Year: 2025
Type: conference-paper
Abstract: This work presents an efficient decoding pipeline for neuromorphic implantable brain-machine interfaces (Neu-iBMI), leveraging sparse neural event data from an event-based neural sensing scheme. We introduce a tunable event filter (EvFilter), which also functions as a spike detector (EvFilter-SPD), significantly reducing the number of events processed for decoding by 192× and 554×, respectively. The proposed pipeline achieves high decoding performance, up to R2= 0.73, with ANN- and SNN-based decoders, eliminating the need for signal recovery, spike detection, or sorting, commonly performed in conventional iBMI systems. The SNN-Decoder reduces computations and memory required by 5 − 23× compared to NN-, and LSTM-Decoders, while the ST-NN-Decoder delivers similar performance to an LSTM-Decoder requiring 2.5× fewer resources. This streamlined approach significantly reduces computational and memory demands, making it ideal for low-power, on-implant, or wearable iBMIs.
Authors: Vivek Mohan, Biyan Zhou, Zhou Wang, Anil Bharath, Emmanuel Drakakis, Arindam Basu
Institutions: Imperial College London, City University of Hong Kong
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Funder: National Research Foundation
OpenAlex ID: W4411725225