A two-dimensional matrix image based feature extraction method for classification of sEMG: A comparative analysis based on SVM, KNN and RBF-NN

2017 ◽  
Vol 25 (2) ◽  
pp. 287-300 ◽  
Author(s):  
Tingxi Wen ◽  
Zhongnan Zhang ◽  
Ming Qiu ◽  
Ming Zeng ◽  
Weizhen Luo
Keyword(s):  
Feature Extraction ◽  
Comparative Analysis ◽  
Extraction Method ◽  
Two Dimensional ◽  
Feature Extraction Method ◽  
Dimensional Matrix ◽  
Matrix Image

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 Early Classification of Motor Tasks Using Dynamic Functional Connectivity Graphs from EEG

2020 ◽  
Author(s):  
Foroogh Shamsi ◽  
Ali Haddad ◽  
Laleh Najafizadeh
Keyword(s):  
Feature Extraction ◽  
Functional Connectivity ◽  
Extraction Method ◽  
Motor Tasks ◽  
Dynamic Functional Connectivity ◽  
Feature Extraction Method ◽  
Brain Functions ◽  
Eeg Data ◽  
Eeg Recordings

AbstractObjectiveClassification of electroencephalography (EEG) signals with high accuracy using short recording intervals has been a challenging problem in developing brain computer interfaces (BCIs). This paper presents a novel feature extraction method for EEG recordings to tackle this problem.ApproachThe proposed approach is based on the concept that the brain functions in a dynamic manner, and utilizes dynamic functional connectivity graphs. The EEG data is first segmented into intervals during which functional networks sustain their connectivity. Functional connectivity networks for each identified segment are then localized, and graphs are constructed, which will be used as features. To take advantage of the dynamic nature of the generated graphs, a Long Short Term Memory (LSTM) classifier is employed for classification.Main resultsFeatures extracted from various durations of post-stimulus EEG data associated with motor execution and imagery tasks are used to test the performance of the classifier. Results show an average accuracy of 85.32% about only 500 ms after stimulus presentation.SignificanceOur results demonstrate, for the first time, that using the proposed feature extraction method, it is possible to classify motor tasks from EEG recordings using a short interval of the data in the order of hundreds of milliseconds (e.g. 500 ms).This duration is considerably shorter than what has been reported before. These results will have significant implications for improving the effectiveness and the speed of BCIs, particularly for those used in assistive technologies.

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