Optical EEG (OEEG): A novel technique toward plug-and-play non-invasive brain imaging and human-machine interfacing

Summary form only given. The human brain dynamics can be studied based on the fast-neuronal and slow-hemodynamic signals. Referring to the available brain imaging techniques, the electroencephalography (EEG) and Magnetoencephalography (MEG) can only measure the fast-neuronal signal. Positron-emission tomography (PET) and Functional-magnetic-resonance-imaging (tMRI) also can measure only the slowhemodynamic signal. However, the functional-near-infrared spectroscopy (fNIRS) is the only imaging system capable of detecting both fast-neuronal and slow-hemodynamic signals. In this research we have first implemented a miniaturized low-power fNIRS front-end using standard CMOS technology for monitoring the hemodynamic signals (HbO & HbR) in continuous wave (CW) and time-correlated single photon counting (TCSPC) modes of operation. A new on-chip implemented system (called Optical EEG: OEEG) is also proposed to estimate the neural activities based on the acquired optical data using fNIRS. This is the first proposed wholly optical system capable of monitoring neuronal and hemodynamic signals and it is the first step towards introducing an optical alternative system for EEG. In order to develop a real-time system, we have proposed detecting the stable, controllable and observable hemodynamic states of the brain, based on the continuous-discrete extended-Kalman-filter and bifurcation analysis of the balloon-model. It extracts and validates the states and verifies their stability, controllability and observability. The stable, controllable and observable states are used for further processing. In contrast to the other available complex techniques, the complexity of the proposed Algorithm is moderate and its implementation requires only raw data) extracted directly from a continuous-wave fNIRS-channel. The results confirm the efficiency of the proposed technique in dealing with noise and movement-artifacts. It increases the signal-to-noise-ratio (SNR) and the speed.