1 Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Silcherstr. 5, 72076, Tübingen, Germany.
2 Instituto de Investigación de Ingeniería de Aragón (I3A), Departamento de Informática e Ingeniería de Sistemas, University of Zaragoza, María de Luna 1, 50015, Zaragoza, Spain.
Int J Neural Syst. 2018 Sep;28(7):1750060. doi: 10.1142/S0129065717500605. Epub 2017 Dec 17.
Motor rehabilitation based on the association of electroencephalographic (EEG) activity and proprioceptive feedback has been demonstrated as a feasible therapy for patients with paralysis. To promote long-lasting motor recovery, these interventions have to be carried out across several weeks or even months. The success of these therapies partly relies on the performance of the system decoding movement intentions, which normally has to be recalibrated to deal with the nonstationarities of the cortical activity. Minimizing the recalibration times is important to reduce the setup preparation and maximize the effective therapy time. To date, a systematic analysis of the effect of recalibration strategies in EEG-driven interfaces for motor rehabilitation has not yet been performed. Data from patients with stroke (4 patients, 8 sessions) and spinal cord injury (SCI) (4 patients, 5 sessions) undergoing two different paradigms (self-paced and cue-guided, respectively) are used to study the performance of the EEG-based classification of motor intentions. Four calibration schemes are compared, considering different combinations of training datasets from previous and/or the validated session. The results show significant differences in classifier performances in terms of the true and false positives (TPs) and (FPs). Combining training data from previous sessions with data from the validation session provides the best compromise between the amount of data needed for calibration and the classifier performance. With this scheme, the average true (false) positive rates obtained are 85.3% (17.3%) and 72.9% (30.3%) for the self-paced and the cue-guided protocols, respectively. These results suggest that the use of optimal recalibration schemes for EEG-based classifiers of motor intentions leads to enhanced performances of these technologies, while not requiring long calibration phases prior to starting the intervention.
基于脑电图 (EEG) 活动与本体感觉反馈的关联的运动康复已被证明是瘫痪患者可行的治疗方法。为了促进长期的运动恢复，这些干预措施必须持续数周甚至数月。这些疗法的成功部分依赖于系统解码运动意图的性能，通常必须重新校准以应对皮层活动的非平稳性。尽量减少重新校准时间对于减少设置准备时间和最大化有效治疗时间非常重要。迄今为止，尚未对运动康复的 EEG 驱动接口的重新校准策略的效果进行系统分析。使用来自中风（4 名患者，8 次会话）和脊髓损伤（SCI）（4 名患者，5 次会话）患者的数据，分别进行两种不同的范式（自我调节和提示引导），以研究基于 EEG 的运动意图分类的性能。比较了四种校准方案，考虑了来自先前和/或验证会话的训练数据集的不同组合。结果表明，在真正的（假）阳性率 (TPs) 和 (FPs) 方面，分类器的性能存在显著差异。将来自先前会话的训练数据与验证会话的数据相结合，可以在校准所需数据量和分类器性能之间取得最佳折衷。使用此方案，在自我调节和提示引导协议中，平均真实（假）阳性率分别为 85.3%（17.3%）和 72.9%（30.3%）。这些结果表明，对于基于 EEG 的运动意图分类器使用最优的重新校准方案可以提高这些技术的性能，同时不需要在开始干预之前进行长时间的校准阶段。
