Kaviri Sina Makhdoomi, Vinjamuri Ramana
Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA.
Bioengineering (Basel). 2024 Sep 27;11(10):967. doi: 10.3390/bioengineering11100967.
Motor impairments caused by stroke significantly affect daily activities and reduce quality of life, highlighting the need for effective rehabilitation strategies. This study presents a novel approach to classifying motor tasks using EEG data from acute stroke patients, focusing on left-hand motor imagery, right-hand motor imagery, and rest states. By using advanced source localization techniques, such as Minimum Norm Estimation (MNE), dipole fitting, and beamforming, integrated with a customized Residual Convolutional Neural Network (ResNetCNN) architecture, we achieved superior spatial pattern recognition in EEG data. Our approach yielded classification accuracies of 91.03% with dipole fitting, 89.07% with MNE, and 87.17% with beamforming, markedly surpassing the 55.57% to 72.21% range of traditional sensor domain methods. These results highlight the efficacy of transitioning from sensor to source domain in capturing precise brain activity. The enhanced accuracy and reliability of our method hold significant potential for advancing brain-computer interfaces (BCIs) in neurorehabilitation. This study emphasizes the importance of using advanced EEG classification techniques to provide clinicians with precise tools for developing individualized therapy plans, potentially leading to substantial improvements in motor function recovery and overall patient outcomes. Future work will focus on integrating these techniques into practical BCI systems and assessing their long-term impact on stroke rehabilitation.
中风导致的运动障碍会显著影响日常活动并降低生活质量，这凸显了有效康复策略的必要性。本研究提出了一种利用急性中风患者的脑电图（EEG）数据对运动任务进行分类的新方法，重点关注左手运动想象、右手运动想象和静息状态。通过使用先进的源定位技术，如最小范数估计（MNE）、偶极子拟合和波束形成，并与定制的残差卷积神经网络（ResNetCNN）架构相结合，我们在EEG数据中实现了卓越的空间模式识别。我们的方法在偶极子拟合下的分类准确率为91.03%，在MNE下为89.07%，在波束形成下为87.17%，明显超过了传统传感器域方法55.57%至72.21%的范围。这些结果突出了在捕捉精确脑活动时从传感器域向源域转变的有效性。我们方法提高后的准确性和可靠性在推进神经康复中的脑机接口（BCI）方面具有巨大潜力。本研究强调了使用先进的EEG分类技术为临床医生提供精确工具以制定个性化治疗计划的重要性，这可能会显著改善运动功能恢复和患者整体预后。未来的工作将集中于将这些技术集成到实际的BCI系统中，并评估它们对中风康复的长期影响。
