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Open Technology: The Free EEG 32 project: Tensor Flow Models and Complexity Metrics.

EasyChair Preprint no. 4669

6 pagesDate: November 30, 2020


Open Technology, for wearables with just in time manufacturing is illustrated with the Free EEG 32 project, with open source hardware and software fulfilled with Seeed Studios, and Mouser as the vendors. The product illustrates the use of 32 EEG channels for M.L based learning of 32 dimensional data streams for the classification, with deep learning of meditative states, quantified as spectral power ratios, differential spectral power ratios and identification of the various bardos, and biofeedback mechanisms. Inspired by the MUSE headset, soundscape engineering is used as feedback on meditation coherence and bird chirps indicate transcendental success factor thresholds, illustrated with the Transcendental Meditation (™) system of Maharishi Mahesh Yogi. A theoretical framework for the complexity of deep learning models, training set estimation and external validity measurements is described, with tensor decomposition for the deep learning network.

Keyphrases: 32 channels, bird chirp, chaos analysis, D-Commerce, deep learning, Drop shipping, EEG based hardware, FFT, free eeg, Free EEG 32, Gerber, just-in-time, Muse headband, on-demand, portals, Seeed studio, TM, Word Cloud

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Anil Kumar Bheemaiah},
  title = {Open Technology: The Free EEG 32 project: Tensor Flow Models and Complexity Metrics.},
  howpublished = {EasyChair Preprint no. 4669},

  year = {EasyChair, 2020}}
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