Characteristic Frequency Band Research Articles

Nonlinear Modeling Analysis and Arc High-Impedance Faults Detection in Active Distribution Networks With Neutral Grounding via Petersen Coil

For Arc High-Impedance Faults (AHIFs) in Active Distribution Networks (ADNs) with neutral grounding via Petersen coil, both nonlinear characteristics caused by arc and the influence of zero-input response have been not considered in previous research. Quantitative analysis methods based on the threshold setting comparison is mostly adopted in current field applications, which cannot balance the detection reliability and sensitivity simultaneously. In response to this problem, a higher precision nonlinear AHIF equivalent model is established based on the logarithmic arc model. State equations describing the occurrences of AHIFs are solved by using piecewise linear fitting technique, and analytical expressions of zero-sequence voltage and currents are present in paper. Furthermore, the regular volt-ampere characteristic dynamic trajectory between the zero-sequence voltage and current in characteristic frequency band is analyzed, and a tangent-inverse method is proposed to extract the direction of dynamic trajectory, then an effective fault detection algorithm is designed. The validity of the proposed nonlinear equivalent model and detection algorithm are verified by various cases’ simulations and field test data.

Damage identification of wind turbine blades based on acoustic emission

Wind turbine blades are subjected to alternating fatigue load caused by wind and, as a result, are prone to damage. It is hard to investigate the damage mechanism of the blades due to their complex structure and large size. In this paper, damage to wind turbine blade composites is identified based on acoustic emission (AE). Samples of singlecomponent glass fibre/epoxy resin composite laminate [90°] and [45°] were prepared and put through a tensile process and the acoustic emission signals during this process were analysed using cluster methodology and wavelet packet transformation. The results show that there are four damage modes: matrix damage, fibre break, interface damage and delamination, with the characteristic frequency bands of 0-0.06 MHz, 0.06-0.12 MHz, 0.25-0.32 MHz and 0.37-0.45 MHz, respectively. The damage process of laminate [45°] is analysed using the damage evolution diagram. The tensile process can be divided into four stages: matrix damage and interface damage appear first, then the fibre starts to break, after which follows the stationary expansion stage, resulting in interlaminar damage until fracture.

Eigenvalue analysis of wideband acoustic immittance in secretory otitis media in children of different ages

Objective:The characteristic values of broadband acoustic immittance of children of different ages during secretory otitis media were collected resonance frequency and absorption rate of middle ear in children of different ages were compared. Methods:Eighty-two children with secretory otitis media were selected for the broadband acoustic immittance test, including 62 cases （83 ears） in the young effusion group（[3.5±0.7]months）, and 20 cases （40 ears） in the older effusion group（[4.0±0.6]years old）. 82 normal children were selected for broadband acoustic immittance test in the same environment, including 62 cases （124 ears） in the younger normal group and 20 cases （40 ears） in the older normal group. The resonance frequency and absorptivity of different age groups under broadband peak pressure were compared, and the frequency absorptivity curve was drawn for analysis. Results:The resonance frequencies of older normal group and younger normal group, older effusion group and older normal group, younger effusion group and younger normal group, older effusion group and younger effusion group were statistically significant（P<0.001）. Analysis of sound energy absorption rate at different ages. There was significant difference in sound energy absorption rate between older normal group and younger normal group at 226-1155 / 4757-8000 Hz（P<0.001）. The difference of full frequency sound energy absorption rate between the older effusion group and the older normal group was statistically significant（P<0.001）, among which the difference of 866-1542 / 2520-4362 Hz absorption rate was the largest. The difference of full frequency sound energy absorption rate between young effusion group and young normal group was statistically significant（P<0.001）, and the difference of 1091-2245 / 3776-5494 Hz absorption rate was the largest. Conclusion:Middle ear effusion in children of different ages has different performance under broadband acoustic immittance peak pressure, and has its own characteristic acoustic energy absorption frequency band. This study provides a reference basis for the analysis of the characteristics of middle ear sound transmission in children with secretory otitis media in the future.

Multi-Frequent Band Collaborative EEG Emotion Classification Method Based on Optimal Projection and Shared Dictionary Learning.

Affective computing is concerned with simulating people’s psychological cognitive processes, of which emotion classification is an important part. Electroencephalogram (EEG), as an electrophysiological indicator capable of recording brain activity, is portable and non-invasive. It has emerged as an essential measurement method in the study of emotion classification. EEG signals are typically split into different frequency bands based on rhythmic characteristics. Most of machine learning methods combine multiple frequency band features into a single feature vector. This strategy is incapable of utilizing the complementary and consistent information of each frequency band effectively. It does not always achieve the satisfactory results. To obtain the sparse and consistent representation of the multi-frequency band EEG signals for emotion classification, this paper propose a multi-frequent band collaborative classification method based on optimal projection and shared dictionary learning (called MBCC). The joint learning model of dictionary learning and subspace learning is introduced in this method. MBCC maps multi-frequent band data into the subspaces of the same dimension using projection matrices, which are composed of a common shared component and a band-specific component. This projection method can not only make full use of the relevant information across multiple frequency bands, but it can also maintain consistency across each frequency band. Based on dictionary learning, the subspace learns the correlation between frequency bands using Fisher criterion and principal component analysis (PCA)-like regularization term, resulting in a strong discriminative model. The objective function of MBCC is solved by an iterative optimization algorithm. Experiment results on public datasets SEED and DEAP verify the effectiveness of the proposed method.

Is Mate Preference Recognizable Based on Electroencephalogram Signals? Machine Learning Applied to Initial Romantic Attraction.

Initial romantic attraction (IRA) refers to a series of positive reactions toward potential ideal partners based on individual preferences; its evolutionary value lies in facilitating mate selection. Although the EEG activities associated with IRA have been preliminarily understood; however, it remains unclear whether IRA can be recognized based on EEG activity. To clarify this, we simulated a dating platform similar to Tinder. Participants were asked to imagine that they were using the simulated dating platform to choose the ideal potential partner. Their brain electrical signals were recorded as they viewed photos of each potential partner and simultaneously assessed their initial romantic attraction in that potential partner through self-reported scale responses. Thereafter, the preprocessed EEG signals were decomposed into power-related features of different frequency bands using a wavelet transform approach. In addition to the power spectral features, feature extraction also accounted for the physiological parameters related to hemispheric asymmetries. Classification was performed by employing a random forest classifier, and the signals were divided into two categories: IRA engendered and IRA un-engendered. Based on the results of the 10-fold cross-validation, the best classification accuracy 85.2% (SD = 0.02) was achieved using feature vectors, mainly including the asymmetry features in alpha (8–13 Hz), beta (13–30 Hz), and theta (4–8 Hz) rhythms. The results of this study provide early evidence for EEG-based mate preference recognition and pave the way for the development of EEG-based romantic-matching systems.

Wavelet packet coefficient of transient first half wave line selection method for single-phase grounding fault

Overvoltage will occur after single-phase grounding fault in distribution network, endangering system insulation. It is of great significance to find out the fault line as soon as possible to ensure the safe operation of the system. Wavelet modulus maxima can be used to detect the sudden change singularity of transient current. However, the judgement of single mode maximum value is easily influenced by the form of fault and interference, leading to misjudgement. In this paper, the transient characteristics of single-phase grounding fault are analyzed, and the magnitude and phase characteristics of the first half wave are given; Based on wavelet packet algorithm, several key problems such as boundary effect, wavelet selection and decomposition scale are explored; Taking the zero sequence voltage as the reference to determine the fault interval, the zero sequence current of each line is decomposed by three-scale wavelet packet using db6 wavelet, and the wavelet coefficients of the characteristic frequency band are extracted for comparison. The fault line is the one with the largest amplitude and different phase from others. The distribution system model of neutral grounding through arc suppression coil is built. Simulation is carried out for several cases with great difficulty in line selection, such as short circuit when phase voltage crosses zero, high resistance grounding and long line fault. The experimental results prove the reliability of this method.

An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces

ObjectiveElectroencephalogram (EEG) based motor imagery (MI) classification is an important aspect in brain-machine interfaces (BMIs) which bridges between neural system and computer devices decoding brain signals into recognizable machine commands. However, the MI classification task is challenging due to inherent complex properties, inter-subject variability, and low signal-to-noise ratio (SNR) of EEG signals. To overcome the above-mentioned issues, the current work proposes an efficient multi-scale convolutional neural network (MS-CNN) which can extract the distinguishable features of several non-overlapping canonical frequency bands of EEG signals from multiple scales for MI-BCI classification. ApproachIn the framework, discriminant user-specific features have been extracted and integrated to improve the accuracy and performance of the CNN classifier. Additionally, different data augmentation methods have been implemented to further improve the accuracy and robustness of the model. Main resultsThe model achieves an average classification accuracy of 93.74% and Cohen’s kappa-coefficient of 0.92 on the BCI competition IV2b dataset outperforming several baseline and current state-of-the-art EEG-based MI classification models. SignificanceThe proposed algorithm effectively addresses the shortcoming of existing CNN-based EEG-MI classification models and significantly improves the classification accuracy. The current framework can provide a stimulus for designing efficient and robust real-time human-robot interaction.

Leak Localization of Gas Pipeline Based on the Combination of EEMD and Cross-Spectrum Analysis

Pipeline transportation systems play an increasingly essential role in the development of the economy. An accurate location of leakage is important to reduce economic losses and maintain the safe operation of pipelines. In this article, a pipeline leakage detection method based on infrasound and negative pressure waves (NPWs) is adopted. This method can locate the leakage only by collecting the infrasound and pressure signals at the first station of the pipeline, which effectively solves the disadvantages of low positioning accuracy of a signal and easily produces false alarms. In this article, the signal denoising method of cross-spectral analysis combined with ensemble empirical mode decomposition (EEMD) was adopted. First, the leakage signals of sound waves and NPWs are processed by EEMD to obtain a series of intrinsic mode functions (IMFs). The characteristic frequency band is obtained by cross-spectral analysis of the leakage signal. Within this frequency band, the dominant component of the leakage source signal is divided by correlation analysis, and the IMF component is reconstructed to complete the signal denoising. When compared to the single signal pipeline leak detection method, the positioning accuracy of this article showed a 1–3 m improvement on average.

Analysis and selection of eigenvalues of vibration signals in cutting tool milling

This paper designs an experimental scheme to monitor the tool wear state by extracting the vibration signal of tool wear. The data acquisition and signal processing hardware includes a T-type cutting tool, a vibration sensor, an amplifier, a data acquisition card, and a computer. In the time domain, the vibration signal is analyzed by statistical analysis method, and it is concluded that the variance of vibration signal of X-axis wear is positively correlated with the degree of tool wear. Moreover, the vibration signal is converted from time domain to frequency domain by Fourier transform, and the characteristic frequency bands of vibration signal are 2–4 kHz and 7–9 kHz in frequency domain. In addition, in time-frequency domain, the vibration signal is decomposed by wavelet packet decomposition technology, and the energy statistics of the decomposed frequency band is carried out. It is further judged that the energy percentage of 2.5–3.75 kHz and 7.5–8.25 kHz is closely related to tool wear, so the energy percentage of the two characteristic frequency bands is selected as the characteristic value of tool wear monitoring.

Plastic Pipeline Leak Localization Based on Wavelet Packet Decomposition and Higher Order Cumulants

This paper presents a plastic pipeline leak localization method based on wavelet packet decomposition and higher order cumulants. The hydrophone sensors are used to capture the acoustic signals generated by the water leakage in the plastic pipe. The feature frequency band containing the most leak information is subsequently extracted by performing wavelet packet decomposition on the signals. The most suitable wavelet packet basis function for leak signals is obtained by comparing the effects of varieties of wavelet packet basis functions. Furthermore, the higher order cumulant method is used to estimate the time delay between the two sensors signals, from which the leak hole can be located. The simulation and experimental results show that, compared with the basic cross-correlation method (BCC) and the generalized cross-correlation method (GCC), the higher order cumulant method has higher leak localization accuracy. The relative error of the third-order cumulant method is 2.99% in experiment 1 and 7.58% in experiment 2, while the relative localization error of the fourth-order cumulant method is 1.96% in experiment 1 and 1.23% in experiment 2.

Task-State EEG Signal Classification for Spatial Cognitive Evaluation Based on Multiscale High-Density Convolutional Neural Network.

In this study, a multi-scale high-density convolutional neural network (MHCNN) classification method for spatial cognitive ability assessment was proposed, aiming at achieving the binary classification of task-state EEG signals before and after spatial cognitive training. Besides, the multi-dimensional conditional mutual information method was used to extract the frequency band features of the EEG data. And the coupling features under the combination of multi-frequency bands were transformed into multi-spectral images. At the same time, the idea of Densenet was introduced to improve the multi-scale convolutional neural network. Firstly, according to the discreteness of multispectral EEG image features, two-scale convolution kernels were used to calculate and learn useful channel and frequency band feature information in multispectral image data. Secondly, to enhance feature propagation and reduce the number of parameters, the dense network was connected after the multi-scale convolutional network, and the learning rate change function of the stochastic gradient descent algorithm was optimized to objectively evaluate the training effect. The experimental results showed that compared with the classical convolution neural network (CNN) and multi-scale convolution neural network, the proposed MHCNN had better classification performance in the six frequency band combinations with the highest accuracy of 98%: Theta-Alpha2-Gamma, Alpha2-Beta2-Gamma, Beta1-Beta2-Gamma, Theta-Beta2-Gamma, Theta- Alpha1-Gamma, and Alpha1-Alpha2-Gamma. By comparing the classification results of six frequency band combinations, it was found that the combination of the Theta-Beta2-Gamma band had the best classification effect. The MHCNN classification method proposed in this research could be used as an effective biological indicator of spatial cognitive training effect and could be extended to other brain function evaluations.

Identification on Vortex Rope in Francis Turbine Draft Tube Based on Convkurgram

Francis turbine is a core energy conversion part and low frequency vibrations caused by unsteady vortex rope in the draft tube may damage the unit through resonance. Therefore, a more comprehensive assessment and monitoring of the status of Francis turbine is of great significance for unit stability. In this hydraulic machinery, mechanical impulses and hydraulic excitation are co-existing. The former manifests as a narrow low frequency band with high kurtosis, while the latter represents a wide high frequency band with energy associated with the vortex rope. Based on that, a novel method coined Convkurgram is proposed to suppress the extreme high kurtosis mechanical components and extract the hydraulic carrier wave efficiently. Besides, it takes multiple information frequency bands into consideration in case of contamination waves exist. Finally, the method is evaluated by means of actual vibration signals collected from the hydroelectric generating unit under normal and cavitation conditions. The results show the proposed method recognizes the characteristic frequency bands of mechanical components and hydraulic carriers as well as extracts the shaft frequency and blade passing frequency successfully.

Motor Imagination of Lower Limb Movements at Different Frequencies.

Motor imagination (MI) is the mental process of only imagining an action without an actual movement. Research on MI has made significant progress in feature information detection and machine learning decoding algorithms, but there are still problems, such as a low overall recognition rate and large differences in individual execution effects, which make the development of MI run into a bottleneck. Aiming at solving this bottleneck problem, the current study optimized the quality of the MI original signal by “enhancing the difficulty of imagination tasks,” conducted the qualitative and quantitative analyses of EEG rhythm characteristics, and used quantitative indicators, such as ERD mean value and recognition rate. Research on the comparative analysis of the lower limb MI of different tasks, namely, high-frequency motor imagination (HFMI) and low-frequency motor imagination (LFMI), was conducted. The results validate the following: the average ERD of HFMI (−1.827) is less than that of LFMI (−1.3487) in the alpha band, so did (−3.4756 < −2.2891) in the beta band. In the alpha and beta characteristic frequency bands, the average ERD of HFMI is smaller than that of LFMI, and the ERD values of the two are significantly different (p=0.0074 < 0.01; r = 0.945). The ERD intensity STD values of HFMI are less than those of LFMI. which suggests that the ERD intensity individual difference among the subjects is smaller in the HFMI mode than in the LFMI mode. The average recognition rate of HFMI is higher than that of LFMI (87.84% > 76.46%), and the recognition rate of the two modes is significantly different (p=0.0034 < 0.01; r = 0.429). In summary, this research optimizes the quality of MI brain signal sources by enhancing the difficulty of imagination tasks, achieving the purpose of improving the overall recognition rate of the lower limb MI of the participants and reducing the differences of individual execution effects and signal quality among the subjects.

Relationship between Twin-to-Twin Selective Intrauterine Growth Restriction with sFas/sFasL Level of Umbilical Cord Blood Using Doppler Ultrasound Fetal Heart Rhythm Detection Algorithm

The study focused on the application value of ultrasound Doppler fetal heart rate detection algorithm based on short-time Fourier transform (STFT) in the diagnosis of twin-to-twin selective intrauterine growth restriction (sIUGR) and the correlation between twin-to-twin sIUGR and sFas/sFasL levels of umbilical cord blood. The normalized method was introduced into the STFT algorithm to optimize it to detect the fetal instantaneous heart rate. 82 pregnant women with twin pregnancies were selected as the research subjects and they were divided into the restricted group (41 cases) and the control group (41 cases) according to whether the fetus had selective growth restriction. The two groups were compared for the differences in the fetal mortality, complication rate, and sFas/sFasL expression levels. The results showed that the STFT-based ultrasonic Doppler fetal heart rate detection algorithm could ensure the quality of the fetal heart rate signal and had high resolution at the 200–400 Hz characteristic frequency band and that the accuracy in distinguishing S1 and S2 was 5.8% higher than that of the traditional autocorrelation algorithm. The proportion of abnormal fetal heart rate in the restricted group was significantly higher than that in the control group ( P &lt; 0.05 ), birth weight was significantly lower than that in the control group ( P &lt; 0.05 ), and fetal mortality was significantly higher than that in the control group ( P &lt; 0.05 ). There was no statistical difference in the incidence of complications between the two groups ( P &gt; 0.05 ). In restricted group, the content of sFas in cord blood was (3326.54 ± 317.42) pg/mL and that in the control group was (2003.29 ± 196.45) pg/mL. The content of sFas in cord blood of the restricted group was significantly higher than that of the control group ( P &lt; 0.01 ). In the restricted group, the content of sFasL in cord blood was (382.52 ± 36.17) pg/mL, and that in the control group was (180.84 ± 16.20) pg/mL. The content of sFasL in cord blood of the restricted group was significantly higher than that of the control group ( P &lt; 0.001 ). It was concluded that the STFT-based ultrasound Doppler fetal heart rate monitoring is beneficial to early diagnosis and timely intervention of twin-to-twin sIUGR.

Fault Diagnosis Method for Rotating Machinery Based on Hierarchical Amplitude-Aware Permutation Entropy and Pairwise Feature Proximity

With a view to solving the defect that multiscale amplitude-aware permutation entropy (MAAPE) can only quantify the low-frequency features of time series and ignore the high-frequency features which are equally important, a novel nonlinear time series feature extraction method, hierarchical amplitude-aware permutation entropy (HAAPE), is proposed. By constructing high and low-frequency operators, this method can extract the features of different frequency bands of time series simultaneously, so as to avoid the issue of information loss. In view of its advantages, HAAPE is introduced into the field of fault diagnosis to extract fault features from vibration signals of rotating machinery. Combined with the pairwise feature proximity (PWFP) feature selection method and gray wolf algorithm optimization support vector machine (GWO-SVM), a new intelligent fault diagnosis method for rotating machinery is proposed. In our method, firstly, HAPPE is adopted to extract the original high and low-frequency fault features of rotating machinery. After that, PWFP is used to sort the original features, and the important features are filtered to obtain low-dimensional sensitive feature vectors. Finally, the sensitive feature vectors are input into GWO-SVM for training and testing, so as to realize the fault identification of rotating machinery. The performance of the proposed method is verified using two data sets of bearing and gearbox. The results show that the proposed method enjoys obvious advantages over the existing methods, and the identification accuracy reaches 100%.

Deep learning reveals personalized spatial spectral abnormalities of high delta and low alpha bands in EEG of patients with early Parkinson’s disease

Objective. Parkinson’s disease (PD) is one of the most common neurodegenerative diseases, and early diagnosis is crucial to delay disease progression. The diagnosis of early PD has always been a difficult clinical problem due to the lack of reliable biomarkers. Electroencephalogram (EEG) is the most common clinical detection method, and studies have attempted to discover the EEG spectrum characteristics of early PD, but the reported conclusions are not uniform due to the heterogeneity of early PD patients. There is an urgent need for a more advanced algorithm to extract spectrum characteristics from EEG to satisfy the personalized requirements. Approach. The structured power spectral density with spatial distribution was used as the input of convolutional neural network (CNN). A visualization technique called gradient-weighted class activation mapping was used to extract the optimal frequency bands for identifying early PD. Based on the model visualization, we proposed a novel quantitative index of spectral characteristics, spatial-mapping relative power (SRP), to detect personalized abnormalities in the spatial spectral characteristics of EEG in early PD. Main results. We demonstrated the feasibility of applying CNN to identify the patients with early PD with an accuracy of 99.87% ± 0.03%. The models indicated the characteristic frequency bands (high-delta (3.5–4.5 Hz) and low-alpha (7.5–11 Hz) frequency bands) that are used to identify the early PD. The SRP of these two characteristic bands in early PD patients was significantly higher than that in the control group, and the abnormalities were consistent at the group and individual levels. Significance. This study provides a novel personalized detection algorithm based on deep learning to reveal the optimal frequency bands for identifying early PD and obtain the spatial frequency characteristics of early PD. The findings of this study will provide an effective reference for the auxiliary diagnosis of early PD in clinical practice.

Study on leak localization for buried gas pipelines based on an acoustic method

As an important part of a city’s “lifeline,” an urban underground natural gas pipeline network is an integral part of the infrastructure, facilitating energy transmission. If the source of a leak is not located accurately, explosive incidents may occur owing to the highly flammable nature of natural gas, causing casualties and property damage. Thus, this study presents a new method of leak identification in buried natural gas pipelines. The proposed method achieves this goal through leak noise in the soil, and this is verified through experiments conducted as part of this research. The surrounding soil of the buried pipeline was considered as the propagation medium of the noise associated with leaks. For four microphones, placed in detection holes, were used to capture leak noise within the soil. Multi-layer wavelet packet decomposition was performed on the collected time domain signal of leak noise and was reconstructed on the characteristic frequency band of 0–200 Hz. Leak localization was completed by estimating the time difference of arrival between each pair of microphones on the buried natural gas pipeline. In the experimental scenario, the leak location error of the buried gas pipeline was 8 %–12 %. Therefore, the proposed method can successfully be applied to realize leak localization for buried natural gas pipelines.

Action Intention Understanding EEG Signal Classification Based on Improved Discriminative Spatial Patterns.

Objective Action intention understanding EEG signal classification is indispensable for investigating human-computer interactions and intention understanding mechanisms. Numerous investigations on classification tasks extract classification features by using graph theory metrics; however, the classification results are usually not good. Method To effectively implement the task of action intention understanding EEG signal classification, we proposed a new feature extraction method by improving discriminative spatial patterns. Results The whole frequency band and fusion band achieved satisfactory classification accuracies. Compared with other authors' methods for action intention understanding EEG signal classification, the new method performs more satisfactorily in some aspects. Conclusions The new feature extraction method not only effectively avoids complex values when solving the generalized eigenvalue problem but also perfectly realizes appreciable classification accuracies. Fusing the classification features of different frequency bands is a useful strategy for the classification task.

Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder.

As one of the key technologies of emotion computing, emotion recognition has received great attention. Electroencephalogram (EEG) signals are spontaneous and difficult to camouflage, so they are used for emotion recognition in academic and industrial circles. In order to overcome the disadvantage that traditional machine learning based emotion recognition technology relies too much on a manual feature extraction, we propose an EEG emotion recognition algorithm based on 3D feature fusion and convolutional autoencoder (CAE). First, the differential entropy (DE) features of different frequency bands of EEG signals are fused to construct the 3D features of EEG signals, which retain the spatial information between channels. Then, the constructed 3D features are input into the CAE constructed in this paper for emotion recognition. In this paper, many experiments are carried out on the open DEAP dataset, and the recognition accuracy of valence and arousal dimensions are 89.49 and 90.76%, respectively. Therefore, the proposed method is suitable for emotion recognition tasks.

PDMS-Parylene Hybrid, Flexible Micro-ECoG Electrode Array for Spatiotemporal Mapping of Epileptic Electrophysiological Activity from Multicortical Brain Regions.

Epilepsy detection and focus location are urgent issues that need to be solved in epilepsy research. A cortex conformable and fine spatial accuracy electrocorticogram (ECoG) sensor array, especially for real-time detection of multicortical functional regions and delineating epileptic focus remains a challenge. Here, we fabricated a polydimethylsiloxane (PDMS)-parylene hybrid, flexible micro-ECoG electrode array. The multiwalled carbon nanotubes (MWCNTs)/poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) nanocomposite-modified electrode interface significantly improved the sensing performance with low impedance (20.68 ± 6.65 kΩ), stable phase offset, and high sensitivity. The electrophysiological activities of multicortical brain regions (somatosensory cortex, parietal association cortex, and visual cortex) were simultaneously monitored during normal and epileptic statuses. The epileptic ECoG activities spread spatiotemporally from the starting point toward the adjacent cortex. Significant variations of the waveform, power, and frequency band were observed. The ECoG potential (123 ± 23 μV) at normal status was prominently up to 417 ± 87 μV at the spike wave stage. Besides, the power for epileptic activity (11.049 ± 4.513 μW) was 10 times higher than that (1.092 ± 0.369 μW) for normal activity. In addition, the theta frequency band was found to be a characteristic frequency band of epileptic signals. These joint analysis results of multicortical regions indicated that the active micron-scale region on the parietal association cortex was more likely to be the epileptogenic focus. Cortical mapping with high spatial detail provides the accurate delineation of lesions. The flexible micro-ECoG electrode array is a powerful tool for constructing a spatiotemporal map of the cortex. It provides a technical platform for epileptic focus location, biomedical diagnosis, and brain-computer interaction.