Efficient Biosignal Processing Using Hyperdimensional Computing: Network Templates for Combined Learning and Classification of ExG Signals
Efficient Biosignal Processing Using Hyperdimensional Computing: Network Templates for Combined Learning and Classification of ExG Signals
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DOI：
10.1109/jproc.2018.2871163
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发表时间：
2019-01-01
影响因子：
20.6
通讯作者：
Rabaey, Jan M.
中科院分区：
文献类型：
--
作者：
Rahimi, Abbas;Kanerva, Pentti;Rabaey, Jan M.
认识到大脑回路的大小，超维（HD）计算可以用HD空间中的点（即HD向量）对神经活动模式进行建模。HD计算的主要研究属性包括：一组通用的HD向量算术运算，通用性，可扩展性，可分析性，一次性学习和能源效率。这使得它成为有效的生物信号处理的主要候选者，其中信号是嘈杂的和非平稳的，训练数据集不是很大，个体差异很大，并且能量效率约束很紧。基于原生HD计算算子，我们描述了一种组合方法，用于多类学习和分类各种ExG生物信号，如肌电图（EMG），脑电图（EEG）和皮层电图（ECoG）。我们开发了一套完整的HD网络模板，将从不同电极记录的身体电位和大脑神经活动全面编码到单个HD向量中，而不需要领域专家知识或特设电极选择过程。这种编码的HD向量被处理为单个单元，用于快速一次性学习和鲁棒分类。它也可以被解释为识别最有用的特征。与最先进的同类产品相比，HD计算可以实现在线、增量和快速学习，因为它需要的训练数据不到三分之一，预处理也更少。
Recognizing the very size of the brain's circuits, hyperdimensional (HD) computing can model neural activity patterns with points in a HD space, that is, with HD vectors. Key examined properties of HD computing include: a versatile set of arithmetic operations on HD vectors, generality, scalability, analyzability, one-shot learning, and energy efficiency. These make it a prime candidate for efficient biosignal processing where signals are noisy and nonstationary, training data sets are not huge, individual variability is significant, and energy-efficiency constraints are tight. Purely based on native HD computing operators, we describe a combined method for multiclass learning and classification of various ExG biosignals such as electromyography (EMG), electroencephalography (EEG), and electrocorticography (ECoG). We develop a full set of HD network templates that comprehensively encode body potentials and brain neural activity recorded from different electrodes into a single HD vector without requiring domain expert knowledge or ad hoc electrode selection process. Such encoded HD vector is processed as a single unit for fast one-shot learning, and robust classification. It can be interpreted to identify the most useful features as well. Compared to state-of-the-art counterparts, HD computing enables online, incremental, and fast learning as it demands less than a third as much training data as well as less preprocessing.