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：脑机接口正被探索用于多种治疗应用。通常，这涉及通过电皮层图（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，由于测量中固有的噪声和变异性，这些信号的分析具有挑战性，并且需要借助大量计算资源进行离线处理。在本文中，我们提出了一种基于机器学习的高效方法，用于解决基于脑信号的手势分类这一典型问题。我们采用一种混合机器学习方法，该方法使用卷积脉冲神经网络，并采用受生物启发的基于事件的突触可塑性规则进行无监督特征学习，以处理经脉冲域编码的测量模拟信号。我们证明，该方法可推广至不同受试者的EEG和ECoG数据，并在识别不同手势类别与运动想象任务中取得92.74%-97.07%的卓越准确率。