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 (APECS)
We have designed a novel computational system for discovering elemental or
atomic EEG sources in each brain hemisphere, which are defined by
uniqueness and stability in the EEG power spectrum and coherence or phase
correlation. The design of APECS was
motivated by application of the parallel factor analyses (PARAFAC) to
specify unique EEG sources and use them to explain experimental effects of fatigue or mental workload manipulations on EEG rhythms. A recent project sponsored by the US Army Research Office
(2008-2009) validated the utility of this system for detecting transient
changes in mental workload. The project also developed an advanced
neurocognitive model, known as CE2, which includes spectrally unique alpha
sub-bands and spatiotemporally unique theta sources operating separately in
each hemisphere. Tests of the APECS system using EEG signals derived from a
precise head model showed that the methods can isolate and track atomic EEG
sources in response to simulated task conditions (Trejo, Rosipal &
Nunez (2010). Advanced Physiological Estimation of Cognitive Status. 27th
Army Science Conference, Orlando,
FL.,Presentation RP-09, available on-line at http://www.armyscienceconference.com/manuscripts/R/RP-009.pdf).
We developed an algorithm for real-time classification of mental states
such as for the detection of engagement, mental workload, and mental
fatigue in pilots or vehicle operators. The algorithm uses kernel
partial-least squares (KPLS) to decompose multi-sensor EEG spectra into a
small set of components. In this way, the algorithm can process practically
unlimited input channels and spectral resolutions. No a priori information
about the spatial or spectral distributions of the sources is required. We
tested the algorithm with EEG data from Air Force pilots who performed
simulated flight maneuvers over a 37-hour period. Over a range of six
distinct signal-to-noise ratios ranging from -10 dB to +10 dB, classifier
performance increased smoothly, with test proportions correct ranging from
84% to 94% (Wallerius,
J., Trejo, L. J, Matthew, R., Rosipal,R., and Caldwell, J. A., 2005).
Wireless telephones are increasingly popular, but with that popularity
there are increased problems. There are not enough radio frequencies for
everyone to have a clear connection to the base station. So when many users
are on the network at the same time, their calls can interfere with each
other. When this happens, the quality of a call may be reduced, or even
worse, the call be may lost. To reduce the annoying problem of interference
and poor call quality, we are working the RadioCosm Inc., to develop
mathematical models for wireless telephone networks. We are also developing
powerful algorithms to tune these models, so that existing networks can
maximize the quality of service they provide to the customer. Once the
model is tuned, the parameters of the model can be applied to an existing
network. Thus, without installing any new hardware, an existing network can
handle increased call volumes while retaining a high level of call quality.
Severely disabled patients with diseases such as amyotrophic lateral
sclerosis (ALS) may still be conscious and aware of their surroundings, but
they may be unable to speak or even move a muscle. To help such patients
communicate, we worked with the Bio-Logics and the University of Illinois
on a brain-computer interface project for the severely disabled. Brain
electrical signals for interface control are very weak and noisy. We
improved the quality of the signal measurement and interpretation analyzed
the signals generated by developing a custom wavelet preprocessor, which
greatly improved the quality of the brain signals and the overall
performance of the interface. A paper containing our method was published
in the IEEE transactions on Rehabilitation Engineering: Donchin, E.,
Spencer, K. M. , and Wijesinghe, R. (2000). The Mental Prosthesis:
Assessing The Speed Of A P300-Based Brain-Computer Interface, IEEE
Transactions on Rehabilitation Engineering, 8, 174-179.
Awarded
five US Patents on algorithm development work in the field of
communications signal processing. All of the patents relate to an algorithm
for extracting confidence metric information from maximum likelihood
sequence estimation (MLSE) equalizer and combining confidence metric
information from multiple base-stations to improve signal quality. This
procedure dramatically improves system quality by minimizing the negative
effect of shadow fading and multipath fading. Invented, developed and
simulated the basic algorithms, and implemented them in real time DSP software
in a fielded system.
Initiated
and led a project which culminated in the deployment of an advanced
acoustic echo cancellation system as part of a high-end commercial video
conferencing system. The adaptive acoustic echo canceller is capable of
fast convergence on echoes with tails as long as a quarter second. A filter
bank is used to separate the signal into multiple sub-bands, which are each
adapted independently. This approach minimizes computational load and makes
fast convergence possible.
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