Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, United States; Neurology Department, Emory University, Atlanta, GA, United States.
Interdisciplinary Neuroscience Program, University of Rhode Island, Kingston, RI, United States.
Comput Biol Med. 2024 Jan;168:107658. doi: 10.1016/j.compbiomed.2023.107658. Epub 2023 Nov 2.
Brain-computer interface (BCI) systems currently lack the required robustness for long-term daily use due to inter- and intra-subject performance variability. In this study, we propose a novel personalized scheme for a multimodal BCI system, primarily using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), to identify, predict, and compensate for factors affecting competence-related and interfering factors associated with performance.
11 (out of 13 recruited) participants, including five participants with motor deficits, completed four sessions on average. During the training sessions, the subjects performed a short pre-screening phase, followed by three variations of a novel visou-mental (VM) protocol. Features extracted from the pre-screening phase were used to construct predictive platforms using stepwise multivariate linear regression (MLR) models. In the test sessions, we employed a task-correction phase where our predictive models were used to predict the ideal task variation to maximize performance, followed by an interference-correction phase. We then investigated the associations between predicted and actual performances and evaluated the outcome of correction strategies.
The predictive models resulted in respective adjusted R-squared values of 0.942, 0.724, and 0.939 for the first, second, and third variation of the task, respectively. The statistical analyses showed significant associations between the performances predicted by predictive models and the actual performances for the first two task variations, with rhos of 0.7289 (p-value = 0.011) and 0.6970 (p-value = 0.017), respectively. For 81.82 % of the subjects, the task/workload correction stage correctly determined which task variation provided the highest accuracy, with an average performance gain of 5.18 % when applying the correction strategies.
Our proposed method can lead to an integrated multimodal predictive framework to compensate for BCI performance variability, particularly, for people with severe motor deficits.
由于个体间和个体内性能的可变性，脑机接口（BCI）系统目前缺乏长期日常使用所需的稳健性。在这项研究中，我们提出了一种新的多模态 BCI 系统个性化方案，主要使用功能近红外光谱（fNIRS）和脑电图（EEG）来识别、预测和补偿与性能相关的因素以及影响性能的干扰因素。
11 名（招募的 13 名中的 11 名）参与者，包括 5 名有运动障碍的参与者，平均完成了 4 次会议。在训练会议期间，受试者进行了一个简短的预筛选阶段，然后进行了三次新的视觉-心理（VM）协议的变体。从预筛选阶段提取的特征用于使用逐步多元线性回归（MLR）模型构建预测平台。在测试会议中，我们采用了任务校正阶段，我们的预测模型用于预测理想的任务变体以最大限度地提高性能，然后是干扰校正阶段。然后，我们研究了预测和实际表现之间的关联，并评估了校正策略的结果。
预测模型在任务的第一个、第二个和第三个变体中分别产生了 0.942、0.724 和 0.939 的调整后的 R-squared 值。统计分析显示，预测模型预测的表现与前两个任务变体的实际表现之间存在显著关联，rho 值分别为 0.7289（p 值=0.011）和 0.6970（p 值=0.017）。对于 81.82%的受试者，任务/工作量校正阶段正确确定了哪个任务变体提供了最高的准确性，当应用校正策略时，平均性能增益为 5.18%。
我们提出的方法可以导致一个集成的多模态预测框架，以补偿 BCI 性能的可变性，特别是对于有严重运动障碍的人。
