Electroencephalographic Correlates of Psychometry

Berger＇s ＂window on the mind＂ [1] has, with modem computing power, become a possibility. A portable electroencephalograph was used to demonstrate a correlation of personality with brain waves. In principle, this could make it as useful a clinical tool for psychiatrists as the stethoscope is for physicians. Besides clinical use, the method could be a cheap, efficient way of investigating the effects ofpsychopharmaceutical drugs.

A Method of SSVEP Signal Identification Based on Improved eCAA

Brain-computer interfaces(BCI)based on steady-state visual evoked potentials(SSVEP)have attracted great interest because of their higher signal-to-noise ratio,less training,and faster information transfer.However,the existing signal recognition methods for SSVEP do not fully pay attention to the important role of signal phase characteristics in the recognition process.Therefore,an improved method based on extended Canonical Correlation Analysis(eCCA)is proposed.The phase parameters are added from the stimulus paradigm encoded by joint frequency phase modulation to the reference signal constructed from the training data of the subjects to achieve phase constraints on eCCA,thereby improving the recognition performance of the eCCA method for SSVEP signals,and transmit the collected signals to the robotic arm system to achieve control of the robotic arm.In order to verify the effectiveness and advantages of the proposed method,this paper evaluated the method using SSVEP signals from 35 subjects.The research shows that the proposed algorithm improves the average recognition rate of SSVEP signals to 82.76%,and the information transmission rate to 116.18 bits/min,which is superior to TRCA and traditional eCAA-based methods in terms of information transmission speed and accuracy,and has better stability.

一种EEG信号盲分离和分类的神经网络方法

提出一种采用多神经网络处理脑电 (EEG)信号的方法。首先 ,对混有噪声的脑电信号给出一种盲分离的自适应神经算法。通过寻求采样时间序列线性组合的kurtosis系数的局部极值 ,得出该算法的模型和步骤。在盲分离的基础上 ,对分离出的估计信号进一步利用Kohonen网络进行分类。将该算法用于 30 0个EEG样本处理 ,并给出处理结果。

Cross-task emotion recognition using EEG measures: first step towards practical application

Electroencephalographic(EEG)-based emotion recognition has received increasing attention in the field of human-computer interaction(HCI)recently,there however remains a number of challenges in building a generalized emotion recognition model,one of which includes the difficulty of an EEG-based emotion classifier trained on a specific task to handle other tasks.Lit-tle attention has been paid to this issue.The current study is to determine the feasibility of coping with this challenge using feature selection.12 healthy volunteers were emotionally elicited when conducting picture induced and videoinduced tasks.Firstly,support vector machine(SVM)classifier was examined under within-task conditions(trained and tested on the same task)and cross-task conditions(trained on one task and tested on another task)for pictureinduced and videoinduced tasks.The within-task classification performed fairly well(classification accuracy:51.6%for picture task and 94.4%for video task).Cross-task classification,however,deteriorated to low levels(around 44%).Trained and tested with the most robust feature subset selected by SVM-recursive feature elimination(RFE),the performance of cross-task classifier was significantly improved to above 68%.These results suggest that cross-task emotion recognition is feasible with proper methods and bring EEG-based emotion recognition models closer to being able to discriminate emotion states for any tasks.

Absence of Quantitative Improvement in Neuropsychological Profiles in Patients Who Exhibit Moderate Brain Impairment: Comparisons of Cross-Sectional and Longitudinal Data (1 through 4 Years Post-Injury)

A total of 127 adult patients who had sustained an impact of significant mechanical energy to their skulls during motor vehicle incidents were given thorough neuropsychological, cognitive and personality assessments between 0.5 years and 4 years after the event. Cross-sectional analysis indicated no statistically significant objective changes in patients as a function of yearly intervals. However there was strong evidence of significant deterioration of neuropsychological proficiency and efficiency between 0.3 to 1.0 years after the injury. A subset (n = 20) of patients who displayed moderately severe neuropsychological impairment when assessed about 1 year after the injury showed no statistically significant changes when reassessed about 1.5 years later (2.5 years after the brain trauma). These results challenge the traditional concept of “recovery” following a traumatic brain injury and indicate that insidious processes that adversely affect neurocognitive capacity may emerge 0.5 years after the trauma. Post-hoc analysis indicated that the occurrence of unconsciousness or its duration at the time of the injury minimally affected the magnitude of subsequent indices of neuropsychological impairment but influenced the incidence of electroencephalographic theta activity during the years following the injury.

Seizure detection using earth movers' distance and SVM in intracranial EEG

Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram （iEEG）. The algorithm conducts wavelet decomposition of iEEGs with five scales, and transforms the sum of the three frequency bands into histogram for computing the distance. The proposed method combines a novel feature called EMD-L1, which is an efficient algorithm of earth movers＇ distance （EMD）, with support vector machine （SVM） for binary classification between seizures and non-sei- zures. The EMD-LI used in this method is characterized by low time complexity and high processing speed by exploiting the L~ metric structure. The smoothing and collar technique are applied on the raw outputs of SVM classifier to obtain more ac- curate results. Several evaluation criteria are recommended to compare our algorithm with other conventional methods using the same dataset from the Freiburg EEG database. Experiment results show that the proposed method achieves a high sensi- tivity, specificity and low false detection rate, which are 95.73 %, 98.45 % and 0.33/h, respectively. This algorithm is char- acterized by its robustness and high accuracy with the possibility of performing real-time analysis of EEG data, and may serve as a seizure detection tool for monitoring long-term EEG.

Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition

Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in practice.We attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration samples.To solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal data.Our method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common space.Through adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of knowledge.Extensive experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed method.Our work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.

Depth of anaesthesia monitors and the latest algorithms

This paper reviews the existing depth of anaesthesia(DoA) monitors and their algorithms and also proposes to improve their performance from four aspects.\n ideal DoA monitor should be able to suggest a personalised drug dosages,to predict and provide early warnings when dosages are inappropriate,to he porlalile and highty cost—effective.The limitations of the existing DoA monitors commonly include unsatisfied data filtering techniques.time delay for the monitoring responses,and inflexible and low noise immunity problems.The latest research results show that their performance can be improved using up—to—date computing technology and neurophysiology.The findings in Chinese market review show that neither the imported nor the Chinese domestic DoA monitors are widely utilised at hospitals.but the demand for DoA monitors is very high.Clearly there is a high demand which encourages the development of a better DoA monitor and its mass production in China.

The modulation of brain functional connectivity with manual acupuncture in healthy subjects:An electroencephalograph case study

Manual acupuncture is widely used for pain relief and stress control.Previous studies on acupuncture have shown its modulatory effects on the functional connectivity associated with one or a few preselected brain regions.To investigate how manual acupuncture modulates the organization of functional networks at a whole-brain level,we acupuncture at ST36 of a right leg to obtain electroencephalograph（EEG） signals.By coherence estimation,we determine the synchronizations between all pairwise combinations of EEG channels in three acupuncture states.The resulting synchronization matrices are converted into functional networks by applying a threshold,and the clustering coefficients and path lengths are computed as a function of threshold.The results show that acupuncture can increase functional connections and synchronizations between different brain areas.For a wide range of thresholds,the clustering coefficient during acupuncture and postacupuncture period is higher than that during the pre-acupuncture control period,whereas the characteristic path length is shorter.We provide further support for the presence of ＂small-world＂ network characteristics in functional networks by using acupuncture.These preliminary results highlight the beneficial modulations of functional connectivity by manual acupuncture,which could contribute to the understanding of the effects of acupuncture on the entire brain,as well as the neurophysiological mechanisms underlying acupuncture.Moreover,the proposed method may be a useful approach to the further investigation of the complexity of patterns of interrelations between EEG channels.

Modulation of electroencephalograph activity by manual acupuncture stimulation in healthy subjects:An autoregressive spectral analysis

To investigate whether and how manual acupuncture（MA） modulates brain activities,we design an experiment where acupuncture at acupoint ST36 of the right leg is used to obtain electroencephalograph（EEG） signals in healthy subjects.We adopt the autoregressive（AR） Burg method to estimate the power spectrum of EEG signals and analyze the relative powers in delta（0 Hz-4 Hz）,theta（4 Hz-8 Hz）,alpha（8 Hz-13 Hz）,and beta（13 Hz-30 Hz） bands.Our results show that MA at ST36 can significantly increase the EEG slow wave relative power（delta band） and reduce the fast wave relative powers（alpha and beta bands）,while there are no statistical differences in theta band relative power between different acupuncture states.In order to quantify the ratio of slow to fast wave EEG activity,we compute the power ratio index.It is found that the MA can significantly increase the power ratio index,especially in frontal and central lobes.All the results highlight the modulation of brain activities with MA and may provide potential help for the clinical use of acupuncture.The proposed quantitative method of acupuncture signals may be further used to make MA more standardized.

An effective initial polytherapy for children with West syndrome

Adrenocorticotropic hormone is recommended worldwide as an initial therapy for infantile spasms. However, infantile spasms in about 50% of children cannot be fully controlled by adrenocorticotropic hormone monotherapy, seizures recur in 33% of patients who initially respond to adrenocorticotropic hormone monotherapy, and side effects are relatively common during adrenocorticotropic hormone treatment. Topiramate, vitamin B6, and immunoglobulin are effective in some children with infantile spasms. In the present study, we hypothesized that combined therapy with adrenocorticotropic hormone, topiramate, vitamin B6, and immunoglobulin would effectively treat infantile spasms and have mild adverse effects. Thus, 51 children newly diagnosed with West syndrome including infantile spasms were enrolled and underwent polytherapy with the four drugs. Electroencephalographic hypsarrhythmia was significantly improved in a majority of patients, and these patients were seizure-free, had mild side effects, and low recurrence rates. The overall rates of effective treatment and loss of seizures were significantly higher in cryptogenic children compared with symptomatic children. The mean time to loss of seizures in cryptogenic children was significantly shorter than in symptomatic patients. These findings indicate that initial polytherapy with adrenocorticotropic hormone, topiramate, vitamin Be, and immunoglobulin effectively improves the prognosis of infantile spasms, and its effects were superior in cryptogenic children to symptomatic children.

40 Hz Auditory Steady State Response to Linguistic Features of Stimuli during Auditory Hallucinations

The auditory steady state response （ASSR） may reflect activity from different regions of the brain, depending on the modulation frequency used. In general, responses induced by low rates （_〈40 Hz） emanate mostly from central structures of the brain, and responses from high rates （〉80 Hz） emanate mostly from the peripheral auditory nerve or brainstem structures. Besides, it was reported that the gamma band ASSR （30-90 Hz） played an important role in working memory, speech understanding and recognition. This paper investigated the 40 Hz ASSR evoked by modulated speech and reversed speech. The speech was Chinese phrase voice, and the noise-like reversed speech was obtained by temporally reversing the speech. Both auditory stimuli were modulated with a frequency of 40 Hz. Ten healthy subjects and 5 patients with hallucination symptom participated in the experiment. Results showed re- duction in left auditory cortex response when healthy subjects listened to the reversed speech compared with the speech. In contrast, when the patients who experienced auditory hallucinations listened to the reversed speech, the auditory cortex of left hemispheric responded more actively. The ASSR results were consistent with the behavior results of patients. Therefore, the gamma band ASSR is expected to be helpful for rapid and objective diagnosis of hallucination in clinic.

Characteristics analysis of acupuncture electroencephalograph based on mutual information Lempel-Ziv complexity

As a convenient approach to the characterization of cerebral cortex electrical information, electroencephalograph （EEG） has potential clinical application in monitoring the acupuncture effects. In this paper, a method composed of the mutual information method and Lempel-Ziv complexity method （MILZC） is proposed to investigate the effects of acupuncture on the complexity of information exchanges between different brain regions based on EEGs. In the experiments, eight subjects are manually acupunctured at ＇Zusanli＇ acupuncture point （ST-36） with different frequencies （i.e., 50, 100, 150, and 200 times/min） and the EEGs are recorded simultaneously. First, MILZC values are compared in general. Then average brain connections are used to quantify the effectiveness of acupuncture under the above four frequencies. Finally, significance index P values are used to study the spatiality of the acupuncture effect on the brain. Three main findings are obtained： （i） MILZC values increase during the acupuncture; （ii） manual acupunctures （MAs） with 100 times/rain and 150 times/min are more effective than with 50 times/min and 200 times/rain; （iii） contralateral hemisphere activation is more prominent than ipsilateral hemisphere＇s. All these findings suggest that acupuncture contributes to the increase of brain information exchange complexity and the MILZC method can successfully describe these changes.

Macrostate and Microstate of EEG Spatio-Temporal Nonlinear Dynamics in Zen Meditation

Macrostate and microstate characteristics of interregional nonlinear interdependence of brain dynamics are investigated for Zen-meditation and normal resting EEG. Evaluation of nonlinear interdependence based on nonlinear dynamic theory and phase space reconstruction is employed in the 30-channel electroencephalographic (EEG) signals to characterize the functioning interactions among different local neuronal networks. This paper presents a new scheme for exploring the microstate and macrostate of interregional brain neural network interactivity. Nonlinear interdependence quantified by similarity index is applied to the phase trajectory reconstructed from multi-channel EEG. The microstate similarity-index matrix (miSIM) is evaluated every 5 millisecond. The miSIMs are classified by K-means clustering. The cluster center corresponds to the macrostate SIM (maSIM) evaluated by conventional scheme. Zen-meditation EEG exhibits rather stationary and stronger interconnectivity among frontal midline regional neural oscillators, whereas resting EEG appears to drift away more often from the midline and extend to the inferior brain regions.

A Study on Melody Tempo with EEG

In this paper, tempo perception is investi- gated by recording spontaneous electroencephalograph （EEG）. Ten normal male non-musician college students are selected according to questionnaire results after listening absorbedly to four different tempos of an excerpt from a Mozart sonata. EEGs data are recorded when the subjects are listening to the music. The EEG spectral power （SP） is analyzed for alpha band. The varying trend of power spectrum during exposure to music excerpts of different tempos is studied and shows the consistence with the previous tempo-specific hypothesis： a tempo-transformed performance will sound less natural than an original performance does. The results presented in this paper suggest that tempo is an important factor that could influence the alpha rhythm.

Neurotherapy for chronic headache following traumatic brain injury

Background: Chronic headache following traumatic brain injury(TBI) sustained in military service, while common, is highly challenging to treat with existing pharmacologic and non-pharmacologic interventions, and it may be complicated by co-morbid posttraumatic stress. Recently, a novel form of brainwave-based intervention known as the Flexyx Neurotherapy System(FNS), which involves minute pulses of electromagnetic energy stimulation of brainwave activity, has been suggested as a means to address symptoms of TBI. This study reports on a clinical series of patients with chronic headache following service-connected TBI treated with FNS.Methods: Nine veterans of the wars in Afghanistan and Iraq with moderate to severe chronic headaches following service-connected TBI complicated by posttraumatic stress symptoms were treated in 20 individual FNS sessions at the Brain Wellness and Biofeedback Center of Washington(in Bethesda, Maryland, USA). They periodically completed measures including the Brief Pain Inventory-Headache(BPI-HA), previous week worst and average pain ratings, the Posttraumatic Stress Disorder Checklist-Military version(PCL-M), and an individual treatment session numerical rating scale(NRS) for the degree of cognitive dysfunction. Data analyses included beginning-to-end of treatment t-test comparisons for the BPI-HA, PCL-M, and cognitive dysfunction NRS. Results: All beginning-to-end of treatment t-test comparisons for the BPI-HA, PCL-M, and cognitive dysfunction NRS indicated statistically significant decreases. All but one participant experienced a reduction in headaches along with reductions in posttraumatic stress and perceived cognitive dysfunction, with a subset experiencing the virtual elimination of headaches. One participant obtained modest headache relief but no improvements in posttraumatic stress or cognitive dysfunction. Conclusions: FNS may be a potentially efficacious treatment for chronic posttraumatic headache sustained in military service. Further research is needed to investigate the efficacy of FNS within a randomized, controlled clinical trial to identify the characteristics of those most likely to respond and to explore underlying mechanisms that may contribute to improvements.

Grading Method for Hypoxic-Ischemic Encephalopathy Based on Neonatal EEG

The grading of hypoxic-ischemic encephalopathy(HIE)contributes to the clinical decision making for neonates with HIE.In this paper,an automated grading method based on electroencephalogram(EEG)data is proposed to describe the severity of HIE infants,namely mild asphyxia,moderate asphyxia and severe asphyxia.The automated grading method is based on a multi-class support vector machine(SVM)classifier,and the input features of SVM classifier include long-term features which are extracted by decomposing the EEG data into different 64 s epoch data and short-term features which are extracted by segmenting the 64 s epoch data into 8 s epoch data with 4 s overlap.Of note,the correlation coefficient and asymmetry extracted in this paper have obvious discriminating capability in HIE infants classification.The experimental results show that the proposed method can achieve the classification accuracy of 78.3%,75.8%and 87.0%of the mild asphyxia group,moderate asphyxia group and severe asphyxia group,respectively.Moreover,the overall accuracy and kappa used to evaluate the performance of the proposed method can reach 79.5%and 0.69,respectively.

Detection the Character Wave in Epileptic EEG by Wavelet

Human epilepsy is an intrinsic brain pathology, which can be characterized by repetitive high-amplitude electroencephalograph (EEG) activity. The wavelet transform provides an important tool in signal analysis and feature extraction. In this paper, the modulus maximum pair of a wavelet transform is used to detect the singularity value of the sharps and the spikes embedded in the background activities of the epilepsy EEG. The efficacy of the proposed method has been tested with clinical EEG.

Online prediction of EEG based on KRLST algorithm

Kernel adaptive algorithm is an extension of adaptive algorithm in nonlinear,and widely used in the field of non-stationary signal processing.But the distribution of classic data sets seems relatively regular and simple in time series.The distribution of the electroencephalograph(EEG)signal is more randomness and non-stationarity,so online prediction of EEG signal can further verify the robustness and applicability of kernel adaptive algorithms.What’s more,the purpose of modeling and analyzing the time series of EEG signals is to discover and extract valuable information,and to reveal the internal relations of EEG signals.The time series prediction of EEG plays an important role in EEG time series analysis.In this paper,kernel RLS tracker(KRLST)is presented to online predict the EEG signals of motor imagery and compared with other 13 kernel adaptive algorithms.The experimental results show that KRLST algorithm has the best effect on the brain computer interface(BCI)dataset.

Comparison of Spatio-Spectral Properties of Zen-Meditation and Resting EEG Based on Unsupervised Learning

This paper reports distinct spatio-spectral properties of Zen-meditation EEG (electroencephalograph), compared with resting EEG, by implementing unsupervised machine learning scheme in clustering the brain mappings of centroid frequency (BMFc). Zen practitioners simultaneously concentrate on the third ventricle, hypothalamus and corpora quadrigemina touniversalize all brain neurons to construct a detached brain and gradually change the normal brain traits, leading to the process of brain-neuroplasticity. During such tri-aperture concentration, EEG exhibits prominent diffuse high-frequency oscillations. Unsupervised self-organizing map (SOM), clusters the dataset of quantitative EEG by matching the input feature vector Fc and the output cluster center through the SOM network weights. Input dataset contains brain mappings of 30 centroid frequencies extracted from CWT (continuous wavelet transform) coefficients. According to SOM clustering results, resting EEG is dominated by global low-frequency (<14 Hz) activities, except channels T7, F7 and TP7 (>14.4 Hz);whereas Zen-meditation EEG exhibits globally high-frequency (>16 Hz) activities throughout the entire record. Beta waves with a wide range of frequencies are often associated with active concentration. Nonetheless, clinic report discloses that benzodiazepines, medication treatment for anxiety, insomnia and panic attacks to relieve mind/body stress, often induce beta buzz. We may hypothesize that Zen-meditation practitioners attain the unique state of mindfulness concentration under optimal body-mind relaxation.