A two-class self-paced BCI to control a robot in four directions

Abstract

In this work, an electroencephalographic analysis-based, self-paced (asynchronous) brain-computer interface (BCI) is proposed to control a mobile robot using four different navigation commands: turn right, turn left, move forward and move back. In order to reduce the probability of misclassification, the BCI is to be controlled with only two mental tasks (relaxed state versus imagination of right hand movements), using an audio-cued interface. Four healthy subjects participated in the experiment. After two sessions controlling a simulated robot in a virtual environment (which allowed the user to become familiar with the interface), three subjects successfully moved the robot in a real environment. The obtained results show that the proposed interface enables control over the robot, even for subjects with low BCI performance.

Mobile robot navigation with a self-paced brain–computer interface based on high-frequency SSVEP

“…In a previous project, 36 using medium range frequencies (13,14,15 and 16 Hz), the subjects express some discomfort with stimuli. In this experiment, using high-frequency range SSVEP no one manifested any complain, furthermore, Volunteer 6 (who participated in both experiments) was delighted with such stimuli.…”

“…relaxed state and right-hand imagination). 13 The robotic group directed by Minguez developed several BCI for controlling robotic systems. For instance, a wheelchair was commanded using P300 evoked potentials, 14 and later on a teleoperated mobile robot was manoeuvred by a BCI.…”

“…16 All of these projects made use of mental tasks, motor imagery or P300 for controlling the robotic systems. [3][4][5][6][7][8][9][10][11][12][13][14][15][16] However, such systems generally require a training step that can necessitate from some minutes up to hours or even days. In addition, the user is sometimes subject to mental efforts that could produce mental fatigue.…”

Brain Computer Interface system based on indoor semi-autonomous navigation and motor imagery for Unmanned Aerial Vehicle control

“…They are widely used in non-invasive BCI system because of its simplicity, inexpensiveness and high temporal resolution (Kayikcioglu & Aydemir, 2010;Zavala-Fernández, Orglmeister, Trahms, & Sander, 2012). Usually, the BCI system can be utilized to restore the motor functions or to offer mobility for the motor disabled individuals by using a BCI controlled device, such as the motorized wheelchairs or service robots (Rebsamen et al, 2006;Ron-Angevin, Velasco-Alvarez, Sancha-Ros, & da Silva-Sauer, 2011;Velasco-Álvarez, RonAngevin, da Silva-Sauer, & Sancha-Ros, 2013). MI task is one of the most studied types of EEG signals in BCI systems (García-Laencina, Rodríguez-Bermudez, & Roca-Dorda, 2014).…”

Driving a Semiautonomous Mobile Robotic Car Controlled by an SSVEP-Based BCI

“…Ron-Angevin et al used a SMR-based two-class BCI application to choose between four direction commands in order to control a robot [ 8 ]. Three out of four subjects were able to control their BCI robot.…”