Dynamic ensemble bayesian filter for robust control of a human brain-machine interface
Jun 13, 2022·
,,,,,,,·
3 min read
Yu Qi
祝歆韵
Kedi Xu
Feixiao Ren
Hongjie Jiang
Junming Zhu
Jianmin Zhang
Gang Pan
Yueming Wang
Abstract
Objective: Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors, which have demonstrated great potential for motor restoration. One major limitation of current BMIs lies in the unstable performance due to the variability of neural signals, especially in online control, which seriously hinders the clinical availability of BMIs. Method: We propose a dynamic ensemble Bayesian filter (DyEnsemble) to deal with the neural variability in online BMI control. Unlike most existing approaches using fixed models, DyEnsemble learns a pool of models that contains diverse abilities in describing the neural functions. In each time slot, it dynamically weights and assembles the models according to the neural signals in a Bayesian framework. In this way, DyEnsemble copes with variability in signals and improves the robustness of online control. Results: Online BMI experiments with a human participant demonstrate that, compared with the velocity Kalman filter, DyEnsemble significantly improves the control accuracy (increases the success rate by 13.9% in the random target pursuit task) and robustness (performs more stably over different experiment days). Conclusion: Experimental results demonstrate the superiority of DyEnsemble in online BMI control. Significance: DyEnsemble frames a novel and flexible dynamic decoding framework for robust BMIs, beneficial to various neural decoding applications.
Type
Publication
IEEE Transactions on Biomedical Engineering
🔶 Brain-Machine Interfaces (BMIs): Challenges and a Dynamic Decoding Solution 🔶
🚀 Motivation: Restoring Mobility with BMIs
- BMIs have shown great promise for restoring mobility in paralyzed individuals (e.g., spinal cord injury).
- Intracortical microelectrodes in motor cortex (MC) can record neural activity, which is translated into control signals for:
- 🖱️ Computer cursors
- 🤖 Robotic limbs
💡 Advances in Motor BMIs
🔄 Real-Time Brain Control of Movement
- Reaching, grasping, limb motion, and multi-DOF arm control
- Real-time BMI systems have become increasingly sophisticated
🧮 Importance of Decoding Methods
- Decode neural signals → behavioral parameters → device control
- Linear decoders (classical and popular):
- Population Vector Algorithm
- Wiener Filter
- Optimal Linear Estimation (OLE)
- Kalman Filter
- Nonlinear decoders:
- Capture complex signal-behavior relationships
- Often based on artificial neural networks
- Show superior offline performance
⚠️ Key Concern: Offline improvements ≠ Online performance
🧠 Due to user adaptation and neural dynamics, linear decoders still dominate online BMI control
⚠️ Key Challenge: Neural Variability
📉 Source of Performance Instability
- Trial-to-trial neural variability (even for same task)
- Real-time feedback in closed-loop systems:
- User adapts control strategy → alters neural tuning
- Feedback properties (e.g., cursor speed) influence encoding
🧪 Effects
- Misinterpretation of intent
- Degradation in BMI control performance
🧰 Current Mitigation Strategies
- Dimensionality reduction (e.g., PCA, FA)
- Record more neurons to smooth variability
- Use larger datasets & complex decoders
🚫 Variability is still an unsolved issue in robust online BMI decoding
🆕 Proposed Solution: DyEnsemble — A Dynamic Ensemble Bayesian Filter
🎯 Objective:
Robust BMI decoding under neural variability
🔧 Key Idea:
Learn a pool of diverse models and dynamically assemble them online to adapt to signal changes.
⚙️ DyEnsemble Framework
✅ Core Innovations:
- Dynamic weighting & assembly of models (Bayesian model averaging)
- Diverse model pool covering functional variability:
- Linear models
- Polynomial models
- Neural networks
- Sequential estimation using particle filtering:
- Estimate kinematics and model ensemble weights in time
🧠 Dynamic Adaptation:
- For each time slot, adaptively assign weights to models based on:
- Likelihood of current neural observation
- Dynamically select the best-fit combination of models
📊 Experimental Validation
👤 Human Closed-Loop BMI Experiments
Task: Random target pursuit
Comparison baseline: Velocity Kalman Filter (vKF)
🚀 Results:
| Metric | DyEnsemble vs vKF |
|---|---|
| ✅ Success rate | +13.9% |
| 📅 Across experiment days | More stable |
| ⚙️ Adaptive model weights | Track neural changes |
🔍 Key Insight: DyEnsemble tracks signal variation and dynamically adjusts decoding strategy → Higher control accuracy & robustness
📌 Conclusion
- Neural variability remains a major bottleneck in practical BMI systems
- DyEnsemble:
- Combines Bayesian filtering + model averaging in a unified framework
- Offers robust and adaptive decoding
- Demonstrates superior performance in human experiments
🎯 A promising step toward clinically viable and stable BMI control
