Biomedical Engineering Reference
In-Depth Information
ing large-scale brain activity and emerging developments in microchip design, signal processing
algorithms, sensors, and robotics are coalescing into a new technology—
neurotechnology
—devoted
to creating BMIs for controls [
33
].
From an optimal signal processing viewpoint, BMI modeling is the identification of a MIMO
time varying, eventually nonlinear system, which is a challenging task because of several factors: the
intrinsic subsampling of the neural activity due to the relative small number of microelectrodes versus
the number of neurons in the motor cortex, the unknown aspects of where the information resides
(neural coding), the huge dimensionality of the problem, and the need for real-time signal processing
algorithms. The problem is further complicated by a need for good generalization in nonstationary
environments that depends on model topologies, fitting criteria, and training algorithms. Finally, re-
construction accuracy must be assessed, because it is linked to the choice of linear versus nonlinear and
feedforward versus feedback models. In the next chapters, we will dissect the BMI design problem from
a signal processing perspective where the choices in the decoding architecture will be evaluated in terms
of performance and feasibility. The goal is to review and critique the past and state of the art in BMI
design for guiding future development.
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