JONES CARLA

Greetings. I am Jones Carla, a neuroengineering researcher specializing in error-resilient neural decoding algorithms for non-invasive brain-computer interfaces (BCIs). With a Ph.D. in Computational Neuroscience (Caltech, 2023) and postdoctoral training at the MIT NeuroX Institute, I have pioneered adaptive compensation frameworks that reduce intent misclassification by 63–82% across diverse user populations.

Core Innovation: NeuroAdapt-EC Framework

1. Problem Identification
Traditional BCIs suffer from critical limitations:

• Signal Instability: EEG amplitude fluctuations (±40%) due to motion artifacts or skin-electrode impedance changes.

• Contextual Ambiguity: Overlapping neural patterns for distinct intents (e.g., "move left" vs. "select menu item").

• Individual Variability: Decoder performance drops 25–50% when applied cross-subject.

2. Technical Architecture
My three-stage compensation system integrates:

• Pre-Decoding Phase:

  • Dynamic noise suppression via Kalman Filter-Transformer hybrids (patent pending).

  • Real-time impedance monitoring using capacitive coupling tomography.

• Intent Decoding Phase:

  • Adversarial Autoencoders to disentangle task-relevant vs. nuisance neural features.

  • Uncertainty-Aware LSTM with adaptive attention gates (published in Nature Neuroengineering, 2024).

• Post-Decoding Phase:

  • Closed-loop calibration via error-related potential (ErrP) feedback.

  • Multi-modal fusion with electrodermal activity (EDA) and eye-tracking data.

3. Validation and Impact

• Achieved 94.3% decoding accuracy (vs. 72.1% baseline) in a 100-subject ALS clinical trial.

• Reduced retraining frequency from weekly to quarterly for locked-in syndrome patients.

• Enabled 22% faster typing speeds (17.3 → 21.2 char/min) in P300 speller systems.

Ethical and Practical Breakthroughs

• NeuroBias Audit Toolkit:
  Quantified and mitigated demographic performance gaps (e.g., ±15% accuracy disparity between age groups).

• Edge-Cloud Hybrid Deployment:
  Designed FPGA-accelerated inference (<8 ms latency) for real-world wheelchair control.

• Open-Source Contributions:
  Released BCI-Compensate, a PyTorch library with 50+ error compensation modules (3,800+ GitHub stars).

Future Directions

1. Cross-Modal Generalization:
   Extend compensation principles to fNIRS-EEG fusion systems (ongoing DARPA-funded project).

2. Consciousness-Aware Decoding:
   Integrate global neuronal workspace theory to distinguish intentional vs. spontaneous signals.

3. Quantum-Enhanced Calibration:
   Collaborate with IBM Quantum to develop variational quantum classifiers for error pattern recognition.

Collaborative Vision
I seek partners to translate these innovations into:

• Clinical Solutions: Adaptive BCIs for post-stroke rehabilitation.

• Human-AI Symbiosis: Error-tolerant neural interfaces for space exploration crews (NASA Artemis program).

• Ethical AI Governance: Standardized BCI certification protocols.