scholarly journals Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain–computer interface: three-class classification of rest, right-, and left-hand motor execution

2017 ◽  
Vol 5 (01) ◽  
pp. 1 ◽  
Author(s):  
Thanawin Trakoolwilaiwan ◽  
Bahareh Behboodi ◽  
Jaeseok Lee ◽  
Kyungsoo Kim ◽  
Ji-Woong Choi
Keyword(s):  
Neural Network ◽  
Near Infrared ◽  
Brain Computer Interface ◽  
High Accuracy ◽  
Computer Interface ◽  
Motor Execution ◽  
Functional Near Infrared Spectroscopy ◽  
Left Hand ◽  
Right And Left Hand

scholarly journals Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2362 ◽  
Author(s):  
Alexander E. Hramov ◽  
Vadim Grubov ◽  
Artem Badarin ◽  
Vladimir A. Maksimenko ◽  
Alexander N. Pisarchik
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
High Efficiency ◽  
Brain Activity ◽  
Blood Oxygenation ◽  
Hemispheric Lateralization ◽  
Motor Execution ◽  
Functional Near Infrared Spectroscopy

Sensor-level human brain activity is studied during real and imaginary motor execution using functional near-infrared spectroscopy (fNIRS). Blood oxygenation and deoxygenation spatial dynamics exhibit pronounced hemispheric lateralization when performing motor tasks with the left and right hands. This fact allowed us to reveal biomarkers of hemodynamical response of the motor cortex on the motor execution, and use them for designing a sensing method for classification of the type of movement. The recognition accuracy of real movements is close to 100%, while the classification accuracy of imaginary movements is lower but quite high (at the level of 90%). The advantage of the proposed method is its ability to classify real and imaginary movements with sufficiently high efficiency without the need for recalculating parameters. The proposed system can serve as a sensor of motor activity to be used for neurorehabilitation after severe brain injuries, including traumas and strokes.

scholarly journals A Brain Computer Interface–Based P300 Speller for Home Appliances Control System

2019 ◽  
Vol 9 (1) ◽  
pp. 1002-1007
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Brain Computer Interface ◽  
High Accuracy ◽  
Computer Interface ◽  
P300 Speller ◽  
Home Appliances ◽  
Environment Control ◽  
Artificial Neural ◽  
Control Application

Environment control is one of the critical difficulties for handicapped individuals who experience the ill effects of neuromuscular ailments. Brain-computer interface systems empower a subject to communicate with a PC machine without drawing down any solid action. This communication does not depend in light of any ordinary medium of correspondences like physical movement, talking, and motion and so forth. The most vital desire for a home control application is high accuracy and solid control. In this study, row-column–based (2 Row, 3 columns) P300 paradigm for home appliances control was designed. In this article, we analyze real-time EEG data for P300 speller using support vector machine and artificial neural network for high accuracy. Using this proposed method we are able to find the target appliance in the correct and fastest way. Four paralyzed people were participating in this study. The artificial neural network gives 85% accuracy within 10 flashes. The results show this paradigm can be used to select the option of a home appliances control application for paralyzed people with users convenient and reliable.

scholarly journals Classification of the gaze fixations in the eye-brain-computer interface paradigm with a compact convolutional neural network

2018 ◽  
Vol 145 ◽  
pp. 293-299
Author(s):  
Bogdan L. Kozyrskiy ◽  
Anastasia O. Ovchinnikova ◽  
Alena D. Moskalenko ◽  
Boris M. Velichkovsky ◽  
Sergei L. Shishkin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
The Gaze ◽  
Gaze Fixations
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