Savić Andrej M, Novičić Marija, Ðorđević Olivera, Konstantinović Ljubica, Miler-Jerković Vera
School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.
Faculty of Medicine, University of Belgrade, Belgrade, Serbia.
Front Hum Neurosci. 2023 Mar 23;17:1096814. doi: 10.3389/fnhum.2023.1096814. eCollection 2023.
A brain computer interface (BCI) allows users to control external devices using non-invasive brain recordings, such as electroencephalography (EEG). We developed and tested a novel electrotactile BCI prototype based on somatosensory event-related potentials (sERP) as control signals, paired with a tactile attention task as a control paradigm.
A novel electrotactile BCI comprises commercial EEG device, an electrical stimulator and custom software for EEG recordings, electrical stimulation control, synchronization between devices, signal processing, feature extraction, selection, and classification. We tested a novel BCI control paradigm based on tactile attention on a sensation at a target stimulation location on the forearm. Tactile stimuli were electrical pulses delivered at two proximal locations on the user's forearm for stimulating branches of radial and median nerves, with equal probability of the target and distractor stimuli occurrence, unlike in any other ERP-based BCI design. We proposed a compact electrical stimulation electrodes configuration for delivering electrotactile stimuli (target and distractor) using 2 stimulation channels and 3 stimulation electrodes. We tested the feasibility of a single EEG channel BCI control, to determine pseudo-online BCI performance, in ten healthy subjects. For optimizing the BCI performance we compared the results for two classifiers, sERP averaging approaches, and novel dedicated feature extraction/selection methods cross-validation procedures.
We achieved a single EEG channel BCI classification accuracy in the range of 75.1 to 88.1% for all subjects. We have established an optimal combination of: single trial averaging to obtain sERP, feature extraction/selection methods and classification approach.
The obtained results demonstrate that a novel electrotactile BCI paradigm with equal probability of attended (target) and unattended (distractor) stimuli and proximal stimulation sites is feasible. This method may be used to drive restorative BCIs for sensory retraining in stroke or brain injury, or assistive BCIs for communication in severely disabled users.
脑机接口（BCI）允许用户使用非侵入性脑记录（如脑电图（EEG））来控制外部设备。我们开发并测试了一种基于体感事件相关电位（sERP）作为控制信号，并结合触觉注意力任务作为控制范式的新型电触觉BCI原型。
一种新型电触觉BCI包括商用EEG设备、电刺激器以及用于EEG记录、电刺激控制、设备间同步、信号处理、特征提取、选择和分类的定制软件。我们测试了一种基于触觉注意力的新型BCI控制范式，该范式针对前臂上目标刺激位置的感觉。触觉刺激是在用户前臂的两个近端位置施加的电脉冲，用于刺激桡神经和正中神经分支，目标刺激和干扰刺激出现的概率相等，这与其他基于ERP的BCI设计不同。我们提出了一种紧凑的电刺激电极配置，使用2个刺激通道和3个刺激电极来传递电触觉刺激（目标和干扰）。我们在10名健康受试者中测试了单通道EEG BCI控制的可行性，以确定准在线BCI性能。为了优化BCI性能，我们比较了两种分类器、sERP平均方法以及新型专用特征提取/选择方法的交叉验证程序的结果。
所有受试者的单通道EEG BCI分类准确率在75.1%至88.1%之间。我们确定了以下各项的最佳组合：用于获取sERP的单次试验平均、特征提取/选择方法和分类方法。
所获得的结果表明，一种具有相等概率的关注（目标）和非关注（干扰）刺激以及近端刺激部位的新型电触觉BCI范式是可行的。该方法可用于驱动恢复性BCI以进行中风或脑损伤后的感觉再训练，或用于严重残疾用户通信的辅助BCI。
