Brain-computer interface (BCI) systems, which provide users with an additional channel to communicate with external devices, have been mainly developed using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). To complement each modality's pros and cons, various hybrid NIRS-EEG studies have been investigated. However, most studies focused on enhancing the classification accuracy rather than analyzing the characteristics of used features. This study aimed to investigate whether EEG features from spatial, temporal, and spectral domains would exhibit the diverse efficacy in hybrid NIRS-EEG BCI. Open access NIRS and EEG recordings of left/right hand gripping imagery from twenty-nine healthy subjects were utilized. Common spatial patterns (CSP), time domain parameters (TDP), and power spectral density (PSD) were separately employed with NIRS to evaluate the discrimination performance. Within dataset, NIRS with CSP showed the highest classification accuracy with linear support vector machine (LSVM) classifier (mean accuracy, 71.4%). For kernel SVM (KSVM) classifiers, mean accuracy of NIRS with TDP features was lower than accuracy of only NIRS features (mean accuracy, NIRS: 53.1% and NIRS with TDP: 50.5%). The findings suggested that binary motor imagery tasks, which involve localized brain activation, could be enhanced by applying features including rich spatial information.