Application of EEG for multimodal human-machine interface

There are many input modalities for human-machine interface (HMI). Brain-signal that is one of biosignal has been studied as an input modality for HMI. Brain-signal based HMI can help disabled people to communicate with a machine using the brain's electrical activity. this study is focuses on usability of the EEG-based HMI's for available tools in real life and possibility of the EEG signal as input modality of multimodal interface. This study attempt to explore the electroencephalogram (EEG) signal measurement and analysis methods related to concentration for multimodal Interface. The experiments have been performed with various tasks, such as self-concentration, self-arithmetic (non-display), self-arithmetic (show display) and eye-closing. EEG signals are recorded while subjects perform each task on Fz, Cz, Pz. The receiver operating characteristic (ROC) curve analysis is to determine the threshold on each task. Rate of distinction range is 50.32% ∼ 56.77% with the threshold about self-arithmetic and 71.67%∼78.33% with the threshold about eye-closing. There are some meaningful results about threshold, self-arithmetic and eye-close activity. It can be used for brain-machine interface and multi-modal interface.