An Accurate and Hardware-Efficient Dual Spike Detector for Implantable Neural Interfaces

Guo, Xiaorang; Shaeri, MohammadAli; Shoaran, Mahsa

doi:10.1109/biocas54905.2022.9948602

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…It is worth noting that the energy of the spikes is mostly concentrated below 3 kHz, and hence, 7 kHz is the lowest sampling frequency for accurate spike detection, in accordance with the Nyquist theorem and 1 kHz oversampling. These findings are consistent with prior research by [36,63] suggesting 7 kHz to be the minimum acceptable sampling frequency to spike detection. However, spike sorting can require a higher sampling frequency because the spike waveform is critical for clustering.…”

Section: Spike Detection Performance In Synthetic Datasetsupporting

confidence: 91%

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Firing-rate-modulated spike detection and neural decoding co-design

Zhang

Constandinou

2023

J. Neural Eng.

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Objective: Translational efforts on spike-signal-based implantable brain-machine interfaces (BMIs) are increasingly aiming to minimise bandwidth while maintaining decoding performance. Developing these BMIs requires advances in neuroscience and electronic technology, as well as using low-complexity spike detection algorithms and high-performance machine learning models. While some state-of-the-art BMI systems jointly design spike detection algorithms and machine learning models, it remains unclear how the detection performance affects decoding.Approach: We propose the co-design of the neural decoder with an ultra-low complexity spike detection algorithm. The detection algorithm is designed to attain a target firing rate, which the decoder uses to modulate the input features preserving statistical invariance over months. Main results: We demonstrate a multiplication-free fixed-point spike detection algorithm with 97% detection accuracy and the lowest complexity among studies we have seen. By co-designing the system to incorporate statistically invariant features, we observe significantly improved long-term stability, with decoding accuracy degrading by less than 10% after 80 days of operation. Our analysis also reveals a nonlinear relationship between spike detection and decoding performance. Increasing the detection sensitivity improves decoding accuracy and long-term stability, which means the activity of more neurons is beneficial despite the detection of more noise. Reducing spike detection sensitivity still provides acceptable decoding accuracy whilst reducing the bandwidth by at least 30%.Significance: Our findings regarding the relationship between spike detection and decoding performance can provide guidance on setting the threshold for spike detection rather than relying on training or trial-and-error. The trade-off between data bandwidth and decoding performance can be effectively managed using appropriate spike detection settings. We demonstrate improved decoding performance by maintaining statistical invariance of input features. We believe this approach can motivate further research focused on improving decoding performance through the manipulation of data itself (based on a hypothesis) rather than using more complex decoding models.

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Section: Spike Detection Performance In Synthetic Datasetsupporting

confidence: 91%

“…In [34], bit shifts are used to replace multiplication, improving the real-time system response time. In [36], dual thresholds derived from smoothed and non-smoothed NEO is used to guarantee high accuracy in both low and high signal-to-noise ratio (SNR) level to improve adaptiveness.…”

Section: Neural Spike Detectionmentioning

confidence: 99%

Firing-rate-modulated spike detection and neural decoding co-design

Zhang

Constandinou

2023

J. Neural Eng.

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“…In [30], bit shifts are used to replace multiplication, improving the real-time system response time. In [32], dual thresholds derived from smoothed and non-smoothed NEO is used to guarantee high accuracy in both low and high SNR level to improve adaptiveness.…”

Section: Challenges In Wireless Invasive Brain Machine Interfacesmentioning

confidence: 99%

Firing-rate-modulated spike detection and neural decoding co-design

Zhang

Constandinou

2023

Preprint

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Objective: Translational efforts on spike-signal-based implantable brain-machine interfaces (BMIs) are increasingly aiming to minimise bandwidth while maintaining decoding performance. Developing these BMIs requires advances in neuroscience and electronic technology, as well as using low-complexity spike detection algorithms and high-performance machine learning models. While some state-of-the-art BMI systems jointly design spike detection algorithms and machine learning models, it remains unclear how the detection performance affects decoding. Approach: We propose the co-design of the neural decoder with an ultra-low complexity spike detection algorithm. The detection algorithm is designed to attain a target firing rate, which the decoder uses to modulate the input features, preserving statistical invariance. Main results: We demonstrate a multiplication-free fixed-point spike detection algorithm with nearly perfect detection accuracy and the lowest complexity among studies we have seen. By co-designing the system to incorporate statistically invariant features, we observe significantly improved long-term stability, with decoding accuracy degrading by less than 10% after 80 days of operation. Our analysis also reveals a nonlinear relationship between spike detection and decoding performance. Increasing the detection sensitivity improves decoding accuracy and long-term stability, which means the activity of more neurons is beneficial despite the detection of more noise. Reducing the spike detection sensitivity still provides acceptable decoding accuracy whilst reducing the bandwidth by at least 30%. Significance: Our findings regarding the relationship between spike detection and decoding performance can provide guidance on setting the threshold for spike detection rather than relying on training or trial-and-error. The trade-off between data bandwidth and decoding performance can be effectively managed using appropriate spike detection settings. We demonstrate improved decoding performance by maintaining statistical invariance of input features. We believe this approach can motivate further research focused on improving decoding performance through the manipulation of data itself (based on a hypothesis) rather than using more complex decoding models.

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A Clockless Robust Bionic Spike Detector for Implantable Brain-Computer Interfaces

Chen,

Liu,

et al. 2023

2023 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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An Accurate and Hardware-Efficient Dual Spike Detector for Implantable Neural Interfaces

Cited by 8 publications

References 24 publications

Firing-rate-modulated spike detection and neural decoding co-design

Firing-rate-modulated spike detection and neural decoding co-design

Firing-rate-modulated spike detection and neural decoding co-design

A Clockless Robust Bionic Spike Detector for Implantable Brain-Computer Interfaces

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