Single trial discrimination between right and left hand movement with EEG signal

2004 ◽  
Author(s):  
J.A. Kim ◽  
D.U. Hwang ◽  
S.Y. Cho ◽  
S.K. Han
Keyword(s):  
Hand Movement ◽  
Eeg Signal ◽  
Single Trial ◽  
Left Hand ◽  
Right And Left Hand

EEG efficient classification of imagined right and left hand movement using RBF kernel SVM and the joint CWT_PCA

AI & Society ◽  
2017 ◽  
Vol 33 (4) ◽  
pp. 621-629 ◽  
Author(s):  
Rihab Bousseta ◽  
Salma Tayeb ◽  
Issam El Ouakouak ◽  
Mourad Gharbi ◽  
Fakhita Regragui ◽  
...  
Keyword(s):  
Hand Movement ◽  
Left Hand ◽  
Rbf Kernel ◽  
Right And Left Hand ◽  
Kernel Svm

EEG-based discrimination between imagination of right and left hand movement

1997 ◽  
Vol 103 (6) ◽  
pp. 642-651 ◽  
Author(s):  
G. Pfurtscheller ◽  
Ch. Neuper ◽  
D. Flotzinger ◽  
M. Pregenzer
Keyword(s):  
Hand Movement ◽  
Left Hand ◽  
Right And Left Hand

Regional cerebral blood flow comparison of right and left hand movement

Neurology ◽  
1979 ◽  
Vol 29 (1) ◽  
pp. 21-21 ◽  
Author(s):  
J. H. Halsey ◽  
U. W. Blauenstein ◽  
E. M. Wilson ◽  
E. H. Wills
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Regional Cerebral Blood Flow ◽  
Hand Movement ◽  
Regional Cerebral Blood ◽  
Left Hand ◽  
Right And Left Hand

scholarly journals Towards Development of a 3-State Self-Paced Brain-Computer Interface

2007 ◽  
Vol 2007 ◽  
pp. 1-8 ◽  
Author(s):  
Ali Bashashati ◽  
Rabab K. Ward ◽  
Gary E. Birch
Keyword(s):  
Brain Computer Interface ◽  
False Positive Rate ◽  
Hand Movement ◽  
True Positive Rate ◽  
Computer Interface ◽  
True Positive ◽  
Left Hand ◽  
Positive Rate ◽  
Right And Left Hand ◽  
Ongoing Eeg

Most existing brain-computer interfaces (BCIs) detect specific mental activity in a so-called synchronous paradigm. Unlike synchronous systems which are operational at specific system-defined periods, self-paced (asynchronous) interfaces have the advantage of being operational at all times. The low-frequency asynchronous switch design (LF-ASD) is a 2-state self-paced BCI that detects the presence of a specific finger movement in the ongoing EEG. Recent evaluations of the 2-state LF-ASD show an average true positive rate of 41% at the fixed false positive rate of 1%. This paper proposes two designs for a 3-state self-paced BCI that is capable of handling idle brain state. The two proposed designs aim at detecting right- and left-hand extensions from the ongoing EEG. They are formed of two consecutive detectors. The first detects the presence of a right- or a left-hand movement and the second classifies the detected movement as a right or a left one. In an offline analysis of the EEG data collected from four able-bodied individuals, the 3-state brain-computer interface shows a comparable performance with a 2-state system and significant performance improvement if used as a 2-state BCI, that is, in detecting the presence of a right- or a left-hand movement (regardless of the type of movement). It has an average true positive rate of 37.5% and 42.8% (at false positives rate of 1%) in detecting right- and left-hand extensions, respectively, in the context of a 3-state self-paced BCI and average detection rate of 58.1% (at false positive rate of 1%) in the context of a 2-state self-paced BCI.

scholarly journals Classification of Right/Left Hand Motor Imagery by Effective Connectivity Based on Transfer Entropy in EEG Signal

2021 ◽  
pp. 1-22
Author(s):  
Erfan Rezaei ◽  
◽  
Ahmad Shalbaf ◽  
Keyword(s):  
Feature Selection ◽  
Motor Imagery ◽  
Effective Connectivity ◽  
Feature Subset Selection ◽  
Transfer Entropy ◽  
Support Vector ◽  
Feature Subset ◽  
Eeg Signal ◽  
Left Hand ◽  
Right And Left Hand

The right and left hand Motor Imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchical feature selection and classification for discrimination of right and left hand MI task. TE is calculated among EEG channels as the distinctive, effective connectivity features. TE is a model-free method that can measure nonlinear effective connectivity and analyze multivariate dependent directed information flow among neural EEG channels. Then four feature subset selection methods namely Relief-F, Fisher, Laplacian and local learning based clustering (LLCFS) algorithms are used to choose the most significant effective connectivity features and reduce redundant information. Finally, support vector machine (SVM) and LDA methods are used for classification. Results show that the best performance in 29 healthy subjects and 60 trials is achieved using the TE method via Relief-F algorithm as feature selection and SVM classification with 91.02% accuracy. Consequently, TE index and a hierarchical feature selection and classification could be useful for discrimination of right and left hand MI task from multichannel EEG signal.

scholarly journals Autonomous Surface Vehicle Controlling Menggunakan Kinect untuk Observasi Terumbu Karang

2019 ◽  
Vol 6 (2) ◽  
pp. 85-92
Author(s):  
Khafiddurrohman Agustianto ◽  
Dyah Ayu Dwijayanti
Keyword(s):  
Hand Movement ◽  
Left Hand ◽  
Autonomous Surface Vehicles ◽  
Brushless Motor ◽  
Autonomous Surface Vehicle ◽  
Tracking Process ◽  
Oceanographic Data ◽  
The Right ◽  
Right And Left Hand ◽  
Effect Of Light

Autonomous Surface Vehicles (ASV) is a platform that is capable of recognizing oceanographic data that is moving on the surface of the sea. The ASV is commonly controlled using a remote RC, smartphones, joysticks, and keyboards. The development of control system innovations in the form of NUI, then ASV can be controlled using Kinect by someone without having to touch the remote. The study developed a marker-less ASV control using the Kinect skeleton feature to get the coordinate value to be the angle value of the right and left hand movement in the implemation as the value of the input fuzzy sugeno algorithm, which resulted in PWM speed output value. This marker-less development communicates wirelessly using the Xbee Pro S28 received by the Arduino Mega for communication and processing the running brushless motor and the ASV generates the movement according to the value received. Effect of light on Kinect in the efficient skeleton tracking process in the room maximum 53105 lux, and in the room maximum 201 lux. Object Spacing (Brainware) to read in the tracking process of the Kinect position of the efficient at a height of 120cm, and angle 00. Kinect communication distance with ASV reaches up to 10000cm. The success of a fuzzy calculation with the result of the movement of the ASV robot is 89.9%.

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