Sample-by-Sample Detection of Movement Intention from EEG Using a Classifier with Optimized Decision Parameters

2013 ◽  
pp. 653-658 ◽  
Author(s):  
Wanjoo Park ◽  
Jae-Hwan Kang ◽  
Gyuhyun Kwon ◽  
Laehyun Kim ◽  
Sung-Phil Kim
Keyword(s):  
Movement Intention

scholarly journals Reprint of “Non-causal spike filtering improves decoding of movement intention for intracortical BCIs”

2015 ◽  
Vol 244 ◽  
pp. 94-103 ◽  
Author(s):  
Nicolas Y. Masse ◽  
Beata Jarosiewicz ◽  
John D. Simeral ◽  
Daniel Bacher ◽  
Sergey D. Stavisky ◽  
...  
Keyword(s):  
Movement Intention

scholarly journals Exploration of neural correlates of movement intention based on characterisation of temporal dependencies in electroencephalography

PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0193722 ◽  
Author(s):  
Maitreyee Wairagkar ◽  
Yoshikatsu Hayashi ◽  
Slawomir J. Nasuto
Keyword(s):  
Neural Correlates ◽  
Movement Intention ◽  
Temporal Dependencies

A Self-Selected Movement Classification Method for Forearm Via sEMG and Attitude Sensor

2020 ◽  
Author(s):  
Lei Zhang
Keyword(s):  
Dimensional Space ◽  
Angular Acceleration ◽  
Principal Component ◽  
Classification Method ◽  
K Nearest Neighbor ◽  
External Interference ◽  
Linear Discriminant ◽  
Rehabilitation Training ◽  
Movement Intention ◽  
Attitude Sensor

Abstract Objective: The use of surface electromyography (sEMG) to realize the recognition of the movement intention can realize the control of the artificial hand or the robot, and can help the rehabilitation training for hemiplegia or muscle weakness. However, the sEMG are weak and susceptible to external interference, so the current research focuses on identifying certain types of movements. But once the subjects are changed, the recognition accuracy will greatly reduce. This study proposes a classification method which the subject could choose optional movements of forearm.Methods: Two sEMG sensors were used, and a 9-axis attitude sensor was added to the wrist. 8 different subjects participated in the experiment, and everyone selected 5 movements. The sEMG sensors were attached to the extensor pollicis brevis and the extensor digitorum. The sEMG features were: Standard Deviation (SD), Power Spectrum Density (PSD); attitude sensor features were: angle and angular acceleration in three dimensional space, and integral of angular acceleration. The results were classified and identified using Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN), Decision Tree (DT) and Ensembles (En) algorithms. The results of using the sEMG, using the attitude sensor signals and combining the two were compared. Analysis of variance was conducted on the average accuracy. Features were reduced the dimension by the Principal Component Analysis (PCA), and the results of using PCA and not were compared. Results: The results showed that the combination of the two types of sensors could improve the recognition effect compared to the using sEMG sensor or the attitude sensor alone. The final recognition result was that KNN performed best, reaching 95.0%. The results of using PCA were more stable.Conclusion: The method could be used between different subjects, and the user could select the movements autonomously.Significance: This method can improve the adaptability of movement intention recognition based on sEMG, and has important significance for popularizing the use of the sEMG to control the manipulator or the prosthetic and the rehabilitation training.

Using Morphological-Linear Neural Network for Upper Limb Movement Intention Recognition from EEG Signals

2019 ◽  
pp. 389-397
Author(s):  
Gerardo Hernández ◽  
Luis G. Hernández ◽  
Erik Zamora ◽  
Humberto Sossa ◽  
Javier M. Antelis ◽  
...  
Keyword(s):  
Neural Network ◽  
Upper Limb ◽  
Limb Movement ◽  
Eeg Signals ◽  
Intention Recognition ◽  
Linear Neural Network ◽  
Movement Intention
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