Neural-Sensor Correspondence for Detecting Corrupt Sensory Perceptions
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Solution Overview
Problem
There is a need for systems and methods to detect and correct inaccuracies or distortions in neural representations of sensory stimuli, particularly in cases of sensory hallucinations associated with nervous system disorders, to alert patients or healthcare providers and potentially intervene to improve mental states.
Innovation Solution
A system comprising neural data analysis, sensing, and correspondence modules to extract neural data from implanted probes and real-world environmental data, using statistical models and machine learning to determine correspondence and generate error signals for intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external sensors are used to monitor real-world environmental stimuli, then the ability to detect inaccurate neural perceptions is improved, but the device complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: a neural data collection module that interfaces with neural tissue to capture neural signals, an external sensor module that measures real-world environmental stimuli, and a processing module that compares neural representations with actual sensory input. This segmentation allows each module to be optimized independently while working together to detect perception inaccuracies.
Solution Approach 2:
The processing module acts as an intermediary that receives data from both the neural data collection module and the external sensor module, compares the neural representation of sensory stimuli with the actual environmental measurements, and generates error signals when discrepancies are detected. This intermediary function enables the system to bridge the gap between internal neural perceptions and external reality without requiring direct interaction between the neural and sensor modules.
2Reliability
If neural interface probes are implanted to collect neural signals, then the reliability of neural data collection is improved, but the invasiveness and surgical complexity increase
Solution Approach 1:
The neural interface probe is designed to perform multiple functions: it collects neural signals from neural tissue, provides electrical stimulation capability for therapeutic intervention, and serves as a stable platform for long-term implantation. This multi-functionality reduces the need for separate devices and justifies the implantation procedure by providing comprehensive neural interface capabilities.
Solution Approach 2:
The system includes automated detection and correction capabilities that reduce the need for continuous manual intervention. The processing module automatically compares neural representations with external sensor data, detects errors, and can trigger corrective stimuli without requiring constant provider oversight, allowing the implanted system to serve itself to some extent.
3Speed
If real-time comparison between neural data and sensor data is performed, then the speed of detecting hallucinations is improved, but the computational requirements and energy consumption increase
Solution Approach 1:
The system performs continuous comparison between neural data and sensor data at key processing points to ensure rapid detection of perception errors. While this requires significant computational resources, the system is designed to operate with implanted neural interfaces that have limited power capacity, so the processing is optimized to perform essential comparisons efficiently rather than exhaustive analysis at all times.
Solution Approach 2:
The system implements a feedback loop where the comparison between neural representations and external sensor measurements continuously informs the detection process. When discrepancies are detected, the system can generate error signals and trigger corrective stimuli, creating a closed-loop system that adapts and responds in real-time. This feedback mechanism enables rapid detection and correction while allowing the system to learn and optimize its detection thresholds over time.
Data Source
AI summary
A system for monitoring neural activity of a living subject is provided. The system may comprise a correspondence module configured to be in communication with (1) a neural module and (2) one or more additional modules comprising a sensing module, another neural module, and/or a data storage module. The neural module(s) are configured to collect neural data indicative of perceptions experienced by the living subject. The sensing module may be configured to collect (1) sensor data indicative of real-world information about an environment around the living subject, and/or (2) sensor data indicative of a physical state or physiological state of the living subject. The data storage module may be configured to store prior neural data and/or prior sensor data. The correspondence module may be configured to measure a correspondence (a) between the neural data collected by the neural module(s) and the sensor data collected by the sensing module, (b) between the neural data collected by two or more neural modules, and/or (c) between the neural data collected by the neural module(s) and the prior data stored in data storage module. The measured correspondence can be used to determine a presence, absence, or extent of a potential cognitive or physiological disturbance of the living subject.