Benchmarking Neural Decoders

Benchmarking Neural Decoders: Benchmarking of Hardware-efficient Real-time Neural Decoding in Brain-computer Interfaces

Hueber, Paul (TU Delft Electrical Engineering, Mathematics and Computer Science)

Tömen, N. (mentor)
Micheli, A. (graduation committee)
van Gemert, J.C. (graduation committee)
Marroquim, Ricardo (graduation committee)

Delft University of Technology

Electrical Engineering

2024-01-29

Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge owing to the constrained operational environment, which requires low latency and high energy efficacy. Previous benchmarks have provided limited insights into power consumption and latency. However, this study introduces algorithmic metrics that capture the potential and limitations of neural decoders for closed-loop intra-cortical brain-computer interfaces in the context of energy and hardware constraints. This study benchmarks common decoding methods for predicting a primate’s finger kinematics from the motor cortex and explores their suitability for low latency and high energy efficient neural decoding. The study found that ANN-based decoders provide superior decoding accuracy, requiring high latency and many operations to effectively decode neural signals. Spiking neural networks have emerged as a solution, bridging this gap by achieving competitive decoding performance within sub-10ms while utilizing a fraction of computational resources. These distinctive advantages of neuromorphic spiking neural networks make them highly suitable for the challenging closed-loop neural modulation environment. Their capacity to balance decoding accuracy and operational efficiency offers immense potential in reshaping the landscape of neural decoders, fostering greater understanding, and opening new frontiers in closed-loop intra-cortical human-machine interaction.

neural decoding
brain computer interfaces
closed-loop neuromodulation
spiking neural networks
neuromorphic computing

http://resolver.tudelft.nl/uuid:6d06a1d8-56b8-4234-8d02-4192721539af

2025-12-07

http://doi.org/10.36227/techrxiv.24523291

Student theses

master thesis

© 2024 Paul Hueber