scholarly journals Mobile robot control based on noninvasive brain-computer interface using hierarchical classifier of imagined motor commands

2018 ◽  
Vol 161 ◽  
pp. 03003 ◽  
Author(s):  
Filipp Gundelakh ◽  
Lev Stankevich ◽  
Konstantin Sonkin
Keyword(s):  
Mobile Robot ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Support Vector ◽  
Non Invasive ◽  
Vector Machines ◽  
Artefact Detection ◽  
Supervisor Control ◽  
Mobile Robot Control ◽  
Hierarchical Classifier

The study describes approaches of direct and supervisor control of a mobile robot based on a non-invasive brain-computer interface. An interface performs electroencephalographic signal decoding, which includes several steps: filtering, artefact detection, feature extraction, and classification. In this study, a classifier with hierarchical structure was developed and applied. Description of a committee of classifiers based on neural networks and support vector machines is given. The developed classifier demonstrated accuracy 50 ± 5% of single trial decoding of four classes of imaginary fine movements. Prospects of using non-invasive brain-computer interface for control of mobile robots was described. Key applications of the system are maintenance of immobilized patients and rehabilitation procedures both in clinic and at home.

Brain Computer Interface Using Motor Imagery and Facial Expressions to Control a Mobile Robot

2018 ◽  
Author(s):  
James Kuffuor ◽  
Biswanath Samanta
Keyword(s):  
Facial Expression ◽  
Mobile Robot ◽  
Facial Expressions ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Hybrid Approach ◽  
Computer Interface ◽  
Support Vector ◽  
Correct Identification ◽  
Characteristic Features

A study is presented on brain computer interface (BCI) using motor imagery (MI) and facial expressions to control a mobile robot. Traditionally, only MI signals are used in BCI applications. In this paper a hybrid approach of using both MI and facial expression stimulations for BCI is proposed. Electroencephalography (EEG) signals were acquired using a sensor system and processed for several MI and facial expressions to extract characteristic features. The features were used to train support vector machine (SVM) based classifiers and the trained classifiers were used to recognize test signals for correct identification of MI and facial expressions. A system was developed to implement the BCI using MI and facial expressions to control a mobile robot. Results of training using MI and facial expressions, individually and together are presented for comparison. The combined features from MI and facial expression stimulations were found to give performance similar to facial expressions but better than MI only.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

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