Texas Patent of the Month – June 2024
Paradromics Inc. has developed a cutting-edge neural data processing system that represents a significant advancement in brain-machine interface technology. This innovative system is designed to capture and process neural signals with unprecedented accuracy and efficiency, letting us fully connect with the human brain.
At its core, the system features a sensing device capable of collecting neural signals, which are then processed by an analog circuit within the neural data processor. This circuit uses multiple filters and summing elements to transform and analyze the neural signals, extracting key features through complex operations that approximate correlation or convolution.
One of the standout features of Paradromics’ system is its multi-stage analog circuit. The first stage involves amplifying and conditioning the neural signals to ensure clarity. The second stage implements programmable filters for diverse feature extraction, while the third stage uses summing elements to generate a variety of transfer functions. This meticulous processing pipeline enables the system to decompose neural signals based on a linear basis set, which enhances the precision of the extracted data.
The processed neural signals are then transmitted to an external device via a brain-machine interface (BMI), making real-time interaction possible. This capability opens doors to numerous applications, from advanced neuroprosthetics to sophisticated brain-computer interfaces, allowing users to control external devices with their neural activity. Additionally, the system’s ability to wirelessly transmit processed neural signals ensures seamless integration with various external platforms.
By leveraging an array of programmable filters and summing circuits, Paradromics’ system offers adaptability to different neural feature sets and signal types, whether electrical or optical. This flexibility, combined with high-density neural recording capabilities, positions the system at the forefront of neurotechnology, promising enhanced performance in both research and clinical settings.
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