On-implant spike sorting methods employ static feature extraction/selection techniques to minimize the hardware cost. Here we propose a novel framework for real-time spike sorting based on dynamic selection of features. We select salient features that maximize the geometric-mean of between-class distances as well as the associated homogeneity index effectively to best discriminate spikes for classification. Wave-shape classification is performed based on a multi-label window discrimination approach. An external module calculates the salient features and discrimination windows through optimizing a replica of the on-implant operation, and then configures the on-implant spike sorter for real-time online operation. Hardware implementation of the on-implant online spike sorter for 512 channels of concurrent extra-cellular neural signals is reported, with an average classification accuracy of ~88%. Compared with other similar methods, our method shows reduction in classification error by a factor of ~2, and also reduction in the required memory space by a factor of ~5.

植入物上的尖峰分类方法采用静态特征提取/选择技术，以最大程度地降低硬件成本。在这里，我们提出了一种基于特征动态选择的实时峰值分类的新颖框架。我们选择显着特征，以最大程度地提高类间距离的几何平均值以及相关的同质性指数，从而最好地区分峰值以进行分类。基于多标签窗口判别方法执行波形分类。外部模块通过优化植入物上操作的副本来计算显着特征和辨别窗口，然后配置植入物钉子分选器以进行实时在线操作。据报道，针对512个并发细胞外神经信号的植入物在线尖峰分选器的硬件实现方式，平均分类准确度约为88％。与其他类似方法相比，我们的方法显示出分类错误减少了约2倍，所需的存储空间也减少了约5倍。