A Learning Scheme for EMG Based Decoding of Dexterous, In-Hand Manipulation Motions.

Electromyography (EMG) based interfaces are the most common solutions for the control of robotic, orthotic, prosthetic, assistive, and rehabilitation devices, translating myoelectric activations into meaningful actions. Over the last years, a lot of emphasis has been put into the EMG based decoding of human intention, but very few studies have been carried out focusing on the continuous decoding of human motion. In this work, we present a learning scheme for the EMG based decoding of object motions in dexterous, in-hand manipulation tasks.

On Muscle Selection for EMG Based Decoding of Dexterous, In-Hand Manipulation Motions.

The field of Brain Machine Interfaces (BMI) has attracted an increased interest due to its multiple applications in the health and entertainment domains. A BMI enables a direct interface between the brain and machines and is capable of translating neuronal information into meaningful actions (e.g.