New York Patent of the Month – June 2024
NeuroSteer, Inc., creators of a versatile brain interpretation platform, has been granted a patent for their brain activity monitoring and analysis technologies.
This innovative technology utilizes a compact, wearable apparatus to record an individual’s brain electrical activity alongside various physiological and environmental parameters. The real genius lies in its advanced processor, which employs sophisticated machine learning algorithms to project the brain’s electrical signals onto an optimal set of wavelet packet atoms.
By analyzing these projections, the system can accurately determine the mental state or neurological condition of the individual. This is achieved through a unique blend of brain state assignment and the correlation of physiological and environmental data. The result is a precise and dynamic understanding of the individual’s cognitive and neurological health.
One of the most compelling features of NeuroSteer’s invention is its feedback mechanism. The system can generate outputs in various forms—audible, visual, or physical signals—that are tailored to influence the detected brain state. For instance, in a minimally conscious subject, the system can deliver specific stimuli to elicit a response, thereby aiding in cognitive therapy and monitoring.
This real-time analysis and feedback loop are not just confined to clinical settings. The wearable nature of the apparatus makes it suitable for continuous monitoring, providing invaluable data for long-term health and wellness tracking. Whether used to monitor the effects of environmental changes on mental states or to track physiological responses during various activities, NeuroSteer’s system offers unprecedented insight.
The application of this technology extends to children, where it can be used to monitor responses to musical stimuli, and potentially improve developmental outcomes through tailored auditory feedback.
NeuroSteer’s innovative approach to brain activity monitoring combines cutting-edge machine learning with practical, real-world applications, making it a significant advancement in neurological health technology.
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