Control of a Wheelchair in an Indoor Environment Based on a Brain–Computer Interface and Automated Navigation

Abstract

The concept of controlling a wheelchair using brain signals is promising. However, the continuous control of a wheelchair based on unstable and noisy electroencephalogram signals is unreliable and generates a significant mental burden for the user. A feasible solution is to integrate a brain-computer interface (BCI) with automated navigation techniques. This paper presents a brain-controlled intelligent wheelchair with the capability of automatic navigation. Using an autonomous navigation system, candidate destinations and waypoints are automatically generated based on the existing environment. The user selects a destination using a motor imagery (MI)-based or P300-based BCI. According to the determined destination, the navigation system plans a short and safe path and navigates the wheelchair to the destination. During the movement of the wheelchair, the user can issue a stop command with the BCI. Using our system, the mental burden of the user can be substantially alleviated. Furthermore, our system can adapt to changes in the environment. Two experiments based on MI and P300 were conducted to demonstrate the effectiveness of our system.

Review of real brain-controlled wheelchairs

“…Other papers used methods such as learning vector quantization in mu and beta bands (43), the logarithmic value in the bands of interest (34,35), the common spatial patterns (CSP) (44,59) …”

“…Five BCWs based on ERD/ERS signal had a low-level navigation system (18,34,43,44,57), three used a shared management (28, 35,46) and one with a highlevel navigation system (59). Therefore, it can be seen that the handling of low-level is used to a greater degree than shared control or highlevel.…”

“…So the CTR was 2.11 (σ = 2.67). It must be taken into account that the BCW of the study of Varona-Moya et al (57) Zhang et al (59) was the only ones with a CTR greater than the unit. The rest of the studies have a CTR equal to the unit, where every task served to execute a single command.…”

“…Only one P300 BCW used a lowlevel navigation system (33), while three of them used high-level (31,32,59) and two used shared control (29,47). On the one hand, a highlevel system allows the selection of the destination to which the BCW will go autonomously, so the P300 is a great candidate to serve as a communication signal, due to its high success rate, and the possibility of offering many destinations in a GUI simultaneously.…”

“…For P300 signal, the extraction methods were more heterogeneous than the previous ERD/ERS signal. Three proposals used the signal averaging technique (32,33,59), and the others used the moving average (29) and a statistical spatial filter (47). On the other hand, only one BCW used raw signal (27).…”