Hybrid BCI for Controlling a Robotic Arm over an IP Network

Abstract

A fully online EEG-based hybrid brain-computer interface (BCI) is presented. The BCI is used to control a robotic arm in three degrees of freedom. The system utilises the commercially available Emotiv SDK and EPOC neuroheadset. Using facial gestures, test subjects were able to carry out six different actions with an average detection accuracy of 66.1%. SSVEPs are used as a biofeedback mechanism to implement an attention-based “brain switch”. When the user gazes at the light stimulus, an SSVEP is detected in the brain and the system is either activated or deactivated. Since SSVEPs cannot be detected simultaneously with facial expressions due to noise, the “brain switch” may only be used to provide a user with the ability to mentally turn the system on or off, thereby reducing the number of false positives during rest periods. All 13 test subjects used in the experiment had different responses to SSVEP frequencies in the range of 3-20 Hz. Each subject has an optimal or resonant frequency. The average true-positive detection rate or accuracy at each individual’s optimal frequency is 74.2% (11.8% false positives) when using the Minimum Energy Classification (MEC) algorithm. A defining feature of the system is that it is highly extensible. The inter process communication (IPC) framework enables users to interact with multiple client objects over an IP network.