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• W628275840 abstract "A new information measure, drawing on the 1-D Cluster Variation Method (CVM), describes local pattern distributions (nearest-neighbor and next-nearest neighbor) in a binary 1-D vector in terms of a single interaction enthalpy parameter <i>h</i> for the specific case where the fractions of elements in each of two states are the same (<i>x₁=x₂=0.5</i>). An example application of this method would be for EEG interpretation in Brain-Computer Interfaces (BCIs), especially in the frontier of invariant biometrics based on distinctive and invariant individual responses to stimuli containing an image of a person with whom there is a strong affiliative response (e.g., to a person’s grandmother). This measure is obtained by mapping EEG observed configuration variables (<i>z₁, z₂, z₃</i> for next-nearest neighbor triplets) to <i>h</i> using the analytic function giving <i>h</i> in terms of these variables at equilibrium. This mapping results in a small phase space region of resulting <i>h</i> values, which characterizes local pattern distributions in the source data. The 1-D vector with equal fractions of units in each of the two states can be obtained using the method for transforming natural images into a binarized equi-probability ensemble (Saremi & Sejnowski, 2014; Stephens et al., 2013). An intrinsically 2-D data configuration can be mapped to 1-D using the 1-D Peano-Hilbert space-filling curve, which has demonstrated a 20 dB lower baseline using the method compared with other approaches (cf. SPIE ICA etc. by Hsu & Szu, 2014). This CVM-based method has multiple potential applications; one near-term one is optimizing classification of the EEG signals from a COTS 1-D BCI baseball hat. This can result in a convenient 3-D lab-tethered EEG, configured in a 1-D CVM equiprobable binary vector, and potentially useful for Smartphone wireless display. Longer-range applications include interpreting neural assembly activations via high-density implanted soft, cellular-scale electrodes." @default.
• W628275840 created "2016-06-24" @default.
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• W628275840 date "2015-06-02" @default.
• W628275840 modified "2023-09-23" @default.
• W628275840 title "A new EEG measure using the 1D cluster variation method" @default.
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• W628275840 doi "https://doi.org/10.1117/12.2184651" @default.
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