A Convolutional Spiking Network for Gesture Recognition in Brain-Computer Interfaces
A Convolutional Spiking Network for Gesture Recognition in Brain-Computer Interfaces
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DOI：
10.1109/aicas57966.2023.10168627
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Yimin Ai;B. Rajendran
中科院分区：
文献类型：
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作者：
Yimin Ai;B. Rajendran
脑机接口正在探索各种各样的治疗应用。通常，这涉及通过诸如皮层电图（ECoG）或脑电图（EEG）等技术来测量和分析连续时间的脑电活动，以驱动外部设备。然而，由于测量中的固有噪声和可变性，这些信号的分析是具有挑战性的，并且需要具有大量计算资源的离线处理。在本文中，我们提出了一个简单而有效的基于机器学习的方法的示例性问题的手势分类的基础上的大脑信号。我们使用一种混合机器学习方法，该方法使用卷积尖峰神经网络，该神经网络采用生物启发的事件驱动的突触可塑性规则，用于在尖峰域中编码的测量模拟信号的无监督特征学习。我们证明，这种方法推广到不同的主题与EEG和ECoG数据，并实现上级的准确性在92.74-97.07%的范围内，在识别不同的手势类和运动想象任务。
Brain-computer interfaces are being explored for a wide variety of therapeutic applications. Typically, this involves measuring and analyzing continuous-time electrical brain activity via techniques such as electrocorticogram (ECoG) or electroencephalography (EEG) to drive external devices. However, due to the inherent noise and variability in the measurements, the analysis of these signals is challenging and requires offline processing with significant computational resources. In this paper, we propose a simple yet efficient machine learning-based approach for the exemplary problem of hand gesture classification based on brain signals. We use a hybrid machine learning approach that uses a convolutional spiking neural network employing a bio-inspired event-driven synaptic plasticity rule for unsupervised feature learning of the measured analog signals encoded in the spike domain. We demonstrate that this approach generalizes to different subjects with both EEG and ECoG data and achieves superior accuracy in the range of 92.74-97.07% in identifying different hand gesture classes and motor imagery tasks.