Abstract
The utility of the EEG and ECoG depends on finding methods of data recording, measurement, and analysis that provide reliable and robust neural correlates of ongoing cognitive behaviors (Basar 1998). The accessible temporal and spatial resolutions are in ms to s and mm to cm. The channel information capacity in the physiological bandwidth of one or a few cortical signals is too narrow to afford correlates for much more than binary-state changes such as wake-sleep, eyes open-shut, start-stop, and right-left and to type letters at an information rate of about 1 bit/s (Gao et al. 2003). The spatial domain in high-density array recording offers far greater channel capacity, provided the macroscopic neural formatting of images can be understood.
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Notes
- 1.
Two examples of markers are described in other chapters. One uses recurring minima in spatial variance of phase (Fig. 6.5, Sect. 6.4.2; Fig. 10.6, Sect. 10.5), and the other uses the recurring maxima in burst power (Fig. 6.5c, Sect. 6.4.2; Fig. 8.3a, Sect. 8.2; Fig. 9.2, Sect. 9.2; Fig. 10.4, Sect. 10.4).
- 2.
Empirical mode decomposition (Huang et al. 1998) has also been proposed for ECoG and EEG decomposition owing to its high spectral resolution. However, the spectral decomposition is not appropriate for tracking frequency modulation in ECoGs.
- 3.
Atmanspacher and Scheingraber (1990) described the concept of the ratio of the rate of energy dissipation to the rate of order formation as a “fundamental extension of Shannonian information” (pp. 731–732). Its use as a measure of the knowledge created from information by cortex as it forms a textured burst is described in Sect. 11.5.
- 4.
The stability of the 1/f background noise set by the nonlinear feedback gain of interactions among excitatory neurons (Fig. 6.10, Sect. 6.6) is governed by a non-convergent attractor (Principe et al. 2001) sometimes called a strange or chaotic attractor (Skarda and Freeman 1987). The representation is by use of stochastic differential equations (Kozma and Freeman 2001). In a piecewise linear model, the random fluctuations are removed by averaging, and modeling is by ordinary differential equations. The non-convergent attractor is replaced by a point attractor.
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Freeman, W.J., Quiroga, R.Q. (2013). Neocortical ECoG Images Formed by Learning. In: Imaging Brain Function With EEG. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4984-3_9
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