University of Belgrade - School of Electrical Engineering, 11000 Belgrade, Serbia.
Innovation Center of the School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia.
Biosensors (Basel). 2024 Jul 28;14(8):368. doi: 10.3390/bios14080368.
This study investigates the feasibility of a novel brain-computer interface (BCI) device designed for sensory training following stroke. The BCI system administers electrotactile stimuli to the user's forearm, mirroring classical sensory training interventions. Concurrently, selective attention tasks are employed to modulate electrophysiological brain responses (somatosensory event-related potentials-sERPs), reflecting cortical excitability in related sensorimotor areas. The BCI identifies attention-induced changes in the brain's reactions to stimulation in an online manner. The study protocol assesses the feasibility of online binary classification of selective attention focus in ten subacute stroke patients. Each experimental session includes a BCI training phase for data collection and classifier training, followed by a BCI test phase to evaluate online classification of selective tactile attention based on sERP. During online classification tests, patients complete 20 repetitions of selective attention tasks with feedback on attention focus recognition. Using a single electroencephalographic channel, attention classification accuracy ranges from 70% to 100% across all patients. The significance of this novel BCI paradigm lies in its ability to quantitatively measure selective tactile attention resources throughout the therapy session, introducing a top-down approach to classical sensory training interventions based on repeated neuromuscular electrical stimulation.
本研究旨在探索一种新型脑-机接口(BCI)设备在中风后感觉训练中的可行性。该 BCI 系统通过在前臂施加电触觉刺激,模拟经典的感觉训练干预。同时,采用选择性注意任务来调节电生理脑反应(体感事件相关电位-sERPs),反映相关感觉运动区域的皮层兴奋性。BCI 以在线方式识别注意力引起的大脑对刺激反应的变化。该研究方案评估了 10 名亚急性中风患者在线分类选择性注意力焦点的可行性。每个实验阶段包括 BCI 训练阶段用于数据收集和分类器训练,然后是 BCI 测试阶段,基于 sERP 评估选择性触觉注意力的在线分类。在在线分类测试中,患者完成 20 次选择性注意任务的重复,同时提供对注意力焦点识别的反馈。使用单个脑电图通道,所有患者的注意力分类准确率在 70%到 100%之间。这种新型 BCI 范式的意义在于它能够在整个治疗过程中定量测量选择性触觉注意力资源,为基于重复神经肌肉电刺激的经典感觉训练干预引入了一种自上而下的方法。