使用深度学习解码EEG和LFP信号:航向trunorth

E. Nurse, B. Mashford, Antonio Jimeno-Yepes, Isabell Kiral-Kornek, S. Harrer, D. Freestone
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引用次数: 87

摘要

深度学习技术特别适合分析脑电图(EEG)和局部场电位(LFP)等神经生理信号,并有望超越传统的基于机器学习的分类和特征提取算法。此外,新的认知计算平台,如IBM最近推出的神经形态TrueNorth芯片,允许在超低功耗环境中以最小的设备占用空间部署深度学习技术。将深度学习和TrueNorth技术结合起来,在感应点实时分析大脑活动数据,将在神经仿生学和人工智能的交汇处创造下一代可穿戴设备。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decoding EEG and LFP signals using deep learning: heading TrueNorth
Deep learning technology is uniquely suited to analyse neurophysiological signals such as the electroencephalogram (EEG) and local field potentials (LFP) and promises to outperform traditional machine-learning based classification and feature extraction algorithms. Furthermore, novel cognitive computing platforms such as IBM's recently introduced neuromorphic TrueNorth chip allow for deploying deep learning techniques in an ultra-low power environment with a minimum device footprint. Merging deep learning and TrueNorth technologies for real-time analysis of brain-activity data at the point of sensing will create the next generation of wearables at the intersection of neurobionics and artificial intelligence.
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