School of Electrical and Computer Engineering, Center for Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA.
Comput Math Methods Med. 2013;2013:243257. doi: 10.1155/2013/243257. Epub 2013 Apr 24.
With the advancements in modern signal processing techniques, the field of brain-computer interface (BCI) is progressing fast towards noninvasiveness. One challenge still impeding these developments is the limited number of features, especially movement-related features, available to generate control signals for noninvasive BCIs. A few recent studies investigated several movement-related features, such as spectral features in electrocorticography (ECoG) data obtained through a spectral principal component analysis (PCA) and direct use of EEG temporal data, and demonstrated the decoding of individual fingers. The present paper evaluated multiple movement-related features under the same task, that is, discriminating individual fingers from one hand using noninvasive EEG. The present results demonstrate the existence of a broadband feature in EEG to discriminate individual fingers, which has only been identified previously in ECoG. It further shows that multiple spectral features obtained from the spectral PCA yield an average decoding accuracy of 45.2%, which is significantly higher than the guess level (P < 0.05) and other features investigated (P < 0.05), including EEG spectral power changes in alpha and beta bands and EEG temporal data. The decoding of individual fingers using noninvasive EEG is promising to improve number of features for control, which can facilitate the development of noninvasive BCI applications with rich complexity.
随着现代信号处理技术的进步，脑机接口（BCI）领域正在朝着非侵入性的方向快速发展。一个仍然阻碍这些发展的挑战是，用于生成非侵入性 BCI 控制信号的特征数量有限，特别是与运动相关的特征。最近的一些研究调查了几种与运动相关的特征，例如通过谱主成分分析（PCA）从脑电皮质电图（ECoG）数据中获得的谱特征以及直接使用 EEG 时间数据，并证明了单个手指的解码。本文在相同任务下评估了多种与运动相关的特征，即使用非侵入性 EEG 从一只手上区分单个手指。目前的结果表明 EEG 中存在宽带特征以区分单个手指，这在之前仅在 ECoG 中被识别出来。它进一步表明，从谱 PCA 获得的多个谱特征的平均解码准确率为 45.2%，明显高于猜测水平（P < 0.05）和其他研究的特征（P < 0.05），包括 alpha 和 beta 波段的 EEG 谱功率变化和 EEG 时间数据。使用非侵入性 EEG 对单个手指进行解码有望改善控制的特征数量，这可以促进具有丰富复杂性的非侵入性 BCI 应用的发展。
