Sensorimotor brain-computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data.

Nikolai Kapralov, Mina Jamshidi Idaji, Tilman Stephani, Alina Studenova, Carmen Vidaurre, Tomas Ros, Arno Villringer, Vadim Nikulin
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Abstract

Objective.Serving as a channel for communication with locked-in patients or control of prostheses, sensorimotor brain-computer interfaces (BCIs) decode imaginary movements from the recorded activity of the user's brain. However, many individuals remain unable to control the BCI, and the underlying mechanisms are unclear. The user's BCI performance was previously shown to correlate with the resting-state signal-to-noise ratio (SNR) of the mu rhythm and the phase synchronization (PS) of the mu rhythm between sensorimotor areas. Yet, these predictors of performance were primarily evaluated in a single BCI session, while the longitudinal aspect remains rather uninvestigated. In addition, different analysis pipelines were used to estimate PS in source space, potentially hindering the reproducibility of the results.Approach.To systematically address these issues, we performed an extensive validation of the relationship between pre-stimulus SNR, PS, and session-wise BCI performance using a publicly available dataset of 62 human participants performing up to 11 sessions of BCI training. We performed the analysis in sensor space using the surface Laplacian and in source space by combining 24 processing pipelines in a multiverse analysis. This way, we could investigate how robust the observed effects were to the selection of the pipeline.Main results.Our results show that SNR had both between- and within-subject effects on BCI performance for the majority of the pipelines. In contrast, the effect of PS on BCI performance was less robust to the selection of the pipeline and became non-significant after controlling for SNR.Significance.Taken together, our results demonstrate that changes in neuronal connectivity within the sensorimotor system are not critical for learning to control a BCI, and interventions that increase the SNR of the mu rhythm might lead to improvements in the user's BCI performance.

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纵向数据的多元宇宙分析中,感知运动脑机接口的性能取决于信噪比,而不取决于μ节律的连通性。
目的:感知运动脑机接口(BCI)可作为与闭锁病人交流或控制假肢的渠道,它能从记录的使用者大脑活动中解码想象中的动作。然而,许多人仍然无法控制BCI,其潜在机制也不清楚。之前的研究表明,用户的 BCI 性能与μ节律的静息态信噪比(SNR)和感觉运动区之间μ节律的相位同步(PS)相关。然而,这些性能预测因素主要是在单次 BCI 会话中进行评估的,而纵向方面仍未得到研究。此外,不同的分析管道被用于估算源空间中的PS,这可能会妨碍结果的可重复性:为了系统地解决这些问题,我们使用一个公开的数据集对刺激前信噪比、PS 和会话中的 BCI 性能之间的关系进行了广泛的验证,该数据集包含 62 名进行了多达 11 次 BCI 训练的人类参与者。我们使用表面拉普拉斯在传感器空间进行了分析,并在多重宇宙分析中结合 24 个处理管道在源空间进行了分析。通过这种方法,我们可以研究观察到的效果对管道选择的稳健性:主要结果:我们的研究结果表明,对于大多数管道而言,信噪比对 BCI 性能既有受试者间的影响,也有受试者内的影响。相比之下,PS 对 BCI 性能的影响对管道选择的稳健性较差,在控制 SNR 后变得不显著:综上所述,我们的研究结果表明,感觉运动系统内神经元连接的变化对于学习控制BCI并不重要,而提高μ节奏信噪比的干预措施可能会提高用户的BCI性能。
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