基于沃尔什-哈达玛变换的鲁棒高效尖峰排序特征提取
Robust and Efficient Feature Extraction for Spike Sorting via the Walsh-Hadamard Transform
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中文总结 AI 辅助
该研究提出沃尔什-哈达玛变换（WHT）作为硬件高效的尖峰排序特征提取方法，相比CHT和PCA，其分类性能提升且鲁棒性更强，适用于植入式神经接口的低功耗实时处理需求。
中文摘要 AI 辅助
植入式神经接口需低功耗实时信号处理以满足严格的热和带宽约束，因此亟需用于片上尖峰排序的轻量型特征提取方法。本研究提出沃尔什-哈达玛变换（WHT）作为硬件高效的神经尖峰分类特征提取方法，WHT仅需加法器、减法器和寄存器即可实现，无需系数存储器。将WHT的性能与压缩哈达玛变换（CHT）及主成分分析（PCA）对比，结果显示：在高噪声困难数据集上，其平均F1分数从55-60%提升至70-75%；在其他所有模拟数据集上，平均F1分数从90-95%提升至95-99%。除分类性能提升外，WHT对噪声、降采样、训练规模缩减及距离度量选择的鲁棒性更强，其标准差通常低于5%，而CHT和PCA在高噪声条件下标准差可达10%。
英文摘要
Implantable neural interfaces require low-power real-time signal processing to remain within strict thermal and bandwidth constraints, motivating lightweight feature extraction methods for on-chip spike sorting. This work presents the Walsh-Hadamard Transform (WHT) as a hardware-efficient feature extraction method for neural spike classification. WHT can be implemented using only adders, subtractors, and registers without coefficient memory. WHT performance is compared against the Compressed Hadamard Transform (CHT) and Principal Component Analysis (PCA), improving mean F1-scores from 55-60% to 70-75% on difficult high-noise datasets and from 90-95% to 95-99% on all other simulated datasets. In addition to improved classification performance, WHT demonstrates greater robustness to noise, downsampling, reduced training size, and distance metric selection, maintaining standard deviations typically below 5%, while CHT and PCA reach up to 10% under high-noise conditions.
