# A Semi-Automatic Wheelchair with Navigation Based on Virtual-Real 2D Grid Maps and EEG Signals
## 1 Introduction  
In recent years, many methods have been introduced to develop types of smart wheelchairs for people with different disabilities such as assistive technology [1], user physical interface [2], or semi-control (sharing control between user and machine) [3]. One of the most important problems in a smart wheelchair is to provide independent mobility for the elderly or severely disabled people, who cannot control an electric wheelchair using a joystick. Therefore, restoring their activity skills can significantly improve their life quality. The development of a typical smart wheelchair highly depends on the ability and disability of the user. It means that a patient with impaired activity often lacks muscle control and then it is difficult to control the movement of the arms and legs in the worst case. To support the mobility of patients, signals for control can be generated from actions such as voice, thoughts, eyes, and tongue [4; 5; 6]. In order to obtain good signals, users must control their emotions well and also highly concentrate for accuracy. This is difficult for users with severe disability, although it may be a good option. For people with severe disability, the best solution could use multiple signals from sensors installed on the user's body parts and the surrounding environment and the signals are analyzed before giving the desired commands for wheelchair control [7]. Using this solution could improve thedifficulty of people with severe disability in the wheelchair control compared to solutions using one input. EEG signals related to the human brain, which have challenging problems, have attracted many researchers. In particular, recent research on cognitive and motor control to improve the Brain-Computer Interface (BCI) for enhancing the health of elderly people has been shown [8]. According to this study, the BCI system can be useful for elderly people in training their motor/cognitive abilities to prevent the effects of aging. Therefore, it can help them to more easily control household appliances and to communicate information in daily activities. In [9], the authors represented the physical principles of BCI and the fundamental new methods for acquiring and analyzing EEG signals for controls related to brain activities. In particular, the BCI system was classified into three main categories including active, reactive, and passive. Regarding an active BCI, the neuro interface user controls a complex external device such as a wheelchair through a series of functional components of the control system and sees the results of this control on a screen. Reactive BCI inherits many features of active BCI, with a significant change to implement a control system based on the classification of brain responses to stimuli such as visuals, sounds, and touch. Passive BCIs are designed to monitor current brain activity and thereby provide important information about the operator's mental state, user intent, and situational interpretation. The Brain-Controlled Wheelchair (BCW) is a typical BCI application, which can help people with a physical disability to communicate with the outside environment. In [10], the BCW was exploited from many aspects, including the type of EEG signal acquisition, the command set for the control system, and the control method. Moreover, the authors summarized the recent development of the BCW and it can be mainly expressed in three aspects: from the wet electrode to the dry electrode; from single-mode to multi-mode; from synchronous control to asynchronous control. Therefore, it indicates that new functions have been employed in the BCW to increase its stability and robustness. Mapping and navigation for wheelchairs or self-propelled robots have attracted many researchers in recent decades. The wheelchairs or self-propelled robots need to be provided maps for movement in detail so that they can be located in moving spaces. Moreover, their current coordinates were used as a basis for collecting new information during the moving process [11].