Szvup142)。
文摘Post-stroke depression(PSD)has negative impacts on the daily life of stroke survivors and delays their neuro-logical recovery.However,traditional post-stroke rehabilitation mainly focused on motor restoration,whereas little attention was given to the affective deficits.Effective management of PSD,including diagnosis,intervention,and follow-ups,is essential for post-stroke rehabilitation.As an objective measurement of the nervous system,electroencephalography(EEG)has been applied to the diagnosis and evaluation of PSD.In this paper,we re-viewed the literature most related to the clinical applications of EEG for PSD and offered a cross-section that is useful for selecting appropriate approaches in practice.This study aimed to gather EEG-based empirical ev-idence for PSD diagnosis,review interventions for managing PSD,and analyze the evaluation approaches.In total,33 diagnostic studies and 19 intervention studies related to PSD and depression were selected from the literature.It was found that the EEG features analyzed by both band-based and nonlinear dynamic approaches were capable of quantifying the abnormal neural responses on the cortical level for PSD diagnosis and interven-tion evaluation/prediction.Meanwhile,EEG-based machine learning has also been applied to the diagnosis and evaluation of depression to automate and speed up the process,and the results have been promising.Although brain-computer interface(BCI)interventions have been widely applied to post-stroke motor rehabilitation and cognitive training,BCI emotional training has not been directly used in PSD yet.This review showed the need for understanding the cortical responses of PSD to improve its diagnosis and precision treatment.It also revealed that future post-stroke rehabilitation plans should include training sessions for motor,affect,and cognitive functions and closely monitor their improvements.
基金This work was supported by Postgraduate Education Reform Project of Liaoning Province(LNYJG2022253)National Natural Science Foundation of China(31470031).
文摘Exposure to plants has been reported to promote health and reduce stress,and plant color has direct impacts on physical and mental health.We used images of common types of tended plant communities in Shenyang,China,with combinations of yellow,green,and red foliage,as experimental stimuli.A total of 27 images were used as visual stimuli.We used electroencephalography to measureαwave activity(8-13 Hz)in 40 subjects while they viewed visual stimuli.These data were combined with subjective questionnaire data to analyze the relaxing effect of images of tended plant communities with different color types and proportions on people.The results revealed that,although there were slight differences between the electroencephalography and psychological findings,women were significantly more relaxed than men after viewing the images.Physiological and psychological responses varied with the types and proportions of colors in the tended plant communities:those of foliage with combinations of two or three colors induced stronger responses than images with a single color.Specifically,(1)for one-color plant communities,green or yellow plant communities induced a stronger relaxation effect than red plant communities;(2)for two-color plant communities,the optimal color proportion was 55%+45%,and the green+yellow and green+red color combinations induced a stronger relaxation effect;(3)for three-color plant communities,the relaxation effect was strongest when the color proportion was 55%green+25%yellow+20%red.These data would provide a plant color matching in future plant landscape design,which may be helpful for creating healthy and relaxing environments.
基金sponsored by the National Defense Science and Technology Key Laboratory Fund(Grant No.61422062205)the Equipment Pre-Research Fund(Grant No.JCKYS2022LD9)。
文摘Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby contributing to the advancement of camouflage evaluation.In this study,images with various camouflage effects were presented to observers to generate electroencephalography(EEG)signals,which were then used to construct a brain functional network.The topological parameters of the network were subsequently extracted and input into a machine learning model for training.The results indicate that most of the classifiers achieved accuracy rates exceeding 70%.Specifically,the Logistic algorithm achieved an accuracy of 81.67%.Therefore,it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability.The proposed method fully considers the aspects of human visual and cognitive processes,overcomes the subjectivity of human interpretation,and achieves stable and reliable accuracy.
文摘Attentional issues may affect acquiring new information, task performance, and learning. Cortical network activities change during different functional brain states, including the default mode network (DMN) and attention network. We investigated the neural mechanisms underlying attentional functions and correlations between DMN connectivity and attentional function using the Trail-Making Test (TMT)-A and -B. Electroencephalography recordings were performed by placing 19 scalp electrodes per the 10 - 20 system. The mean power level was calculated for each rest and task condition. Non-parametric Spearman’s rank correlation was used to examine the correlation in power levels between the rest and TMT conditions. The most significant correlations during TMT-A were observed in the high gamma wave, followed by theta and beta waves, indicating that most correlations were in the parietal lobe, followed by the frontal, central, and temporal lobes. The most significant correlations during TMT-B were observed in the beta wave, followed by the high and low gamma waves, indicating that most correlations were in the temporal lobe, followed by the parietal, frontal, and central lobes. Frontoparietal beta and gamma waves in the DMN may represent attentional functions.
基金supported by the National Natural Science Foundation of China (81271435 and 91332202)
文摘Intracranial electroencephalography(i EEG)provides the best precision in estimating the location and boundary of an epileptogenic zone. Analysis of i EEG in the routine EEG frequency range(0.5–70 Hz) remains the basis in clinical practice. Low-voltage fast activity is the most commonly reported ictal onset pattern in neocortical epilepsy, and low-frequency high-amplitude repetitive spiking is the most commonly reported ictal onset pattern in mesial temporal lobe epilepsy. Recent studies using wideband EEG recording have demonstrated that examining higher(80–1000 Hz) and lower(0.016–0.5 Hz) EEG frequencies can provide additional diagnostic information and help to improve the surgical outcome. In addition,novel computational techniques of i EEG signal analysis have provided new insights into the epileptic network.Here, we review some of these recent advances. Although these sophisticated and advanced techniques of i EEG analysis show promise in localizing the epileptogenic zone,their utility needs to be further validated in larger studies.
基金This work was supported by the Guangzhou Science Technology and Innovation Commission 1563000668(Lian Zhang).
文摘Background:To observe the development of neonatal sleep among healthy infants of different conceptional age(CA)by analyzing the amplitude-integrated electroencephalography(aEEG)of their sleep-wake cycles(SWC).Methods:Bedside aEEG monitoring was carried out for healthy newborns from 32 to 46 weeks CA between September 1,2011 and August 30,2012.For each aEEG tracing,mean duration of every complete SWC,number of SWC repetition within 12 hours,mean duration of each narrow and broadband of SWC,mean voltage of the upper edge and lower edge of SWC,mean bandwidth of SWC were counted and calculated.Analysis of the correlations between voltages or bandwidth of SWC and CA was performed to assess the developmental changes of central nervous system of newborns with different CA.Results:The SWC of different CA on aEEG showed clearly identifiable trend after 32 weeks of CA.The occurrence of SWC gradually increases from preterm to post-term infants;term infants had longer SWC duration.The voltage of upper edge of the broadband decreased at 39 weeks,while the lower edge voltage increases and the bandwidth of broadband declined along with the growing CA.The upper edge of the narrowband dropped while the lower edge rised gradually,especially in preterm stage.The width of the narrowband narrowed down while CA increased.Conclusions:The SWC on aEEG of 32-46 weeks infants showed a continuous,dynamic and developmental progress.The appearance of SWC and the narrowing bandwidth of narrowband is the main indicator to identify the CA-dependent SWC from the preterm to the late preterm period.The lower edge of the broadband identifi es the term to post-term period.
文摘Importance:In children,anesthesia dosages are based on population pharmacokinetics and patient hemodynamics rather than patient-specific brain activity.Brain function is highly susceptible to the effects of anesthetics.Objective:The primary objective of this retrospective pilot study was to assess the prevalence of electroencephalography(EEG)burst suppression-a sign of deep anesthesia-in children undergoing general anesthesia.Methods:We analyzed EEG in patients aged 1-36 months who received sevoflurane or propofol as the primary anesthetic.Patient enrollment was stratified into two age groups:1-12 months and 13-36 months.Burst suppression(voltage≤5.0 mV,lasting>0.5 seconds)was characterized by occurrence over anesthesia time.Associations with patient demographics and anesthetics were determined.Results:In total,54 patients(33 males and 21 females)were included in the study[age 11.0(5.0-19.5)months;weight 9.2(6.5-11.0)kg].The total prevalence of burst suppression was 56%(30/54).Thirty-three percent of patients experienced burst suppression during the surgical phase.The greatest proportion of burst suppression occurred during the induction phase.More burst suppression event occurrences(18/30)were observed in the patient under sevoflurane anesthesia(P=0.024).Virtually all patients who received propofol boluses had burst suppression(P=0.033).More burst suppression occurred in patients with hypotension(P<0.001).During the surgical phase,a younger age was associated with more burst suppression(P=0.002).Interpretation:EEG burst suppression was associated with younger age,inhalation anesthetics,propofol bolus,and lower arterial pressure.
基金supported by grants from the National Key R&D Program of China(2017YFC1307500 to QW)the Capital Health Research and Development of Special Program(2016-1-2011 and 2020-1-2013 to QW)+2 种基金the Beijing-Tianjin-Hebei Cooperative Basic Research Program(H2018206435 to QW)the Beijing Natural Science Foundation(Z200024 to YGW and QW)the National Natural Science Foundation of China(81801280 to GR,81601126 to JR).
文摘High-frequency oscillations(HFOs)in the electroencephalography(EEG)have been extensively investigated as a potential biomarker of epileptogenic zones.The understanding of the role of HFOs in epilepsy has been advanced considerably over the past decade,and the use of scalp EEG facilitates recordings of HFOs.HFOs were initially applied in large scale in epilepsy surgery and are now being utilized in other applications.In this review,we summarize applications of HFOs in 3 subtopics:(1)HFOs as biomarkers to evaluate epilepsy treatment outcome;(2)HFOs as biomarkers to measure seizure propensity;(3)HFOs as biomarkers to reflect the pathological severity of epilepsy.Nevertheless,knowledge regarding the above clinical applications of HFOs remains limited at present.Further validation through prospective studies is required for its reliable application in the clinical management of individual epileptic patients.
基金the National Natural Science Foundation of China(No.61874171)the Alibaba Innovative Research Program of Alibaba Group。
文摘Removing different types of artifacts from the electroencephalography(EEG)recordings is a critical step in performing EEG signal analysis and diagnosis.Most of the existing algorithms aim for removing single type of artifacts,leading to a complex system if an EEG recording contains different types of artifacts.With the advancement in wearable technologies,it is necessary to develop an energy-efficient algorithm to deal with different types of artifacts for single-channel wearable EEG devices.In this paper,an automatic EEG artifact removal algorithm is proposed that effectively reduces three types of artifacts,i.e.,ocular artifact(OA),transmission-line/harmonic-wave artifact(TA/HA),and muscle artifact(MA),from a single-channel EEG recording.The effectiveness of the proposed algorithm is verified on both simulated noisy EEG signals and real EEG from CHB-MIT dataset.The experimental results show that the proposed algorithm effectively suppresses OA,MA and TA/HA from a single-channel EEG recording as well as physical movement artifact.
基金supported by the National Natural Science Foundation of China,Nos.82171138 (to YQZ),82071 062 (to YXC)the Natural Science Foundation of Guangdong Province,No.2021A1515012038 (to YXC)+1 种基金the Fundamental Research Funds for the Central Universities,No.20ykpy91 (to YXC)the Sun Yat-Sen Clinical Research Cultivating Program,No.SYS-Q-201903 (to YXC)。
文摘Patients with age-related hearing loss face hearing difficulties in daily life.The causes of age-related hearing loss are complex and include changes in peripheral hearing,central processing,and cognitive-related abilities.Furthermore,the factors by which aging relates to hearing loss via changes in audito ry processing ability are still unclear.In this cross-sectional study,we evaluated 27 older adults(over 60 years old) with age-related hearing loss,21 older adults(over 60years old) with normal hearing,and 30 younger subjects(18-30 years old) with normal hearing.We used the outcome of the uppe r-threshold test,including the time-compressed thres h old and the speech recognition threshold in noisy conditions,as a behavioral indicator of auditory processing ability.We also used electroencephalogra p hy to identify presbycusis-related abnormalities in the brain while the participants were in a spontaneous resting state.The timecompressed threshold and speech recognition threshold data indicated significant diffe rences among the groups.In patients with age-related hearing loss,information masking(babble noise) had a greater effect than energy masking(speech-shaped noise) on processing difficulties.In terms of resting-state electroencephalography signals,we observed enhanced fro ntal lobe(Brodmann’s area,BA11) activation in the older adults with normal hearing compared with the younger participants with normal hearing,and greater activation in the parietal(BA7) and occipital(BA19) lobes in the individuals with age-related hearing loss compared with the younger adults.Our functional connection analysis suggested that compared with younger people,the older adults with normal hearing exhibited enhanced connections among networks,including the default mode network,sensorimotor network,cingulo-opercular network,occipital network,and frontoparietal network.These results suggest that both normal aging and the development of age-related hearing loss have a negative effect on advanced audito ry processing capabilities and that hearing loss accele rates the decline in speech comprehension,especially in speech competition situations.Older adults with normal hearing may have increased compensatory attentional resource recruitment represented by the to p-down active listening mechanism,while those with age-related hearing loss exhibit decompensation of network connections involving multisensory integration.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences,Grant No.XDA0330000 and Grant No.XDB44000000。
文摘Epilepsy is a common neurological disorder that occurs at all ages.Epilepsy not only brings physical pain to patients,but also brings a huge burden to the lives of patients and their families.At present,epilepsy detection is still achieved through the observation of electroencephalography(EEG)by medical staff.However,this process takes a long time and consumes energy,which will create a huge workload to medical staff.Therefore,it is particularly important to realize the automatic detection of epilepsy.This paper introduces,in detail,the overall framework of EEG-based automatic epilepsy identification and the typical methods involved in each step.Aiming at the core modules,that is,signal acquisition analog front end(AFE),feature extraction and classifier selection,method summary and theoretical explanation are carried out.Finally,the future research directions in the field of automatic detection of epilepsy are prospected.
文摘Developments in biomedical science, signal processing technologies have led Electroencephalography (EEG) signals to be widely used in the diagnosis of brain disease and in the field of Brain-Computer Interface (BCI). The collected EEG signals are processed using Machine Learning-Random Forest and Naive Bayes- and Deep Learning-Recurrent Neural Network (RNN), Neural Network (NN) and Long Short Term Memory (LSTM)-Algorithms to obtain the recent mood of a person. The Algorithms mentioned above have been imposed on the data set in order to find out what the person is feeling at a particular moment. The following thesis is conducted to find out one of the following moods (happy, surprised, disgust, fear, anger and sadness) of a person at an instant, with an aim to obtain the result with least amount of time delay as the mood differs. It is pretty obvious that the accuracy of the output varies depending upon the algorithm used, time taken to process the data, so that it is easy for us to compare the reliability and dependency of a particular algorithm to another, prior to its practical implementation. The imbalance data sets that were used had an imbalanced class and thus, over fitting occurred. This problem was handled by generating Artificial Data sets with the use of SMOTE Oversampling Technique.
