Network Properties in Transitions of Consciousness during Propofol-induced Sedation.

Reliable electroencephalography (EEG) signatures of transitions between consciousness and unconsciousness under anaesthesia have not yet been identified. Herein we examined network changes using graph theoretical analysis of high-density EEG during patient-titrated propofol-induced sedation. Responsiveness was used as a surrogate for consciousness.

Spatio-temporal dynamics of multimodal EEG-fNIRS signals in the loss and recovery of consciousness under sedation using midazolam and propofol.

On sedation motivated by the clinical needs for safety and reliability, recent studies have attempted to identify brain-specific signatures for tracking patient transition into and out of consciousness, but the differences in neurophysiological effects between 1) the sedative types and 2) the presence/absence of surgical stimulations still remain unclear. Here we used multimodal electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) measurements to observe electrical and hemodynamic responses during sedation simultaneously. Forty healthy volunteers were instructed to push the button to administer sedatives in response to auditory stimuli every 9-11 s.

Change in functional networks for transitions between states of consciousness during midazolam-induced sedation.

How brain dynamics change across conscious states, including reliable signatures of the transitions between unconsciousness and consciousness, remains unclear. In this work, we addressed the changes in functional brain networks during self-titrated midazolam sedation using high-density electroencephalography (EEG) in ten subjects. We were particularly interested in the underlying network alterations, identified with graph theory, associated with transitions between states of consciousness.

Towards an enhanced ERP speller based on the visual processing of face familiarity.

In this study, a novel P300 based brain-computer interface (BCI) system using random set presentation pattern and employing the effect of face familiarity has been proposed and developed. While the effect of face familiarity is widely studied in the cognitive neurosciences, it has so far not been addressed for the purpose of BCI. We compare P300-based BCI performances of a conventional row-column (RC)-based paradigm with our novel approach.

An efficient ERP-based brain-computer interface using random set presentation and face familiarity.

Event-related potential (ERP)-based P300 spellers are commonly used in the field of brain-computer interfaces as an alternative channel of communication for people with severe neuro-muscular diseases. This study introduces a novel P300 based brain-computer interface (BCI) stimulus paradigm using a random set presentation pattern and exploiting the effects of face familiarity. The effect of face familiarity is widely studied in the cognitive neurosciences and has recently been addressed for the purpose of BCI.