Faller Josef, Scherer Reinhold, Costa Ursula, Opisso Eloy, Medina Josep, Müller-Putz Gernot R
Institute for Knowledge Discovery, Graz University of Technology, Graz, Austria.
Guttmann Institute, Neurorehab. University inst. affil. with the UAB, Barcelona, Spain.
PLoS One. 2014 Jul 11;9(7):e101168. doi: 10.1371/journal.pone.0101168. eCollection 2014.
Co-adaptive training paradigms for event-related desynchronization (ERD) based brain-computer interfaces (BCI) have proven effective for healthy users. As of yet, it is not clear whether co-adaptive training paradigms can also benefit users with severe motor impairment. The primary goal of our paper was to evaluate a novel cue-guided, co-adaptive BCI training paradigm with severely impaired volunteers. The co-adaptive BCI supports a non-control state, which is an important step toward intuitive, self-paced control. A secondary aim was to have the same participants operate a specifically designed self-paced BCI training paradigm based on the auto-calibrated classifier. The co-adaptive BCI analyzed the electroencephalogram from three bipolar derivations (C3, Cz, and C4) online, while the 22 end users alternately performed right hand movement imagery (MI), left hand MI and relax with eyes open (non-control state). After less than five minutes, the BCI auto-calibrated and proceeded to provide visual feedback for the MI task that could be classified better against the non-control state. The BCI continued to regularly recalibrate. In every calibration step, the system performed trial-based outlier rejection and trained a linear discriminant analysis classifier based on one auto-selected logarithmic band-power feature. In 24 minutes of training, the co-adaptive BCI worked significantly (p = 0.01) better than chance for 18 of 22 end users. The self-paced BCI training paradigm worked significantly (p = 0.01) better than chance in 11 of 20 end users. The presented co-adaptive BCI complements existing approaches in that it supports a non-control state, requires very little setup time, requires no BCI expert and works online based on only two electrodes. The preliminary results from the self-paced BCI paradigm compare favorably to previous studies and the collected data will allow to further improve self-paced BCI systems for disabled users.
基于事件相关去同步化（ERD）的脑机接口（BCI）的协同自适应训练范式已被证明对健康用户有效。目前尚不清楚协同自适应训练范式是否也能使严重运动障碍用户受益。我们论文的主要目标是评估一种针对严重受损志愿者的新型线索引导的协同自适应BCI训练范式。这种协同自适应BCI支持非控制状态，这是迈向直观、自定节奏控制的重要一步。次要目标是让相同的参与者操作基于自动校准分类器的专门设计的自定节奏BCI训练范式。协同自适应BCI在线分析来自三个双极导联（C3、Cz和C4）的脑电图，而22名最终用户交替进行右手运动想象（MI）、左手MI和睁眼放松（非控制状态）。不到五分钟后，BCI自动校准，并开始为MI任务提供视觉反馈，该任务相对于非控制状态可以得到更好的分类。BCI继续定期重新校准。在每个校准步骤中，系统进行基于试验的异常值剔除，并基于一个自动选择的对数带功率特征训练线性判别分析分类器。在24分钟的训练中，对于22名最终用户中的18名，协同自适应BCI的表现显著优于随机水平（p = 0.01）。对于20名最终用户中的11名，自定节奏BCI训练范式的表现显著优于随机水平（p = 0.01）。所提出的协同自适应BCI补充了现有方法，因为它支持非控制状态，所需设置时间极少，不需要BCI专家，并且仅基于两个电极在线工作。自定节奏BCI范式的初步结果与先前的研究相比具有优势，所收集的数据将有助于进一步改进针对残疾用户的自定节奏BCI系统。
