Technology
Overview
The SynapSense technology stack is designed to translate raw neural signals into reproducible, interpretable pain-related metrics under controlled experimental conditions. The system integrates research-grade EEG acquisition, structured experimental labeling, signal processing, and machine learning within a framework that prioritizes data quality, interpretability, and scientific validation over premature clinical deployment.
Rather than treating pain as a static outcome, the system is explicitly designed to capture dynamic neural changes across baseline, pain induction, and recovery phases. This temporal framing informs every layer of the technology.
System Architecture
At a high level, the SynapSense system consists of five tightly coupled components:
Neural Data Acquisition
Experimental Task Synchronization
Signal Preprocessing & QC
Feature Extraction
ML-Based Analysis
Each component is designed to be modular, auditable, and adaptable as research findings evolve.
Neural Data Acquisition
EEG Hardware Configuration
SynapSense uses a research-grade, wet-electrode EEG system configured with 32 channels arranged according to the international 10-20 system. Wet electrodes are selected to maximize signal fidelity and reduce impedance-related noise during extended recordings.
Key Characteristics
- High temporal resolution for millisecond-scale neural dynamics
- Continuous impedance monitoring (target < 15 kΩ)
- Stable electrode placement for sensorimotor coverage
Primary Electrode Sites
Sites of interest include C3, Cz, and C4, aligned with the primary somatosensory cortex and motor-adjacent regions implicated in pain processing.
Additional channels support artifact detection, spatial context, and connectivity analysis.
Recording Environment
All recordings are conducted in a controlled laboratory environment to minimize confounding noise sources. Participants are seated comfortably, instructed to minimize movement, and continuously monitored by trained research personnel throughout data collection.
Experimental Synchronization and Labeling
Pain Induction Tasks
To generate time-aligned neural and behavioral data, SynapSense employs standardized, controlled pain induction paradigms, including cold pressor exposure, blood pressure cuff occlusion, and transcutaneous electrical stimulation.
These tasks are not intended to replicate clinical pain, but to induce controlled, transient pain states that can be precisely aligned with EEG signals.
Continuous Self-Report Labeling
A central design feature is high-frequency labeling. Participants provide Visual Analog Scale (VAS) ratings at regular intervals during pain tasks, generating a time series of subjective pain intensity rather than a single summary score.
Alignment of EEG features with moment-to-moment pain fluctuations
Evaluation of temporal lag or lead relationships
More granular training and evaluation of models
Signal Preprocessing and Quality Control
Artifact Management
Raw EEG signals are susceptible to artifacts from eye movements, muscle activity, motion, and environmental noise. SynapSense employs a multi-stage preprocessing pipeline:
- Band-pass filtering to remove slow drifts and high-frequency noise
- Line-noise suppression
- Automated and manual artifact detection
- Channel-level quality assessment
Baseline Normalization
Baseline EEG recordings collected prior to pain induction establish individualized reference states. Subsequent pain and recovery data are analyzed relative to these baselines, enabling within-subject comparisons that reduce inter-individual variability.
Feature Extraction and Representation
Signals are decomposed into canonical frequency bands (theta, alpha, beta, gamma). Features such as power, relative power, and temporal modulation are computed over sliding windows to capture dynamic changes.
Measures of signal complexity and entropy capture changes in neural variability and organization that may accompany pain states and transitions.
Functional connectivity features (coherence, phase-based coupling) characterize interactions between cortical regions and network-level reorganization.
Machine Learning Framework
Modeling Philosophy
Machine learning within SynapSense is used as a scientific tool, not a black box. Models are designed to test hypotheses about neural-pain relationships rather than to maximize predictive performance in isolation. Interpretability, generalizability, and resistance to overfitting are prioritized.
Model Types
- Linear and kernel-based classifiers
- Tree-based ensemble methods
- Neural network architectures for temporal pattern recognition
Training and Evaluation
- Balanced accuracy to account for class imbalance
- ROC analysis
- Correlation between predictions and reported intensity
- Cross-validation and participant-level partitioning
Output Representation
The output is a continuous, quantitative pain-related index derived from neural features, intended for research interpretation and hypothesis testing, not for diagnosis or clinical decision-making. Output values are contextualized within each participant's baseline and task structure.
Data Security and Ethical Design
All data are de-identified at the point of storage and labeled only with numerical subject identifiers. Identifiable information is stored separately under restricted access. Research data are encrypted and stored on institution-approved secure systems. Access is limited to trained research personnel, and all procedures operate under institutional ethical oversight.
Design Boundaries and Limitations
SynapSense explicitly acknowledges the limitations of EEG-based approaches, including susceptibility to noise, limited spatial resolution, and inter-individual variability. The technology is designed to surface these limitations transparently rather than obscure them.
The system does not claim to infer emotional states, diagnose conditions, or operate autonomously. All interpretations are constrained by experimental context and validated against self-report.
Technology Trajectory
The current technology represents a research-stage system optimized for feasibility and validation. Future iterations may explore:
Improved signal robustness
Reduced setup complexity
Longitudinal monitoring capabilities
Translation into applied research or clinical feasibility studies
Any such transitions will be driven by data and validation outcomes, not assumptions.