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A Proof-of-Concept Spike Based Neuromorphic Brain-Computer Interface.

E B Dijkema, C M A Pennartz, U Olcese

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
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    Summary

    This study demonstrates a neuromorphic brain-computer interface (BCI) capable of processing neural spike events in near real-time. This technology shows promise for developing advanced closed-loop BCIs for neurological repair.

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    Area of Science:

    • Neuroscience
    • Computer Engineering
    • Biomedical Engineering

    Background:

    • Closed-loop brain-computer interfaces (BCIs) offer potential for functional restoration after neurological damage.
    • High spatiotemporal precision is crucial for clinically effective BCI systems.
    • Current BCIs often require extensive preprocessing of neural signals.

    Purpose of the Study:

    • To demonstrate a proof-of-concept neuromorphic BCI system.
    • To process neural spike events in near real-time using spiking neural networks (SNNs) on neuromorphic hardware.
    • To evaluate the system's latency and processing capabilities for closed-loop applications.

    Main Methods:

    • Developed a system to acquire neural signals and stream spike events to an SNN on SpiNNaker hardware.
    • Utilized in vivo recordings from mouse visual cortex and simulated neural waveforms for evaluation.
    • Measured roundtrip latency from spike detection to SNN output spike.

    Main Results:

    • Achieved a mean roundtrip latency of 4.69 ms (±1.70 ms) without hidden SNN layers.
    • Each additional hidden layer increased latency by approximately 3.65 ms.
    • Successfully processed neural spikes in near real-time, suitable for closed-loop interventions.

    Conclusions:

    • Neuromorphic SNNs can rapidly process neural signals, forming a basis for closed-loop BCIs.
    • This approach can potentially bypass damaged neural pathways.
    • Future work includes implementing stimulation protocols for enhanced neuroprosthetic devices.