Bimodal-Shared Control Interface for Assisted Mobility Application

The paper presents (Electro-oculography) EOG- (Electroencephalography) EEG- Radio Frequency Identification (RFID) based Bimodal-Shared Control Interface for mobility assistance application by controlling a mobile robotic arm. EOG-EEG bio-signal bimodal interface has been used to move the robot following predefined path reach at an object placed initial position (Zone 1). RFID as shared control identification and sending trigger signal gripper arm pick place it another 2) automatically. A threshold algorithm developed horizontal eye movement (HEOG) detection. Minimum Energy Combination (MEC) method recognize Steady State Visual Evoked Potential (SSVEP) brain pattern of EEG. Combining with system improved classification accuracy increased number commands without giving extra effort user. This reduces chances fatigue in users due continuously performing same task. Classification Information Transfer Rate (ITR) have calculated performance parameters compared previous literatures evaluation.