NEUROSONIX: TRANSFORMING NEURAL SIGNALS INTO SOUND THROUGH ADVANCED MACHINE LEARNING AND SIGNAL PROCESSING

Abstract

This invention introduces NeuroSonix, a system for converting brain neural signals into sound in real-time. Using EEG data, signal processing, and neural networks, NeuroSonix enables applications in communication for non-verbal individuals, cognitive monitoring, and artistic expression, providing an innovative auditory-based brain-machine interface.

Specification

Description:FIELD OF THE INVENTION
This invention relates to neurotechnology and machine learning, focusing on a system that converts brain neural signals into audible sound. Using a combination of advanced signal processing and machine learning, NeuroSonix enables real-time auditory feedback from neural activity, providing applications in communication for non-verbal individuals, cognitive monitoring, and artistic expression.
BACKGROUND OF THE INVENTION
Translating neural signals into other forms of communication is challenging due to the complexity of brain signals and the limitations of traditional brain-machine interfaces. Current technologies in neural signal processing are primarily restricted to non-auditory outputs, lacking real-time auditory feedback and precise pattern recognition. Traditional systems often require extensive data, lack customization, and have limited capacity to represent neural signals meaningfully.
NeuroSonix addresses these challenges by introducing a method that processes neural signals through machine learning algorithms and transforms them into sound. This invention offers a means of communication for individuals with speech impairments, enables researchers to study brain activity audibly, and supports cognitive monitoring in therapeutic and educational settings. Through its adaptive machine learning framework, NeuroSonix bridges a critical gap in human-machine interaction, enabling intuitive auditory feedback from neural data.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
This invention provides NeuroSonix, a system that converts neural signals into sound in real-time using EEG data and advanced signal processing. Through neural network models, the system translates complex neural patterns into distinct sound characteristics, offering applications in communication for individuals with limited motor function, cognitive monitoring, and creative expression. NeuroSonix combines EEG devices, neural processing units, and an intuitive user interface for auditory output, delivering a comprehensive neural-to-audio transformation solution.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a"," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", "third", and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The NeuroSonix system integrates hardware and software to convert neural signals into sound in real-time. EEG electrodes or implantable devices capture neural signals from the user's brain, which are then amplified and digitized through signal amplifiers and analog-to-digital converters (ADCs). The digitized signals undergo preprocessing for noise reduction, filtering, and feature extraction to ensure high-quality input data.
The signal processing unit uses digital signal processors (DSPs) and microcontrollers to extract meaningful patterns from the neural signals, which are then processed by the system's machine learning framework. The machine learning models, primarily deep neural networks, interpret the neural patterns and classify them into distinct categories, which are assigned specific sound characteristics. This classification enables NeuroSonix to produce auditory outputs that represent different neural activities in real-time.
Neural network accelerators enhance the computational efficiency of the machine learning algorithms, supported by GPUs or ASICs for complex neural data interpretation. These networks adaptively learn from user-specific neural patterns, improving personalization and accuracy over time. The processed data is converted back into analog signals by digital-to-analog converters (DACs), which are then transmitted to headphones or speakers to produce sound. This transformation allows users to audibly perceive their neural activity.
The user interface includes controls for modifying sound characteristics and receiving neurofeedback, offering real-time information on the user's cognitive state. Users interact with the system through an intuitive interface, potentially influencing sound outputs based on their preferences, and receiving additional haptic feedback.
Connectivity options include both wireless (e.g., Bluetooth, Wi-Fi) and wired protocols, enabling data transfer to external devices or cloud-based storage for additional processing or analysis. The NeuroSonix system can be powered by a battery or external source, making it suitable for both portable and stationary applications.
, Claims:1. A system for converting brain neural signals into sound, known as NeuroSonix, comprising EEG electrodes, signal amplifiers, analog-to-digital converters, and a neural processing unit.
2. The system as claimed in Claim 1, wherein neural signals are captured using EEG electrodes or implantable devices and amplified for accurate data collection.
3. The system as claimed in Claim 1, wherein a signal processing unit with digital signal processors and microcontrollers extracts relevant patterns from neural data.
4. The system as claimed in Claim 1, wherein machine learning models, including neural networks, classify neural patterns into auditory outputs based on signal characteristics.
5. The system as claimed in Claim 1, wherein digital-to-analog converters and audio output devices convert processed data into sound, allowing users to perceive neural activity audibly.
6. The system as claimed in Claim 1, wherein a user interface provides controls for sound customization and real-time neurofeedback, enhancing user engagement.
7. The system as claimed in Claim 1, wherein the connectivity options include wireless and wired protocols for data transfer to external devices or cloud storage.
8. A method for converting neural signals to sound as claimed in Claim 1, involving real-time data processing, neural classification, and audio output for perceptible feedback.
9. The system as claimed in Claim 1, wherein it includes neural network accelerators to enhance machine learning efficiency for real-time neural-to-audio conversion.
10. The system as claimed in Claim 1, wherein it provides an auditory-based brain-machine interface, facilitating communication, cognitive monitoring, and creative applications.

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