Decoding kinematic variables from Electroencephalographic (EEG) signals during lower-limb mobility protocols

Luis Mercado


Over 15% of the population of the world has some kind of disability, and a prevalent type is associated with their lower-limbs. In order to provide this type of disabled people with a mean to restore the mobility they once had, it comes to interest the usage of brain-machine interfaces (BMI). Many BMI studies have been done using the approach of electroencephalography (EEG); however, they tend to use a “classical scheme” which consists of classifying only the movement intention of the user. When this intention is detected, the system is programmed to automatically perform realistic movements according to the user’s wishes. This is why direct decoding from the EEG signals into limb kinematics would be preferable, as it gives the possibility of characterizing the intended movement in detail. A limited number of studies have implemented these “decoding schemes”; nevertheless, they just decode a single type of movement. This work will show some of the different methods currently used for decoding, as well as their comparison and performance for different sets of types of movements.

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ISSN: 2530-7320

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