SwitchBraidNet: Quantisation-Aware Lightweight Architecture for Hybrid Brain-Computer Interface
arXiv:2606.18816v1 Announce Type: cross Abstract: Hybrid brain-computer interfaces (BCIs) that integrate motor imagery (MI) and steady-state visual evoked potentials (SSVEP) provide high-dimensional neural decoding but typically exceed the computational limits of embedded hardware. To address this, we propose SwitchBraidNet, a compact EEG classification architecture designed for low-power deployment. The model employs a dual-path temporal braid to extract multiscale oscillatory features, an adaptive squeeze-and-excitation spatial switch for electrode gating, and a log-variance readout layer fo
The increasing sophistication of hybrid Brain-Computer Interfaces (BCIs) combined with the demand for real-time, on-device processing necessitates innovations in compact, efficient architectures.
This development addresses a critical bottleneck in BCI deployment, making advanced neural interfaces more practical for real-world, low-power applications outside of research labs.
The ability to run complex BCI algorithms on embedded hardware changes the accessibility and potential applications of neural decoding, expanding from clinical settings to everyday use cases.
- · BCI developers
- · Medical technology companies
- · Embedded hardware manufacturers
- · Patients with motor impairments
- · Companies relying on high-power BCI solutions
- · Specialized BCI research labs with proprietary, non-scalable hardware
More widespread adoption of hybrid BCIs due to reduced computational requirements.
Acceleration of research and development into novel BCI applications beyond motor imagery and SSVEP.
Ethical and societal debates around pervasive neural interfaces becoming more urgent as technology matures and becomes accessible.
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Read at arXiv cs.AI