Dragas Jelena, Jäckel David, Franke Felix, Hierlemann Andreas
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:2535-8. doi: 10.1109/EMBC.2013.6610056.
Emerging multi-electrode-based brain-machine interfaces (BMIs) and large multi-electrode arrays used in in vitro experiments, enable recording of single neuron's activity on multiple electrodes and allow for an in-depth investigation of neural preparations, even at a sub-cellular level. However, the use of these devices entails stringent area and power consumption constraints for the signal-processing hardware units. In addition, the high autonomy of these units and an ability to automatically adapt to changes in the recorded neural preparations is required. Implementing spike detection in close proximity to recording electrodes offers the advantage of reducing the transmission data bandwidth. By eliminating the need of transmitting the full, redundant recordings of neural activity and by transmitting only the spike waveforms or spike times, significant power savings can be achieved in the majority of cases. Here, we present a low-complexity, unsupervised, adaptable, real-time spike-detection method targeting multi-electrode recording devices and compare this method to other spike-detection methods with regard to complexity and performance.
新兴的基于多电极的脑机接口（BMI）以及用于体外实验的大型多电极阵列，能够在多个电极上记录单个神经元的活动，并允许对神经制剂进行深入研究，甚至在亚细胞水平上也是如此。然而，使用这些设备对信号处理硬件单元带来了严格的面积和功耗限制。此外，这些单元需要高度自主性以及能够自动适应所记录神经制剂变化的能力。在靠近记录电极的位置实施尖峰检测具有减少传输数据带宽的优势。通过消除传输完整、冗余的神经活动记录的需求，仅传输尖峰波形或尖峰时间，在大多数情况下可以实现显著的功耗节省。在此，我们提出一种针对多电极记录设备的低复杂度、无监督、适应性强的实时尖峰检测方法，并在复杂度和性能方面将该方法与其他尖峰检测方法进行比较。
