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.

翻译：脑机接口正在被探索用于多种治疗应用。通常，这涉及通过诸如电皮层图（ECoG）或脑电图（EEG）等技术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变异性，这些信号的分析具有挑战性，并且需要具有大量计算资源的离线处理。在本文中，我们提出了一种简单而高效的基于机器学习的方法，用于手势分类的例子问题，基于脑信号。我们使用混合机器学习方法，使用卷积尖峰神经网络，采用生物启发式事件驱动突触可塑性规则，用于无监督地学习编码在尖峰域中的测量模拟信号的特征。我们证明了这种方法泛化到具有EEG和ECoG数据的不同主体，并实现了在识别不同的手势类和运动想象任务方面92.74-97.07％的优越准确性。