Noise reduction in non-invasive brain-computer interfaces for robot control

Abstract To employ a brain-computer interface to control an assistance robot could help disabled people to achieve a minimum degree of autonomy and reduce their dependence on caregivers for simple tasks. In this project, we use a textile, capacitive brain computer interface (BCI) to control an assistant robot. Our BCI is based on Steady State Visual Evoked Potentials (SSVEP), which are a response of the brain to a periodic visual stimulus at a fixed frequency. Since the brain signals are in the microvolt range, the noise reduction of external disturbances has a high relevance. Here, we demonstrate a correlation method for detecting and eliminating phase shifts between different measurements for averaging. This method allows measurements in unshielded environment of two microvolts (uV) with an averaging factor of 100 in our non-invasive BCI set-up.