Abstract
The functional use of brain-machine interfaces (BMIs) in everyday tasks requires the accurate decoding of both movement and force information. In real-word tasks such as reach-to-grasp movements, a prosthetic hand should be switched between reaching and grasping modes, depending on the detection of the user intents in the decoder part of the BMI. Therefore, it is important to detect the rest or active states of different actions in the decoder to produce the corresponding continuous command output during the estimated state. In this study, we demonstrated that the resting and force-generating time-segments in a key pressing task could be accurately detected from local field potentials (LFPs) in rat's primary motor cortex. Common spatial pattern (CSP) algorithm was applied on different spectral LFP sub-bands to maximize the difference between the two classes of force and rest. We also showed that combining a discrete state decoder with linear or non-linear continuous force variable decoders could lead to a higher force decoding performance compared with the case we use a continuous variable decoder only. Moreover, the results suggest that gamma LFP signals (50-100 Hz) could be used successfully for decoding the discrete rest/force states as well as continuous values of the force variable. The results of this study can offer substantial benefits for the implementation of a self-paced, force-related command generator in BMI experiments without the need for manual external signals to select the state of the decoder.
Highlights
Technology advances of cortical signal processing and microelectrode array fabrication can enable the practical use of intra-cortical brain-machine interface (BMI) systems, applied for the movement restoration after spinal cord injury (SCI) and stroke
It is thought that a high information transfer rate from the brain to a neural prosthesis and the long-term stability of the brain signals are supposedly the key factors toward designing real-world BMI systems [1]
In this study, we presented a method to continuously decode force information from local field potentials (LFPs) signals depending on the discrete state of behavior
Summary
Technology advances of cortical signal processing and microelectrode array fabrication can enable the practical use of intra-cortical brain-machine interface (BMI) systems, applied for the movement restoration after spinal cord injury (SCI) and stroke. The accurate neural control of a prosthetic hand, especially for reach-to-grasp movements, requires the decoding of kinetic information such as hand grasping force in addition to kinematic trajectories of the shoulder and arm. For this reason, various studies have been devoted to the possibility of decoding different types of kinetic parameters such as grasping force [21], [22], joint torque [8], and muscle activities [23], [24]. Decoding the discrete state of the action and combining it with a continuous force decoder can produce a more accurate neural command signal for the realworld BMI systems
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