A Hybrid Brain Computer Interface to Control the Direction and Speed of a Simulated or Real Wheelchair

Abstract

Brain-computer interfaces (BCIs) are used to translate brain activity signals into control signals for external devices. Currently, it is difficult for BCI systems to provide the multiple independent control signals necessary for the multi-degree continuous control of a wheelchair. In this paper, we address this challenge by introducing a hybrid BCI that uses the motor imagery-based mu rhythm and the P300 potential to control a brain-actuated simulated or real wheelchair. The objective of the hybrid BCI is to provide a greater number of commands with increased accuracy to the BCI user. Our paradigm allows the user to control the direction (left or right turn) of the simulated or real wheelchair using left- or right-hand imagery. Furthermore, a hybrid manner can be used to control speed. To decelerate, the user imagines foot movement while ignoring the flashing buttons on the graphical user interface (GUI). If the user wishes to accelerate, then he/she pays attention to a specific flashing button without performing any motor imagery. Two experiments were conducted to assess the BCI control; both a simulated wheelchair in a virtual environment and a real wheelchair were tested. Subjects steered both the simulated and real wheelchairs effectively by controlling the direction and speed with our hybrid BCI system. Data analysis validated the use of our hybrid BCI system to control the direction and speed of a wheelchair.

Closed-Loop Brain–Machine–Body Interfaces for Noninvasive Rehabilitation of Movement Disorders

“…The adaptive model (Section 6.3) controls the actuator's force and the sensory feedback closes the sensory-motor loop (Section 6.4). It is expected, after sufficient training, that the sensory feedback will trigger synaptic changes in the corticostriato-thalamic circuits that will then modify positively the outcomes of the pathophysiological condition of PD patients, as proposed by other authors 9,14,15 . Ultimately, the neuroplastic changes induced by practice with the brain-machine-body interface are expected to provide long-term benefits post-training.…”

“…The earliest use of BMIs was to bypass neuromuscular signaling pathways, providing a means for paralyzed patients to interact with their environment in a way that does not depend on muscle control 10 . This strategy has been, and still is, the focus of a great body of research, allowing patients suffering from various neuromuscular conditions to interact and communicate with their environment via artificial actuators, including a computer cursor 11 , a neuroprosthetic limb 12 , and virtual 13 or real devices, such as a robotic arm 14 , or electric wheelchairs 15 . The use of BMIs for communication is often referred to as assistive.…”

Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation

“…A BCI allows subjects to manipulate mobile robots, robotic exoskeletons, and robotic wheelchairs via their “minds.” For example, a brain-actuated wheelchair is a solution for subjects who are unable to use conventional interfaces due to motor disabilities but able to issue commands using their thoughts. Long et al proposed a hybrid BCI integrating motor imagery-based mu rhythm and P300 potential to control wheelchairs [3]. Subjects were able to command directions by using left and right hand imageries and change speeds via flashing buttons on a graphical user interface.…”

An Efficient Framework for EEG Analysis with Application to Hybrid Brain Computer Interfaces Based on Motor Imagery and P300

“…This leads to difficulty in the application of conventional statistical analysis to combine and learn brain patterns together. Many attempts to analyze the signal under the hybrid BCI are carried out though extracting the features from different modalities separately and then concatenating them to feed into some relative simple classifiers [2, 3]. However, these methods combine and learn the features indirectly which would lead to a nonoptimized resolution.…”