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

EasyChair Preprint 4669

Abstract

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

@booklet{EasyChair:4669,
  author    = {Anil Kumar Bheemaiah},
  title     = {Open Technology: The Free EEG 32 project: Tensor Flow Models and Complexity Metrics.},
  howpublished = {EasyChair Preprint 4669},
  year      = {EasyChair, 2020}}