Aghagolzadeh Mehdi, Zhang Fei, Oweiss Karim
Department of Electrical and Computer Engineering at Michigan State University, East Lansing, MI 48824, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:1569-72. doi: 10.1109/IEMBS.2010.5626691.
Brain Machine Interface (BMI) systems demand real-time spike sorting to instantaneously decode the spike trains of simultaneously recorded cortical neurons. Real-time spike sorting, however, requires extensive computational power that is not feasible to implement in implantable BMI architectures, thereby requiring transmission of high-bandwidth raw neural data to an external computer. In this work, we describe a miniaturized, low power, programmable hardware module capable of performing this task within the resource constraints of an implantable chip. The module computes a sparse representation of the spike waveforms followed by "smart" thresholding. This cascade restricts the sparse representation to a subset of projections that preserve the discriminative features of neuron-specific spike waveforms. In addition, it further reduces telemetry bandwidth making it feasible to wirelessly transmit only the important biological information to the outside world, thereby improving the efficiency, practicality and viability of BMI systems in clinical applications.
脑机接口（BMI）系统需要实时进行尖峰分类，以便即时解码同时记录的皮质神经元的尖峰序列。然而，实时尖峰分类需要大量的计算能力，这在可植入的BMI架构中是无法实现的，因此需要将高带宽的原始神经数据传输到外部计算机。在这项工作中，我们描述了一种小型化、低功耗、可编程的硬件模块，该模块能够在可植入芯片的资源限制内执行此任务。该模块计算尖峰波形的稀疏表示，然后进行“智能”阈值处理。这种级联将稀疏表示限制在保留神经元特定尖峰波形判别特征的投影子集上。此外，它进一步降低了遥测带宽，使得仅将重要的生物信息无线传输到外部世界成为可能，从而提高了BMI系统在临床应用中的效率、实用性和可行性。
