Real-time in-sensor spike sorting is a forefront requirement in the development of brainmachine interfaces (BMIs). This work presents the characterization, design, and efficient implementation on a field-programmable gate array (FPGA) of a novel approach to neural spike sorting intended for implantable devices based on convolutional neural networks (CNNs). While the temporal features, the shape of the spike signals, could be highly mitigated from the ambient noise, the proposed classifier effectively extracts spatial features from the multi-channel neural signal to maintain high accuracy on the noisy data. The proposed classifier mechanism was tested on real data that is recorded from multi-channel electrodes, containing 27 neural units, and the classifier achieves 93.1% accuracy despite high temporal noise in the signal. For hardware synthesis, the CNN weights are quantized to reduce the model storage requirement by 93% compared to its floating point-precision version, and the model achieves an accuracy of 86.1%.

中文翻译：

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基于卷积神经网络的多通道多类实时神经尖峰排序

实时传感器内尖峰排序是脑机接口 (BMI) 开发的最前沿要求。这项工作介绍了一种用于基于卷积神经网络 (CNN) 的可植入设备的神经尖峰排序新方法的现场可编程门阵列 (FPGA) 的表征、设计和有效实现。虽然时间特征，即尖峰信号的形状，可以从环境噪声中得到高度缓解，但所提出的分类器有效地从多通道神经信号中提取空间特征，以保持对噪声数据的高精度。所提出的分类器机制在多通道电极记录的真实数据上进行了测试，包含 27 个神经单元，尽管信号中存在高时间噪声，分类器仍实现了 93.1% 的准确度。对于硬件合成，