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
