Aiming at the problem of low recognition accuracy of motor imagery electroencephalogram signal due to individual differences of subjects, an individual adaptive feature representation method of motor imagery electroencephalogram signal is proposed in this paper. Firstly, based on the individual differences and signal characteristics in different frequency bands, an adaptive channel selection method based on expansive relevant features with label F (ReliefF) was proposed. By extracting five time-frequency domain observation features of each frequency band signal, ReliefF algorithm was employed to evaluate the effectiveness of the frequency band signal in each channel, and then the corresponding signal channel was selected for each frequency band. Secondly, a feature representation method of common space pattern (CSP) based on fast correlation-based filter (FCBF) was proposed (CSP-FCBF). The features of electroencephalogram signal were extracted by CSP, and the best feature sets were obtained by using FCBF to optimize the features, so as to realize the effective state representation of motor imagery electroencephalogram signal. Finally, support vector machine (SVM) was adopted as a classifier to realize identification. Experimental results show that the proposed method in this research can effectively represent the states of motor imagery electroencephalogram signal, with an average identification accuracy of (83.0±5.5)% for four types of states, which is 6.6% higher than the traditional CSP feature representation method. The research results obtained in the feature representation of motor imagery electroencephalogram signal lay the foundation for the realization of adaptive electroencephalogram signal decoding and its application.
This paper aims to assist the individual clinical diagnosis of children with attention-deficit/hyperactivity disorder using electroencephalogram signal detection method. Firstly, in our experiments, we obtained and studied the electroencephalogram signals from fourteen attention-deficit/hyperactivity disorder children and sixteen typically developing children during the classic interference control task of Simon-spatial Stroop, and we completed electroencephalogram data preprocessing including filtering, segmentation, removal of artifacts and so on. Secondly, we selected the subset electroencephalogram electrodes using principal component analysis (PCA) method, and we collected the common channels of the optimal electrodes which occurrence rates were more than 90% in each kind of stimulation. We then extracted the latency (200~450 ms) mean amplitude features of the common electrodes. Finally, we used the k-nearest neighbor (KNN) classifier based on Euclidean distance and the support vector machine (SVM) classifier based on radial basis kernel function to classify. From the experiment, at the same kind of interference control task, the attention-deficit/hyperactivity disorder children showed lower correct response rates and longer reaction time. The N2 emerged in prefrontal cortex while P2 presented in the inferior parietal area when all kinds of stimuli demonstrated. Meanwhile, the children with attention-deficit/hyperactivity disorder exhibited markedly reduced N2 and P2 amplitude compared to typically developing children. KNN resulted in better classification accuracy than SVM classifier, and the best classification rate was 89.29% in StI task. The results showed that the electroencephalogram signals were different in the brain regions of prefrontal cortex and inferior parietal cortex between attention-deficit/hyperactivity disorder and typically developing children during the interference control task, which provided a scientific basis for the clinical diagnosis of attention-deficit/hyperactivity disorder individuals.
To enhance the accuracy of computer-aided diagnosis of adolescent depression based on electroencephalogram signals, this study collected signals of 32 female adolescents (16 depressed and 16 healthy, age: 16.3 ± 1.3) with eyes colsed for 4 min in a resting state. First, based on the phase synchronization between the signals, the phase-locked value (PLV) method was used to calculate brain functional connectivity in the θ and α frequency bands, respectively. Then based on the graph theory method, the network parameters, such as strength of the weighted network, average characteristic path length, and average clustering coefficient, were calculated separately (P < 0.05). Next, using the relationship between multiple thresholds and network parameters, the area under the curve (AUC) of each network parameter was extracted as new features (P < 0.05). Finally, support vector machine (SVM) was used to classify the two groups with the network parameters and their AUC as features. The study results show that with strength, average characteristic path length, and average clustering coefficient as features, the classification accuracy in the θ band is increased from 69% to 71%, 66% to 77%, and 50% to 68%, respectively. In the α band, the accuracy is increased from 72% to 79%, 69% to 82%, and 65% to 75%, respectively. And from overall view, when AUC of network parameters was used as a feature in the α band, the classification accuracy is improved compared to the network parameter feature. In the θ band, only the AUC of average clustering coefficient was applied to classification, and the accuracy is improved by 17.6%. The study proved that based on graph theory, the method of feature optimization of brain function network could provide some theoretical support for the computer-aided diagnosis of adolescent depression.
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).
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.
Aiming at the limitations of clinical diagnosis of Parkinson’s disease (PD) with rapid eye movement sleep behavior disorder (RBD), in order to improve the accuracy of diagnosis, an intelligent-aided diagnosis method based on few-channel electroencephalogram (EEG) and time-frequency deep network is proposed for PD with RBD. Firstly, in order to improve the speed of the operation and robustness of the algorithm, the 6-channel scalp EEG of each subject were segmented with the same time-window. Secondly, the model of time-frequency deep network was constructed and trained with time-window EEG data to obtain the segmentation-based classification result. Finally, the output of time-frequency deep network was postprocessed to obtain the subject-based diagnosis result. Polysomnography (PSG) of 60 patients, including 30 idiopathic PD and 30 PD with RBD, were collected by Nanjing Brain Hospital Affiliated to Nanjing Medical University and the doctor’s detection results of PSG were taken as the gold standard in our study. The accuracy of the segmentation-based classification was 0.902 4 in the validation set. The accuracy of the subject-based classification was 0.933 3 in the test set. Compared with the RBD screening questionnaire (RBDSQ), the novel approach has clinical application value.
Simultaneous recording of electroencephalogram (EEG)-functional magnetic resonance imaging (fMRI) plays an important role in scientific research and clinical field due to its high spatial and temporal resolution. However, the fusion results are seriously influenced by ballistocardiogram (BCG) artifacts under MRI environment. In this paper, we improve the off-line constrained independent components analysis using real-time technique (rt-cICA), which is applied to the simulated and real resting-state EEG data. The results show that for simulated data analysis, the value of error in signal amplitude (Er) obtained by rt-cICA method was obviously lower than the traditional methods such as average artifact subtraction (P<0.005). In real EEG data analysis, the improvement of normalized power spectrum (INPS) calculated by rt-cICA method was much higher than other methods (P<0.005). In conclusion, the novel method proposed by this paper lays the technical foundation for further research on the fusion model of EEG-fMRI.
Objective To explore the actual experience of epileptic patients in video electroencephalogram (VEEG) examination, and to provide reference basis for formulating corresponding nursing strategies and coping methods. MethodsIn this descriptive analysis study, 18 patients (11 males and 7 females, average age 37.78±18.7 years) receiving VEEG from January to April 2022 in the Second Affiliated Hospital of Guangzhou Medical University, underwent a semi-structural interview. Information obtained from the interview was analyzed using the Colaizzi 7-step method. ResultsThe actual experience of epileptic patients in video EEG examination can be summarized into two aspects: the medical experience and the need for nursing care. The medical experience includes positive and negative experience. The positive experience includes good service attitude, professional medical services, good endurance, and being hopeful. The negative experience includes a weird feeling in the head, insomnia, inconvenience in life, eye discomfort, psychological pressure, and pain. The need for nursing care includes needs for knowledge, strong needs for communicating with doctors, needs for humanistic care and female needs for female implementing the equipment. Conclusion Epileptic patients suffer from different degrees of discomfort and psychological pressure during VEEG examination. Both negative and positive experience exist. Medical staff should improve the content of nursing services according to the nursing needs of patients and provide professional VEEG examination services to patients.
The electroencephalogram (EEG) has proved to be a valuable tool in the study of comprehensive conditions whose effects are manifest in the electrical brain activity, and epilepsy is one of such conditions. In the study, multi-scale permutation entropy (MPE) was proposed to describe dynamical characteristics of EEG recordings from epilepsy and healthy subjects, then all the characteristic parameters were forwarded into a support vector machine (SVM) for classification. The classification accuracies of the MPE with SVM were evaluated by a series of experiments. It is indicated that the dynamical characteristics of EEG data with MPE could identify the differences among healthy, inter-ictal and ictal states, and there was a reduction of MPE of EEG from the healthy and inter-ictal state to the ictal state. Experimental results demonstrated that average classification accuracy was 100% by using the MPE as a feature to characterize the healthy and seizure, while 99.58% accuracy was obtained to distinguish the seizure-free and seizure EEG. In addition, the single-scale permutation entropy (PE) at scales 1-5 was put into the SVM for classification at the same time for comparative analysis. The simulation results demonstrated that the proposed method could be a very powerful algorithm for seizure prediction and could have much better performance than the methods based on single scale PE.
Early detection and timely intervention are very essential for autism. This paper used the wavelet transform and empirical mode decomposition (EMD) to extract the features of electroencephalogram (EEG), to compare the feature differences of EEG between the autistic children and healthy children. The experimental subjects included 25 healthy children (aged 5–10 years old) and 25 children with autism (20 boys and 5 girls aged 5–10 years old) respectively. The alpha, beta, theta and delta rhythm wave spectra of the C3, C4, F3, F4, F7, F8, FP1, FP2, O1, O2, P3, P4, T3, T4, T5 and T6 channels were extracted and decomposed by EMD decomposition to obtain the intrinsic modal functions. Finally the support vector machine (SVM) classifier was used to implement assessment of autism and normal classification. The results showed that the accuracy could reach 87% and which was nearly 20% higher than that of the model combining the wavelet transform and sample entropy in the paper. Moreover, the accuracy of delta (1–4 Hz) rhythm wave was the highest among the four kinds of rhythms. And the classification accuracy of the forehead F7 channel, left FP1 channel and T6 channel in the temporal region were all up to 90%, which expressed the characteristics of EEG signals in autistic children better.