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Proceedings Paper

Brain order disorder 2nd group report of f-EEG
Author(s): Francois Lalonde; Nitin Gogtay; Jay Giedd; Nadarajen Vydelingum; David Brown; Binh Q. Tran; Charles Hsu; Ming-Kai Hsu; Jae Cha; Jeffrey Jenkins; Lien Ma; Jefferson Willey; Jerry Wu; Kenneth Oh; Joseph Landa; C. T. Lin; T. P. Jung; Scott Makeig; Carlo Francesco Morabito; Qyu Moon; Takeshi Yamakawa; Soo-Young Lee; Jong-Hwan Lee; Harold H. Szu; Balvinder Kaur; Kenneth Byrd; Karen Dang; Alan Krzywicki; Babajide O. Familoni; Louis Larson; Susan Harkrider; Keith A. Krapels; Liyi Dai
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Paper Abstract

Since the Brain Order Disorder (BOD) group reported on a high density Electroencephalogram (EEG) to capture the neuronal information using EEG to wirelessly interface with a Smartphone [1,2], a larger BOD group has been assembled, including the Obama BRAIN program, CUA Brain Computer Interface Lab and the UCSD Swartz Computational Neuroscience Center. We can implement the pair-electrodes correlation functions in order to operate in a real time daily environment, which is of the computation complexity of O(N3) for N=102~3 known as functional f-EEG. The daily monitoring requires two areas of focus. Area #(1) to quantify the neuronal information flow under arbitrary daily stimuli-response sources. Approach to #1: (i) We have asserted that the sources contained in the EEG signals may be discovered by an unsupervised learning neural network called blind sources separation (BSS) of independent entropy components, based on the irreversible Boltzmann cellular thermodynamics(ΔS < 0), where the entropy is a degree of uniformity. What is the entropy? Loosely speaking, sand on the beach is more uniform at a higher entropy value than the rocks composing a mountain – the internal binding energy tells the paleontologists the existence of information. To a politician, landside voting results has only the winning information but more entropy, while a non-uniform voting distribution record has more information. For the human’s effortless brain at constant temperature, we can solve the minimum of Helmholtz free energy (H = E − TS) by computing BSS, and then their pairwise-entropy source correlation function. (i) Although the entropy itself is not the information per se, but the concurrence of the entropy sources is the information flow as a functional-EEG, sketched in this 2nd BOD report. Area #(2) applying EEG bio-feedback will improve collective decision making (TBD). Approach to #2: We introduce a novel performance quality metrics, in terms of the throughput rate of faster (Δt) & more accurate (ΔA) decision making, which applies to individual, as well as team brain dynamics. Following Nobel Laureate Daniel Kahnmen’s novel “Thinking fast and slow”, through the brainwave biofeedback we can first identify an individual’s “anchored cognitive bias sources”. This is done in order to remove the biases by means of individually tailored pre-processing. Then the training effectiveness can be maximized by the collective product Δt * ΔA. For Area #1, we compute a spatiotemporally windowed EEG in vitro average using adaptive time-window sampling. The sampling rate depends on the type of neuronal responses, which is what we seek. The averaged traditional EEG measurements and are further improved by BSS decomposition into finer stimulus-response source mixing matrix [A] having finer & faster spatial grids with rapid temporal updates. Then, the functional EEG is the second order co-variance matrix defined as the electrode-pair fluctuation correlation function C(s~, s~’) of independent thermodynamic source components. (1) We define a 1-D Space filling curve as a spiral curve without origin. This pattern is historically known as the Peano-Hilbert arc length a. By taking the most significant bits of the Cartesian product a≡ O(x * y * z), it represents the arc length in the numerical size with values that map the 3-D neighborhood proximity into a 1-D neighborhood arc length representation. (2) 1-D Fourier coefficients spectrum have no spurious high frequency contents, which typically arise in lexicographical (zig-zag scanning) discontinuity [Hsu & Szu, “Peano-Hilbert curve,” SPIE 2014]. A simple Fourier spectrum histogram fits nicely with the Compressive Sensing CRDT Mathematics. (3) Stationary power spectral density is a reasonable approximation of EEG responses in striate layers in resonance feedback loops capable of producing a 100, 000 neuronal collective Impulse Response Function (IRF). The striate brain layer architecture represents an ensemble <IRF< e.g. at V1-V4 of Brodmann areas 17-19 of the Cortex, i.e. stationary Wiener-Kintchine-Einstein Theorem. Goal#1: functional-EEG: After taking the 1-D space-filling curve, we compute the ensemble averaged 1-D Power Spectral Density (PSD) and then make use of the inverse FFT to generate f-EEG. (ii) Goal#2 individual wellness baseline (IWB): We need novel change detection, so we derive the ubiquitous fat-tail distributions for healthy brains PSD in outdoor environments (Signal=310°C; Noise=27°C: SNR=310/300; 300°K=(1/40)eV). The departure from IWB might imply stress, fever, a sports injury, an unexpected fall, or numerous midnight excursions which may signal an onset of dementia in Home Alone Senior (HAS), discovered by telemedicine care-giver networks. Aging global villagers need mental healthcare devices that are affordable, harmless, administrable (AHA) and user-friendly, situated in a clothing article such as a baseball hat and able to interface with pervasive Smartphones in daily environment.

Paper Details

Date Published: 24 June 2014
PDF: 22 pages
Proc. SPIE 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII, 91180J (24 June 2014); doi: 10.1117/12.2051706
Show Author Affiliations
Francois Lalonde, National Institutes of Health (United States)
Nitin Gogtay, National Institutes of Health (United States)
Jay Giedd, National Institutes of Health (United States)
Nadarajen Vydelingum, National Institutes of Health (United States)
David Brown, U.S. Food and Drug Administration (United States)
Binh Q. Tran, The Catholic Univ. of America (United States)
Charles Hsu, The George Washington Univ. (United States)
Ming-Kai Hsu, The George Washington Univ. (United States)
Jae Cha, Virginia Polytechnic Institute and State Univ. (United States)
Jeffrey Jenkins, The Catholic Univ. of America (United States)
Lien Ma, The George Washington Univ. (United States)
Jefferson Willey, The George Washington Univ. (United States)
Jerry Wu, The George Washington Univ. (United States)
Kenneth Oh, The George Washington Univ. (United States)
Joseph Landa, BriarTek, Inc. (United States)
C. T. Lin, Univ. of California, San Diego (United States)
T. P. Jung, Univ. of California, San Diego (United States)
Scott Makeig, Univ. of California, San Diego (United States)
Carlo Francesco Morabito, Univ. Mediterranea di Reggio Calabria (Italy)
Qyu Moon, Hanyang Univ. (Korea, Republic of)
Takeshi Yamakawa, Fuzzy Logic Systems Institute (Japan)
Soo-Young Lee, KAIST (Korea, Republic of)
Jong-Hwan Lee, Korea Univ. (Korea, Republic of)
Harold H. Szu, The Catholic Univ. of America (United States)
Balvinder Kaur, George Mason Univ. (United States)
Kenneth Byrd, Harvard Univ. (United States)
Karen Dang, Univ. of Virginia (United States)
Alan Krzywicki, The Catholic Univ. of America (United States)
Babajide O. Familoni, The Catholic Univ. of America (United States)
Louis Larson, The Catholic Univ. of America (United States)
Susan Harkrider, The Catholic Univ. of America (United States)
Keith A. Krapels, The Catholic Univ. of America (United States)
Liyi Dai, Harvard Univ. (United States)


Published in SPIE Proceedings Vol. 9118:
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII
Harold H. Szu; Liyi Dai, Editor(s)

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