Sound is a vibration and as all sounds form a sine wave hence conscious thought vibrates as sound.
Sound is a type of energy made by vibrations. Where the size of the vibration (also called amplitude) determines the volume. The amplitude of the vibration carries the energy. Sound modulation is the process of encoding information in a transmitted signal, while demodulation is the process of extracting information from the transmitted signal.
All sounds in nature are fundamentally constructed of sine waves. More complex sounds simply contain more oscillations at different frequencies, stacked one upon another. Higher-frequency, oscillations which are tonally related to the fundamental frequency (the base note or tone) are known as harmonics. Therefore as conscious thought is a vibration hence it makes a sound and forms a sine wave that people mistake for tinnitus.
When acoustic resonance happens to the listener and audience then they experience positive emotions as the encoded signals data volume amplitude harmonizes frequency with the end user that decodes input and conversely mechanical resonance occurs to data servers and electric generators who experience negative emotions as output of energy or data is transfered from one terminal to the other and for example New Zealand is the perfect electric network where there is a balanced dipole energy dynamic between the North Island and the South Island.
For example AI brain-to-text use statistical signal processing and machine learning whose results prove the brain encodes a repertoire of phonetic representations that can be decoded continuously during internal speech. Whereby fourier analysis is a mathematical method of analysing a complex periodic waveform to find its constituent frequencies (as sine waves). Hence the fourier transform is a mathematical operation that decomposes a signal into its constituent frequency components and it converts a signal from the time domain to the frequency domain.
Therefore computation powered by wireless energy supply enables the AI neural network identification of association rule based phonetic weights linked to that specific countries neural network domain that is fundamental to national sovereignty hence the real phonetic alphabet being classified as national security is the encryption that thereby disables foreign intelligence reconnaissance, hacking and cyberwarfare due to the cipher being the cryptography that enforces modern demands of anti-enumeration.
Research has found that EMF sensors have recorded wireless brain electromagnetic activity at distances over two feet away. Testing was conducted to investigate the effect of brainwave EMF from a distance. Testing distances occurred as far back as 63 cm. This study demonstrated the utilization possibility of generated brainwave EMF functional information used by AI to decode the brains wireless EEG signals into real time data.
Smart wireless neural decoding is a neuroscience field concerned with the reconstruction of wireless brainwave signals. Brainwave reconstruction refers to the ability of deep learning AI to sense, process and broadcast software-defined radio signals that interface with the human brains neural network and these methods produce results of 79% accuracy.
Software research has found Python AI packages that enables sound wireless interface with, Software-Defined Radio, Brain Decode, Brainwaves API, Brain Signal Decoding, Brain Decoder Toolbox, Interactive Scientific Visualization, all together with installed TensorFlow AI powers next level signal intelligence.
First install Software-Defined Radio (SDR) that is a radio communication system where components that conventionally have been implemented in analog hardware (e.g. mixers, filters, amplifiers, detectors, modulators/demodulators, etc.) are instead implemented by means of software on a computer or embedded system that enables wireless brain computer interface (BCI)
Also install Brain Decode for decoding raw electrophysiological brain data with deep learning models. It includes dataset fetchers, data preprocessing and visualization tools, as well as implementations of several deep learning architectures and data augmentations for analysis of EEG, ECoG and MEG.
https://pypi.org/project/braindecode/
Then install Neurodecode a real-time brain signal decoding framework that, acquires signals, signal analyzer, visualizes signals in real time, spectrum analyzer, records signals, decoder and trainer modules, neural network-based autoencoders, stimulus triggers, various utilities, and these features bridge the intelligence between man and machine.
https://pypi.org/project/neurodecode/
Next install Neurosity brainwaves API to manipulate and analyze, brainwaves raw, brainwaves power spectral density, brainwaves power by band, sampling of brainwave hertz, brainwave signal filters, brainwave analog to digital converter, Fast Fourier transform, signal quality metrics, for real data intelligence.
https://pypi.org/project/neurosity/
Now install Brain decoder toolbox that's equipped with, target variables, various data formats, computation utilities, deep learning, DNN features, open data utilities, reconstruction methods, statistical models, and resonate with the AI digital revolution.
https://pypi.org/project/bdpy/
I.T. Development, Alec Bellamy