NEUROFUSE: FEATURE FUSION WITH SOFT-RESET SPIKING NETWORKS FOR EFFICIENT BCI
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Accurate decoding of brain signals is vital for intra-cortical brain–computer interfaces (iBCIs). Traditional methods relying on hand-crafted neural activity features often lack accuracy, while deep learning approaches require high computational resources. In this paper (2025), we propose a spiking neural network (SNN) framework combined with a feature fusion strategy to achieve both efficiency and high decoding performance. Our model integrates manually extracted neural activity vector features with deep representations, enabling improved classification of motor-related signals. Experiments on rhesus macaque datasets show that the proposed method outperforms artificial neural network baselines in accuracy while being tens to hundreds of times more energy efficient, making it well-suited for real-world iBCIs applications.
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SURUTHEKA.S, (2026). Neurofuse: Feature Fusion with Soft-Reset Spiking Networks for Efficient BCI. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.366
SURUTHEKA.S, . "Neurofuse: Feature Fusion with Soft-Reset Spiking Networks for Efficient BCI." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.366.
SURUTHEKA.S, . "Neurofuse: Feature Fusion with Soft-Reset Spiking Networks for Efficient BCI." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.366.
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