Leamy Darren J, Ward Tomas E
Biomedical Research Group, Department of Electronic Engineering, National University of Ireland Maynooth, Ireland.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:4230-3. doi: 10.1109/IEMBS.2010.5627377.
We describe here the design, set-up and first time classification results of a novel co-locational functional Near-Infrared Spectroscopy/Electroencephalography (fNIRS/EEG) recording device suitable for brain computer interfacing applications using neural-hemodynamic signals. Our dual-modality system recorded both hemodynamic and electrical activity at seven sites over the motor cortex during an overt finger-tapping task. Data was collected from two subjects and classified offline using Linear Discriminant Analysis (LDA) and Leave-One-Out Cross-Validation (LOOCV). Classification of fNIRS features, EEG features and a combination of fNIRS/EEG features were performed separately. Results illustrate that classification of the combined fNIRS/EEG feature space offered average improved performance over classification of either feature space alone. The complementary nature of the physiological origin of the dual measurements offer a unique and information rich signal for a small measurement area of cortex. We feel this technology may be particularly useful in the design of BCI devices for the augmentation of neurorehabilitation therapy.
我们在此描述一种新型的共定位功能近红外光谱/脑电图(fNIRS/EEG)记录设备的设计、设置及首次分类结果,该设备适用于利用神经血液动力学信号的脑机接口应用。在一项明显的手指轻敲任务中,我们的双模态系统在运动皮层的七个部位记录了血液动力学和电活动。数据来自两名受试者,并使用线性判别分析(LDA)和留一法交叉验证(LOOCV)进行离线分类。分别对fNIRS特征、EEG特征以及fNIRS/EEG特征组合进行分类。结果表明,与单独对任一特征空间进行分类相比,对组合的fNIRS/EEG特征空间进行分类平均性能有所提高。这两种测量的生理起源的互补性质为小面积的皮层测量区域提供了独特且信息丰富的信号。我们认为这项技术在用于增强神经康复治疗的脑机接口设备设计中可能特别有用。