Motor Imagery-based Brain-Computer Interface: Neural Network Approach


Дәйексөз келтіру

Толық мәтін

Ашық рұқсат Ашық рұқсат
Рұқсат жабық Рұқсат берілді
Рұқсат жабық Тек жазылушылар үшін

Аннотация

A neural network approach has been developed for detecting EEG patterns accompanying the implementation of motor imagery, which are mental equivalents of real movements. The method is based on Local Approximation of Spectral Power using Radial Basis Functions (LASP-RBF) and the original algorithm for interpreting the time sequence of neural network responses. An asynchronous neural interface has been created, the basic element of which is a committee of three neural networks providing the classification of target EEG patterns accompanying the execution of motor imagery by the upper and lower limbs. A comparative evaluation of the classification efficiency of EEG patterns of mental equivalents of real movements was carried out using the developed classifier and traditional classification methods in particular, Random Forest, Linear Discriminant Analysis and Linear Regression methods. It was shown that the classification accuracy using the developed approach is higher (up to 90%) than other classifiers.

Авторлар туралы

D. Lazurenko

Research Technology Center of Neurotechnology, South Federal University

Хат алмасуға жауапты Автор.
Email: dmlazurenko@sfedu.ru
Ресей, Rostov-on-Don, 344006

V. Kiroy

Research Technology Center of Neurotechnology, South Federal University

Email: dmlazurenko@sfedu.ru
Ресей, Rostov-on-Don, 344006

I. Shepelev

Research Technology Center of Neurotechnology, South Federal University

Email: dmlazurenko@sfedu.ru
Ресей, Rostov-on-Don, 344006

L. Podladchikova

Research Technology Center of Neurotechnology, South Federal University

Email: dmlazurenko@sfedu.ru
Ресей, Rostov-on-Don, 344006

Қосымша файлдар

Қосымша файлдар
Әрекет
1. JATS XML

© Allerton Press, Inc., 2019