Wearable BCI Signal Decoding for 5G SMS Transmission
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Solution Overview
Problem
Existing communication technologies do not effectively utilize brain-computer interface (BCI) systems for transmitting communications via telecommunications networks, particularly for SMS transmission using 5G new radio (NR) technology and embedded subscriber identity modules (eSIMs, lacking the precision and reliability needed for efficient data interpretation and transmission.
Innovation Solution
A wearable BCI device with deep learning capabilities integrates an electrode to measure neurological activity, a processing system, and a communication interface to transmit encoded data via a 5G NR network using an eSIM, leveraging deep learning models to enhance signal interpretation and pattern recognition for accurate SMS transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are integrated into the BCI device for signal interpretation, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The system divides the BCI device into distinct functional modules: electrode array for signal acquisition, preprocessing module for initial filtering, deep learning model database for pattern recognition, and communication interface for data transmission. This segmentation allows each component to be optimized independently while maintaining overall system precision.
Solution Approach 2:
A preprocessing module serves as an intermediary between the electrode and the deep learning model, performing initial signal filtering and feature extraction. This intermediary layer reduces the complexity burden on the deep learning model while maintaining measurement precision through staged processing.
2Speed
If 5G NR technology with eSIM is used for communication, then transmission speed and reliability are improved, but device complexity and energy consumption increase
Solution Approach 1:
The system uses 5G NR technology with eSIM for communication, implementing partial 5G functionality that balances transmission speed requirements with energy consumption constraints. The eSIM enables seamless network switching to optimize energy usage while maintaining high-speed transmission when needed.
3Measurement precision
If multiple processing components are added to enhance signal processing capabilities, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The processing system is segmented into specialized components: electrode arrays for specific neurological detection, preprocessing modules for signal conditioning, and deep learning models for pattern recognition. This segmentation improves measurement precision while managing complexity through modular design.
Solution Approach 2:
The deep learning models are designed to perform multiple functions including pattern recognition, intent determination, and signal classification across different neurological activities. This multi-functionality reduces the need for separate specialized components, managing complexity while maintaining precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides precise and reliable communication by converting brain electrical signals into SMS messages using 5G NR technology, ensuring minimal latency and high accuracy in data interpretation and transmission.
Implementation Method 1
an electrode to measure neurological activity of a user wearing the BCI device, where the electrode may be in contact with the user
Implementation Method 2
The processing system may be configured to apply a filter to the electrical signal to generate a preprocessed electrical signal
Implementation Method 3
the machine learning model to extract a pattern from the preprocessed electrical signal and determine, based on the pattern, an intent of the electrical signal
Implementation Method 4
transmit, via the communication interface, the encoded data via the 5G NR telecommunications network to a recipient user equipment
Data Source
AI summary
Systems and methods to implement a wearable brain computer interface (BCI) device for transmitting communications via a telecommunications network. One system includes a processing system of a wearable BCI device that is configured to receive an electrical signal related to neurological activity. The processing system may be configured to provide the electrical signal to a machine learning model, the machine learning model to extract a pattern from the electrical signal and determine, based on the pattern, an intent of the electrical signal. The processing system may be configured to receive, from the machine learning model, intent data related to the intent of the electrical signal. The processing system may be configured to encode the intent data to generate encoded data compatible for transmission via a 5G NR network. The processing system may be configured to transmit the encoded data via the 5G NR network to a recipient user equipment.