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Firing-rate-modulated spike detection and neural decoding co-design
Zheng Zhang1, Timothy G Constandinou1,2,3
1Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.
Journal of Neural Engineering
|April 20, 2023
Summary
We developed a low-complexity spike detection algorithm for brain-machine interfaces (BMIs) that maintains high decoding accuracy and long-term stability. Optimizing spike detection balances bandwidth and performance for better BMI systems.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Implantable brain-machine interfaces (BMIs) aim to minimize bandwidth while preserving decoding performance.
- Developing advanced BMIs requires integrating neuroscience, electronics, low-complexity spike detection, and high-performance machine learning.
- The impact of spike detection performance on decoding accuracy in co-designed BMI systems is not fully understood.
Purpose of the Study:
- To co-design an ultra-low complexity spike detection algorithm with a neural decoder for implantable brain-machine interfaces.
- To investigate the relationship between spike detection performance and decoding accuracy over extended periods.
- To optimize the trade-off between data bandwidth and decoding performance in BMIs.
Main Methods:
- Developed a multiplication-free, fixed-point spike detection algorithm.
- Designed the neural decoder to modulate input features, preserving statistical invariance over months.
- Evaluated detection accuracy on synthetic datasets across varying noise levels.
- Assessed long-term decoding stability over 80 days.
Main Results:
- Achieved 97% average spike detection accuracy with minimal hardware complexity.
- Demonstrated improved long-term stability, with decoding accuracy degrading by less than 10% after 80 days.
- Revealed a nonlinear relationship between spike detection sensitivity and decoding performance, showing benefits from detecting more neural activity.
- Showcased a 30% bandwidth reduction by adjusting spike detection sensitivity while maintaining acceptable decoding accuracy.
Conclusions:
- Spike detection performance significantly influences BMI decoding accuracy and long-term stability.
- Findings provide guidance for setting spike detection thresholds, optimizing the bandwidth-performance trade-off.
- Maintaining statistical invariance of input features enhances decoding performance.
- This approach encourages data manipulation over complex decoding models for BMI improvement.
