Technion-Israel Institute of Technology, Haifa 32000, Israel.
J Neural Eng. 2012 Oct;9(5):054001. doi: 10.1088/1741-2560/9/5/054001. Epub 2012 Sep 6.
Brain-machine interfaces (BMIs) rely on decoding neuronal activity from a large number of electrodes. The implantation procedures, however, do not guarantee that all recorded units encode task-relevant information: selection of task-relevant neurons is critical to performance but is typically performed based on heuristics. Here, we describe an algorithm for decoding/classification of volitional actions from multiple spike trains, which automatically selects the relevant neurons. The method is based on sparse decomposition of the high-dimensional neuronal feature space, projecting it onto a low-dimensional space of codes serving as unique class labels. The new method is tested against a range of existing methods using simulations and recordings of the activity of 1592 neurons in 23 neurosurgical patients who performed motor or speech tasks. The parameter estimation algorithm is orders of magnitude faster than existing methods and achieves significantly higher accuracies for both simulations and human data, rendering sparse decoding highly attractive for BMIs.
脑机接口（BMI）依赖于从大量电极解码神经元活动。然而，植入程序并不能保证所有记录的单元都编码与任务相关的信息：选择与任务相关的神经元对于性能至关重要，但通常是基于启发式方法进行的。在这里，我们描述了一种从多个尖峰列车解码/分类自主动作的算法，该算法自动选择相关神经元。该方法基于高维神经元特征空间的稀疏分解，将其投影到作为唯一类别标签的低维代码空间上。使用模拟和 23 名接受过运动或言语任务的神经外科患者的 1592 个神经元的活动记录，对新方法与一系列现有方法进行了测试。参数估计算法比现有方法快几个数量级，并且在模拟和人体数据方面都实现了显著更高的准确性，这使得稀疏解码对于 BMI 非常有吸引力。
