Kato Masaki, Kanoga Suguru, Hoshino Takayuki, Fukami Tadanori
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2991-2994. doi: 10.1109/EMBC44109.2020.9176612.
Electroencephalogram (EEG) data during motor imagery tasks regarding small-scale physical dynamics such as finger motions have low discriminability because capturing the spatial difference of the motions is difficult. We assumed that more discriminative features can be captured if spatial filters maximize the independence of each class data. This study constructed spatial filters named multiclass common spatial pattern (CSP), which maximize an approximation of mutual in-formation of extracted components and class labels, and applied them to a five-class motor-imagery dataset containing finger motion tasks. By applying multiclass CSP, the classification accuracies were improved (Mean SD: 40.6 ± 10.1%) compared with classical CSP (21.8 ± 2.5%) and no spatial filtering case (38.7±10.0%). In addition, we visualized learned spatial filters to assess the trend of discriminative features of finger motions. For these results, it was clear that multiclass CSP captured task-specific spatial maps for each finger motion and outperformed multiclass motor-imagery classification performance about 2% even when the tasks are small-scale physical dynamics.
在诸如手指运动等小规模身体动态的运动想象任务期间，脑电图（EEG）数据的可辨别性较低，因为捕捉这些运动的空间差异很困难。我们假设，如果空间滤波器能使每个类别数据的独立性最大化，就能捕捉到更具辨别力的特征。本研究构建了名为多类别共同空间模式（CSP）的空间滤波器，它能使提取成分与类别标签之间的互信息近似最大化，并将其应用于包含手指运动任务的五类运动想象数据集。通过应用多类别CSP，与经典CSP（21.8±2.5%）和无空间滤波情况（38.7±10.0%）相比，分类准确率得到了提高（均值±标准差：40.6±10.1%）。此外，我们将学习到的空间滤波器可视化，以评估手指运动辨别特征的趋势。基于这些结果，很明显多类别CSP为每个手指运动捕捉到了特定任务的空间图谱，并且即使在任务是小规模身体动态的情况下，其多类别运动想象分类性能也优于约2%。
