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Technology and signal processing for brain-machine
interfaces
(paper)
Jie Fu
https://sites.google.com/site/bigaidream/
3/7/2013 8:16 PM
KEY: signal processing for neural signal
Neurotechnology is the enabling mechanism through which function
of the nervous system can
be restored or enhanced using bioengineering principles.
It is believed that the neocortex is a scale-free system, with
highly unstable transient dynamics
and order parameters that bring the system back to lower energy
states. How a neuron
contributes to macroscopic cognitive events in space-time that
produce behavior is still today
largely unknown.
Grand challenges of BMIs
Multiscale signal processing and modeling
[KEY] The challenge in the development of new signal processing
approaches for BMI data
analysis is learning how to handle large multi-input
multi-output systems with signalrepresentations that span
continuous and discrete time. The best performing BMIs require
in
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principle sufficient knowledge of the spatio-temporal
representation created by assemblies of
neurons. Therefore, a critical path for advancement involves the
integration of improved signal
processing methods that capture global computation on multiple
spatial and temporal scales.
{deep architecture would be a good choice}
Control feature extraction in model-based BMIs
[KEY] The success of BMIs relies heavily upon the ability to
first extract control features from
neural activity related to goal-directed behavior.
The neural representation of control commands has been analyzed
from three perspectives:
spectral analysis (continuous brain oscillations), firing rate
coding (semi-continuous
neuromodulation), and spike timing (point process spike events),
as shown in Figure 1.
[KEY] The rate coding BMI experimental paradigm lends itself
very nicely to statistical signalprocessing methodologies used to
derive optimal models from data.
It is possible to translate the decoding problem into a system
identification framework, where a
parametric linear or nonlinear system can be trained directly
from the data to achieve outputs
with small error compared to the true hand positions.
Spike timing in neural decoding
Quantification of temporal structure of spike trains for BMIs is
an important operation in the
processing and analysis of any point process and, in particular,
of neural spike trains [Multipleneural spike train data analysis:
State-of-the-art and future challenges]. In contrast to the
rate-coding hypothesis, is there any advantage in decoding
accuracy when using spike
timing-based representations?
[KEY] The fundamental problem in BMI signal processing is how to
find more effective ways to
work directly with spikes for modeling.
A new statistical framework to directly quantify the time
structure of multichannel spike trains to
quantify entropy over a time segment using the concepts recently
introduced in information
theoretic learning (ITL). These approaches can define a
continuous time stochastic function to
quantify the similarity between spike trains, which is called
the instantaneous cross information
potential (ICIP).
[LIMITATIONs] Despite these advances, all the probabilistic
approaches require preknowledge of
the neuron receptive properties. Moreover, there is potential
advantage in terms of accuracy but
the algorithms become much more computationally complex because
they must be updated on
the time scale of the spike times (1 ms or less).
Summary
It is clear that the next generation of BMI technologies cannot
be built solely from existing
