University of Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000 Lille, France.
J Neural Eng. 2021 Nov 2;18(5). doi: 10.1088/1741-2552/ac2fc4.
A brain-computer interface (BCI) aims to derive commands from the user's brain activity in order to relay them to an external device. To do so, it can either detect a spontaneous change in the mental state, in the so-called 'active' BCIs, or a transient or sustained change in the brain response to an external stimulation, in 'reactive' BCIs. In the latter, external stimuli are perceived by the user through a sensory channel, usually sight or hearing. When the stimulation is sustained and periodical, the brain response reaches an oscillatory steady-state that can be detected rather easily. We focus our attention on electroencephalography-based BCIs (EEG-based BCI) in which a periodical signal, either mechanical or electrical, stimulates the user skin. This type of stimulus elicits a steady-state response of the somatosensory system that can be detected in the recorded EEG. The oscillatory and phase-locked voltage component characterising this response is called a steady-state somatosensory-evoked potential (SSSEP). It has been shown that the amplitude of the SSSEP is modulated by specific mental tasks, for instance when the user focuses their attention or not to the somatosensory stimulation, allowing the translation of this variation into a command. Actually, SSSEP-based BCIs may benefit from straightforward analysis techniques of EEG signals, like reactive BCIs, while allowing self-paced interaction, like active BCIs. In this paper, we present a survey of scientific literature related to EEG-based BCI exploiting SSSEP. Firstly, we endeavour to describe the main characteristics of SSSEPs and the calibration techniques that allow the tuning of stimulation in order to maximise their amplitude. Secondly, we present the signal processing and data classification algorithms implemented by authors in order to elaborate commands in their SSSEP-based BCIs, as well as the classification performance that they evaluated on user experiments.
脑-机接口（BCI）旨在从用户的大脑活动中提取命令，以便将其传达给外部设备。为此，它可以检测到精神状态的自发变化，在所谓的“主动”BCI 中，或者检测到大脑对外界刺激的反应的瞬态或持续变化，在“反应”BCI 中。在后一种情况下，用户通过感觉通道感知外部刺激，通常是视觉或听觉。当刺激持续且周期性时，大脑反应达到可以轻松检测到的振荡稳态。我们将注意力集中在基于脑电图的 BCI（基于 EEG 的 BCI）上，其中周期性信号，无论是机械的还是电的，刺激用户的皮肤。这种类型的刺激会引起体感系统的稳态反应，在记录的 EEG 中可以检测到。这种反应的特征是振荡和相位锁定电压分量称为稳态体感诱发电位（SSSEP）。已经表明，SSSEP 的幅度可以通过特定的心理任务进行调制，例如当用户专注于或不专注于体感刺激时，允许将这种变化转换为命令。实际上，SSSEP 基于 BCI 可能受益于 EEG 信号的简单分析技术，例如反应性 BCI，同时允许自我调节的交互，例如主动性 BCI。在本文中，我们对利用 SSSEP 的基于 EEG 的 BCI 的科学文献进行了调查。首先，我们努力描述 SSSEPs 的主要特征和校准技术，这些技术允许调整刺激以最大化其幅度。其次，我们介绍了作者为了在他们的 SSSEP 基于 BCI 中制定命令以及他们在用户实验中评估的分类性能而实施的信号处理和数据分类算法。
