The Feature Extraction Method of EEG Signals Based on Transition Network

2017 ◽  
pp. 491-497 ◽  
Author(s):  
Mingmin Liu ◽  
Qingfang Meng ◽  
Qiang Zhang ◽  
Dong Wang ◽  
Hanyong Zhang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Eeg Signals ◽  
Feature Extraction Method ◽  
Transition Network

Combined feature extraction method for classification of EEG signals

2016 ◽  
Vol 28 (11) ◽  
pp. 3153-3161 ◽  
Author(s):  
Yong Zhang ◽  
Xiaomin Ji ◽  
Bo Liu ◽  
Dan Huang ◽  
Fuding Xie ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Eeg Signals ◽  
Feature Extraction Method ◽  
Combined Feature

Feature extraction method for classification of alertness and drowsiness states EEG signals

2020 ◽  
Vol 163 ◽  
pp. 107224 ◽  
Author(s):  
Varun Bajaj ◽  
Sachin Taran ◽  
Smith K. Khare ◽  
Abdulkadir Sengur
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Eeg Signals ◽  
Feature Extraction Method

scholarly journals A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1631 ◽  
Author(s):  
Dong-Wei Chen ◽  
Rui Miao ◽  
Wei-Qi Yang ◽  
Yong Liang ◽  
Hao-Heng Chen ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Time Complexity ◽  
Extraction Method ◽  
Differential Entropy ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Original Dataset

Feature extraction of electroencephalography (EEG) signals plays a significant role in the wearable computing field. Due to the practical applications of EEG emotion calculation, researchers often use edge calculation to reduce data transmission times, however, as EEG involves a large amount of data, determining how to effectively extract features and reduce the amount of calculation is still the focus of abundant research. Researchers have proposed many EEG feature extraction methods. However, these methods have problems such as high time complexity and insufficient precision. The main purpose of this paper is to introduce an innovative method for obtaining reliable distinguishing features from EEG signals. This feature extraction method combines differential entropy with Linear Discriminant Analysis (LDA) that can be applied in feature extraction of emotional EEG signals. We use a three-category sentiment EEG dataset to conduct experiments. The experimental results show that the proposed feature extraction method can significantly improve the performance of the EEG classification: Compared with the result of the original dataset, the average accuracy increases by 68%, which is 7% higher than the result obtained when only using differential entropy in feature extraction. The total execution time shows that the proposed method has a lower time complexity.

Feature Extraction of EEG Signals Based on Local Mean Decomposition and Fuzzy Entropy

2020 ◽  
Vol 34 (12) ◽  
pp. 2058017
Author(s):  
Yanping Li ◽  
Qi Wang ◽  
Tao Wang ◽  
Jian Pei ◽  
Shuo Zhang
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Extraction Method ◽  
Decomposition Method ◽  
Fuzzy Entropy ◽  
Eeg Signals ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Local Mean

An improved feature extraction method is proposed aiming at the recognition of motor imagined electroencephalogram (EEG) signals. Using local mean decomposition, the algorithm decomposes the original signal into a series of product function (PF) components, and meaningless PF components are removed from EEG signals in the range of mu rhythm and beta rhythm. According to the principle of feature time selection, 4[Formula: see text]s to 6[Formula: see text]s motor imagery EEG signals are selected as classification data, and the sum of fuzzy entropies of second-and third-order PF components of [Formula: see text], [Formula: see text] lead signals is calculated, respectively. Mean value of fuzzy entropy [Formula: see text] is used as input element to construct EEG feature vector, and support vector machine (SVM) is used to classify and predict EEG signals for recognition. The test results show that this feature extraction method has higher classification accuracy than the empirical mode decomposition method and the total empirical mode decomposition method.

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