Virtual Reality-Based Lathe Skills Transfer Based on Brain Activity Assessment Using Functional Near-Infrared Spectroscopy

2011 ◽  
Author(s):  
Keiichi Watanuki ◽  
Lei Hou
Keyword(s):  
Virtual Reality ◽  
Infrared Spectroscopy ◽  
Human Resource Development ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Brain Activity ◽  
Activity Patterns ◽  
Skills Training ◽  
Haptic Information ◽  
Functional Near Infrared Spectroscopy

Here, we propose a new virtual reality-based job training program for human resource development. In our proposed system, the educational content is displayed in the immersive virtual environment with haptic information, very similar to the way a trainee may experience work in the virtual site operation. The brain functional activities of the trainee under virtual and real skills training are measured using near-infrared spectroscopy, and the characteristics of the activity patterns are analyzed in detail.

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.

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