ObjectiveTo explore the clinical value of video-electroencephalograph (VEEG) for non-epileptic seizures disease in children. MethodsThe clinical data of 58 children with non-epileptic seizures (NES) diagnosed by VEEG from October 2010 to November 2012 were retrospectively analyzed. ResultsIn 50 out of 58 patients in the process of monitoring,the NES clinical onset was found while no synchronized epileptiform discharges was observed;in five patients with NES combined with epilepsy,no epileptiform discharges was found by VEEG at the clinical onset of NES;there were 3 patients with epileptiform discharges without seizures,who had no history of epilepsy,but non-synchronized clinical nonparoxysmal epileptiform discharges was found by VEEG monitoring. ConclusionVEEG is an effective diagnosis method for NES and seizures in children,which could be regarded as the gold standard for NES diagnosis.
ObjectiveTo investigate the video-electroencephalography (VEEG) characteristics of old patients with epilepsy (OPWE). MethodsBetween June 2013 and July 2014, 57 OPWE at an age over 60 years were assigned to research group and 65 adults between 16 and 60 years old with epilepsy were regarded as controls. All the subjects underwent VEEG for 24 hours covering awake state and sleep with hyperventilation test being applied. Chi square was used to compare occurrence rate of epileptic wave and abnormal response rate after hyperventilation between the two groups of patients. Additionally, ictal elcetroencephalograph (EEG) was analyzed. ResultsCommon features of waves on EEG for patients in both the two groups during the ictal period included widespread low amplitude fast wave (2 cases in the research group, 7.4%; 4 cases in the control group, 12.5%), focal low amplitude fast wave (5 cases in the research group, 18.5%; 6 cases in the control group, 18.8%), widespread spike or spike slowing complex (3 cases in the research group, 11.1%; 7 case in the control group, 21.8%), focal spike or spike slowing complex (5 cases in the research group, 14.9%; 8 cases in the control group, 25.0%), and focal rhythmic slow wave (6 cases in the research group, 18.5%; 6 cases in the control group, 18.8%). In the research group, there were two following cases:single abnormal background activity in 5 cases (18.5%), and neither abnormal background activity nor epileptic discharge in 1 case (3.7%). Ictal focal epileptic discharges were found in 16 cases in the research group and 8 in the control group (59.3% vs 25.0%), with statistical difference (P<0.05). Inter-ictal epilepsy discharges were found in 57 patients of the research group (awake, 15.8%; sleep, 52.6%), which was less than that in the control group (awake, 46.2%; sleep, 83.1%) with statistical difference (P<0.05), accompanied by focal slow wave (temporal intermittent rhythmic delta activity, TIRDA) in 9 cases. In natural sleep period, epilepsy discharge occurrences increased (65.3%). Abnormal response rate in the research group (14.0%) was lower than that in the control group (64.6%) with statistical difference (P<0.05). ConclusionEarly onset EEG of the old and the adult are similar except those with single abnormal background activity and those with neither abnormal background activity nor epileptic discharge. Focal onset on EEG is more frequently seen in OPWE than in APWE. In natural sleep, epileptic discharge increases among OPWE, and abnormal response during hyperventilation is less likely to happen in OPWE.
Electroencephalogram (EEG) classification for brain-computer interface (BCI) is a new way of realizing human-computer interreaction. In this paper the application of semi-supervised sparse representation classifier algorithms based on help training to EEG classification for BCI is reported. Firstly, the correlation information of the unlabeled data is obtained by sparse representation classifier and some data with high correlation selected. Secondly, the boundary information of the selected data is produced by discriminative classifier, which is the Fisher linear classifier. The final unlabeled data with high confidence are selected by a criterion containing the information of distance and direction. We applied this novel method to the three benchmark datasets, which were BCIⅠ, BCIⅡ_Ⅳ and USPS. The classification rate were 97%,82% and 84.7%, respectively. Moreover the fastest arithmetic rate was just about 0.2 s. The classification rate and efficiency results of the novel method are both better than those of S3VM and SVM, proving that the proposed method is effective.
Electroencephalogram (EEG) is the primary tool in investigation of the brain science. It is necessary to carry out a deepgoing study into the characteristics and information hidden in EEGs to meet the needs of the clinical research. In this paper, we present a wavelet-nonlinear dynamic methodology for analysis of nonlinear characteristic of EEGs and delta, theta, alpha, and beta sub-bands. We therefore studied the effectiveness of correlation dimension (CD), largest Lyapunov exponen, and approximate entropy (ApEn) in differentiation between the interictal EEG and ictal EEG based on statistical significance of the differences. The results showed that the nonlinear dynamic characteristic of EEG and EEG subbands could be used as effective identification statistics in detecting seizures.
Feature extraction is a very crucial step in P300-based brain-computer interface (BCI) and independent component analysis (ICA) is a suitable P300 feature extraction method. But at present the convergence performance of the general ICA iteration methods are not very satisfactory. In this paper, a method based on quantum particle swarm optimizer (QPSO) algorithm and ICA technique is put forward for P300 extraction. In this method, quantum computing is used to impel ICA iteration to globally converge faster. It achieved the purpose of extracting P300 rapidly and efficiently. The method was tested on two public datasets of BCI Competition Ⅱ and Ⅲ, and a simple linear classifier was employed to classify the extracted P300 features. The recognition accuracy reached 94.4% with 15 times averaged. The results showed that the proposed method could extract P300 rapidly and the extraction effect did not reduce. It provides an experimental basis for further study of real-time BCI system.
The validity and reasonableness of emotional data are the key issues in the cognitive affective computing research. Effects of the emotion recognition are decided by the quality of selected data directly. Therefore, it is an important part of affective computing research to build affective computing database with good performance, so that it is the hot spot of research in this field. In this paper, the performance of two classical cognitive affective computing databases, the Massachusetts Institute of Technology (MIT) cognitive affective computing database and Germany Augsburg University emotion recognition database were compared, their data structure and data types were compared respectively, and emotional recognition effect based on the data were studied comparatively. The results indicated that the analysis based on the physical parameters could get the effective emotional recognition, and would be a feasible method of pressure emotional evaluation. Because of the lack of stress emotional evaluation data based on the physiological parameters domestically, there is not a public stress emotional database. We hereby built a dataset for the stress evaluation towards the high stress group in colleges, candidates of postgraduates of Ph.D and master as the subjects. We then acquired their physiological parameters, and performed the pressure analysis based on this database. The results indicated that this dataset had a certain reference value for the stress evaluation, and we hope this research can provide a reference and support for emotion evaluation and analysis.
Wavelet entropy is a quantitative index to describe the complexity of signals. Continuous wavelet transform method was employed to analyze the spontaneous electroencephalogram (EEG) signals of mild, moderate and severe Alzheimer's disease (AD) patients and normal elderly control people in this study. Wavelet power spectrums of EEG signals were calculated based on wavelet coefficients. Wavelet entropies of mild, moderate and severe AD patients were compared with those of normal controls. The correlation analysis between wavelet entropy and MMSE score was carried out. There existed significant difference on wavelet entropy among mild, moderate, severe AD patients and normal controls (P<0.01). Group comparisons showed that wavelet entropy for mild, moderate, severe AD patients was significantly lower than that for normal controls, which was related to the narrow distribution of their wavelet power spectrums. The statistical difference was significant (P<0.05). Further studies showed that the wavelet entropy of EEG and the MMSE score were significantly correlated (r=0.601-0.799, P<0.01). Wavelet entropy is a quantitative indicator describing the complexity of EEG signals. Wavelet entropy is likely to be an electrophysiological index for AD diagnosis and severity assessment.
Using electroencephalogram (EEG) signal to control external devices has always been the research focus in the field of brain-computer interface (BCI). This is especially significant for those disabilities who have lost capacity of movements. In this paper, the P300-based BCI and the microcontroller-based wireless radio frequency (RF) technology are utilized to design a smart home control system, which can be used to control household appliances, lighting system, and security devices directly. Experiment results showed that the system was simple, reliable and easy to be populirised.
Neurofeedback, as an alternative treatment method of behavioral medicine, is a technique which translates the electroencephalogram (EEG) signals to styles as sounds or animation to help people understand their own physical status and learn to enhance or suppress certain EEG signals to regulate their own brain functions after several repeated trainings. This paper develops a neurofeedback system on the foundation of brain-computer interface technique. The EEG features are extracted through real-time signal process and then translated to feedback information. Two feedback screens are designed for relaxation training and attention training individually. The veracity and feasibility of the neurofeedback system are validated through system simulation and preliminary experiment.
It is the functional connectivity between motor cortex and muscle that directly relates to the rehabilitation of the dysfunction in upper limbs and neuromuscular activity status, which can be detected by electroencephalogram-electromyography (EEG-EMG) coherence analysis. In this study, based on coherence analysis method, we process the acquisition signals which consist of 9 channel EEG signal from motor cortex and 4 channel EMG signal from forearm, by using 4 groups of hand motions in the healthy subjects, including flexor digitorum, extensor digitorum, wrist flexion, and wrist extension. The results showed that in the β-band, the coherence coefficients between C3 and flexor digitorum (FD) was greater than extensor digitorum (ED) in the right hand flexor digitorum movement; the coherence coefficients between C3 and ED was greater than FD in the right hand extensor digitorum movement; the coherence coefficients between C3 and flexor carpi ulnaris (FCU) was greater than extensor carpi radialis (ECR) in the right hand wrist flexion movement; the coherence coefficients between C3 and ECR was greater than FCU in the right hand wrist extension movement. This analysis provides experimental basis to explore the information decoding of hand motion based on corticomuscular coherence (CMC).