scholarly journals Realization of multiple neural activity resolutions via multidimensional ensemble empirical mode decomposition of calcium imaging data: a proof of concept

2015 ◽  
Author(s):  
Samuel Akwei-Sekyere
Keyword(s):  
Empirical Mode Decomposition ◽  
Neural Activity ◽  
Calcium Imaging ◽  
Single Neuron ◽  
Ensemble Empirical Mode Decomposition ◽  
Proof Of Concept ◽  
Imaging Data ◽  
Mode Decomposition ◽  
Multiple Resolutions ◽  
The Brain

One of the goals of systems and computational neuroscience is to understand how information is processed by a single neuron and integrated by a network of neurons. A plausible approach to identifying spatial neighborhoods of the brain that host potential neural networks of interest is by observing spatially-bounded aggregates of neural activity. To this end, the potential of the multidimensional ensemble empirical mode decomposition algorithm in extracting multiple resolutions of neural activity from calcium imaging data is evaluated.

scholarly journals Realization of multiple neural activity resolutions via multidimensional ensemble empirical mode decomposition of calcium imaging data: a proof of concept

2015 ◽  
Author(s):  
Samuel Akwei-Sekyere
Keyword(s):  
Empirical Mode Decomposition ◽  
Neural Activity ◽  
Calcium Imaging ◽  
Single Neuron ◽  
Ensemble Empirical Mode Decomposition ◽  
Proof Of Concept ◽  
Imaging Data ◽  
Mode Decomposition ◽  
Multiple Resolutions ◽  
The Brain

One of the goals of systems and computational neuroscience is to understand how information is processed by a single neuron and integrated by a network of neurons. A plausible approach to identifying spatial neighborhoods of the brain that host potential neural networks of interest is by observing spatially-bounded aggregates of neural activity. To this end, the potential of the multidimensional ensemble empirical mode decomposition algorithm in extracting multiple resolutions of neural activity from calcium imaging data is evaluated.

scholarly journals A computational toolbox and step-by-step tutorial for the analysis of neuronal population dynamics in calcium imaging data

2017 ◽  
Author(s):  
Sebastián A. Romano ◽  
Verónica Pérez-Schuster ◽  
Adrien Jouary ◽  
Alessia Candeo ◽  
Jonathan Boulanger-Weill ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Calcium Imaging ◽  
Single Neuron ◽  
Neuronal Population ◽  
Population Coding ◽  
Imaging Data ◽  
Neuronal Responses ◽  
Population Activity ◽  
The Brain

The development of new imaging and optogenetics techniques to study the dynamics of large neuronal circuits is generating datasets of unprecedented volume and complexity, demanding the development of appropriate analysis tools. We present a tutorial for the use of a comprehensive computational toolbox for the analysis of neuronal population activity imaging. It consists of tools for image pre-processing and segmentation, estimation of significant single-neuron single-trial signals, mapping event-related neuronal responses, detection of activity-correlated neuronal clusters, exploration of population dynamics, and analysis of clusters’ features against surrogate control datasets. They are integrated in a modular and versatile processing pipeline, adaptable to different needs. The clustering module is capable of detecting flexible, dynamically activated neuronal assemblies, consistent with the distributed population coding of the brain. We demonstrate the suitability of the toolbox for a variety of calcium imaging datasets, and provide a case study to explain its implementation.

scholarly journals A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1170
Author(s):  
Yangyang Dai ◽  
Feng Duan ◽  
Fan Feng ◽  
Zhe Sun ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Empirical Mode Decomposition ◽  
Mean Squared Error ◽  
Simulated Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Mode Decomposition ◽  
Significant Difference ◽  
The Brain

An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.

scholarly journals Denoising electromyogram and electroencephalogram signals using improved complete ensemble empirical mode decomposition with adaptive noise

2021 ◽  
Vol 23 (2) ◽  
pp. 829
Author(s):  
S. Elouaham ◽  
A. Dliou ◽  
N. Elkamoun ◽  
R. Latif ◽  
S. Said ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
High Performance ◽  
Signal To Noise Ratio ◽  
Ensemble Empirical Mode Decomposition ◽  
Internal Reasons ◽  
Mode Decomposition ◽  
The Mean ◽  
Adaptive Noise ◽  
The Brain ◽  
Proper Analysis

The health of the brain and muscles depends on the proper analysis of electroencephalogram and electromyogram signals without noise. The latter blends into the recording of biomedical signals for external or internal reasons of the human body. Therefore, to obtain a more accurate signal, it is needed to select filtering techniques that minimize the noise. In this study, the techniques used are empirical mode decomposition and its variants. Among the new versions of variants is the improved complete ensemble empirical mode decomposition with adaptive noise. These methods are applied to electroencephalogram and electromyogram signals corrupted by natural noise and white Gaussian noise. The obtained results through the use of the improved complete ensemble empirical mode decomposition with adaptive noises how the high performance that includes minimizing the noise and the effectiveness of the components of the signals used in the present research. This method has low values of the mean square error and high values of signal-to-noise ratio compared to other methods used in this study.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

scholarly journals Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 460-477
Author(s):  
Sajjad Khan ◽  
Shahzad Aslam ◽  
Iqra Mustafa ◽  
Sheraz Aslam
Keyword(s):  
Extreme Learning Machine ◽  
Empirical Mode Decomposition ◽  
Electricity Market ◽  
Ensemble Empirical Mode Decomposition ◽  
Electricity Price ◽  
Price Forecasting ◽  
Short Term ◽  
Mode Decomposition ◽  
Electricity Price Forecasting ◽  
Learning Machine

Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts.

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