Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level
Alexander E. Hramov,
Vadim Grubov,
Artem Badarin,
Vladimir A. Maksimenko,
Alexander N. Pisarchik
Affiliations
Alexander E. Hramov
Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Universitetskaja Str., 1, 420500 Innopolis, Russia
Vadim Grubov
Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Universitetskaja Str., 1, 420500 Innopolis, Russia
Artem Badarin
Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Universitetskaja Str., 1, 420500 Innopolis, Russia
Vladimir A. Maksimenko
Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Universitetskaja Str., 1, 420500 Innopolis, Russia
Alexander N. Pisarchik
Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Universitetskaja Str., 1, 420500 Innopolis, Russia
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.
