Analysis of Epileptic EEG Signals by Means of Empirical Mode Decomposition and Time-Varying Two-Sided Autoregressive modelling

A brain-machine interface (BMI) is a communication system that translates human brain activity into commands, and then these commands are conveyed to a machine or a computer. It is proposes a technique for features extraction from electroencephalographic (EEG) signals and afterward, their classification on different mental tasks. The empirical mode decomposition (EMD) is a method capable of processing non-stationary and nonlinear signals, as the EEG. The EMD was applied on EEG signals of seven subjects performing five mental tasks. Six features were computed, namely, root mean square (RMS), variance, Shannon entropy, Lempel–Ziv complexity value, and central and maximum frequencies. In order to reduce the dimensionality of the feature vector, the Wilks' lambda (WL) parameter was used for the selection of the most important variables. The classification of mental tasks was performed using linear discriminant analysis (LDA) and neural networks (NN). Using this method, the average classification over all subjects in database is 91 ± 5% and 87 ± 5% using LDA and NN, respectively. Bit rate was ranging from 0.24 bits/trial up to 0.84 bits/trial. The proposed method allows achieving higher performances in the classification of mental tasks than other traditional methods using the same database. This represents an improvement in the brain-machine communication system.

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Optimal signal reconstruction based on time-varying weighted empirical mode decomposition

Computers & Electrical Engineering ◽

10.1016/j.compeleceng.2016.12.006 ◽

2017 ◽

Vol 57 ◽

pp. 28-42 ◽

Cited By ~ 3

Author(s):

Aydin Kizilkaya ◽

Mehmet D. Elbi

Keyword(s):

Empirical Mode Decomposition ◽

Signal Reconstruction ◽

Time Varying ◽

Mode Decomposition ◽

Optimal Signal

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Short Term Traffic Flow Prediction of Urban Road Using Time Varying Filtering Based Empirical Mode Decomposition

Applied Sciences ◽

10.3390/app10062038 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2038 ◽

Cited By ~ 1

Author(s):

Yanpeng Wang ◽

Leina Zhao ◽

Shuqing Li ◽

Xinyu Wen ◽

Yang Xiong

Keyword(s):

Traffic Flow ◽

Root Mean Square ◽

Empirical Mode Decomposition ◽

Original Data ◽

Time Varying ◽

Mean Square ◽

Short Term ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Mode Decomposition

Short-term traffic flow prediction is important to realize real-time traffic instruction. However, due to the existing strong nonlinearity and non-stationarity in short-term traffic volume data, it is hard to obtain a satisfactory result through the traditional method. To this end, this paper develops an innovative hybrid method based on the time varying filtering based empirical mode decomposition (TVF-EMD) and least square support vector machine (LSSVM). Specifically, TVF-EMD is firstly used to deal with the implied non-stationarity in the original data by decomposing them into several different subseries. Then, the LSSVM models are established for each subseries to capture the linear and nonlinear characteristics embedded in the original data, and the corresponding prediction results are superimposed to obtain the final one. Finally, case studies based on two groups of data measured from an arterial road intersection are employed to evaluate the performance of the proposed method. The experimental results indicate it outperforms the other involved models. For example, compared with the LSSVM model, the average improvements by the proposed method in terms of the indexes of mean absolute error, mean relative percentage error, root mean square error and root mean square relative error are 7.397, 15.832%, 10.707 and 24.471%, respectively.

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