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Robotic Control by EOG-EEG-RFID based Multimodal Interface and Shared Control Technology

. This study describes a robotic control application that uses Radio Frequency Identification (RFID) technology as shared control architecture and Electrooculography (EOG) and Electroencephalography (EEG) biosignals for multimodal Man-Machine Interface. The proposed application had to steer a robot along a given route aimed at helping people with mobility disabilities in performing daily tasks independently. With proper threshold selection, EOG signals were classified. Whereas, EEG interface used Minimum Energy (ME) combination for feature extraction and Linear Discriminant Analysis (LDA) based classifier. Both EOG and EEG signals had been synchronously used for robotic movement control and both complements each other. Further, RFID was used to identify the object and perform the pick/place operation. The proposed model provides longer range of applications with less fatigue. The effectiveness of the proposed model had been verified by controlling a mobile robot successfully. In this study, EOG-EEG-RFID based multimodal interface and shared control approach has been proposed. The application results have been applied to control a robot. The model has three modes: (a) EOG mode, (b) EEG mode, (c) RFID mode. EOG is responsible for two horizontal movements (right, left) and EEG is being used for two movements (forward, backward) and emergency alarm. RFID mode is used to identify the object and to perform pick/place operation.