People with severe neurological impairments face many challenges in sensorimotor functions and communication with the environment; therefore they have increased demand for advanced, adaptive and personalized rehabilitation. During the last several decades, numerous studies have developed brain-computer interfaces (BCIs) with the goals ranging from providing means of communication to functional rehabilitation. Here we review the research on non-invasive, electroencephalography (EEG)-based BCI systems for communication and rehabilitation. We focus on the approaches intended to help severely paralyzed and locked-in patients regain communication using three different BCI modalities: slow cortical potentials, sensorimotor rhythms and P300 potentials, as operational mechanisms. We also review BCI systems for restoration of motor function in patients with spinal cord injury and chronic stroke. We discuss the advantages and limitations of these approaches and the challenges that need to be addressed in the future.

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EEG-Based Brain-Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21 st Century.

The emergence of assistive robots presents the possibility of restoring vital degrees of independence to the elderly and impaired in activities of daily living (ADL). However, one of the main challenges is the lack of a means for effective and intuitive human–robot interaction (HRI). While humans can express their intentions in different ways (e.g., physical gestures or motions, or speech or language patterns), gaze-based implicit intention communication is still underdeveloped. In this study, a novel nonverbal implicit communication framework based on eye gaze is introduced for HRI. In this framework, a user's eye-gaze movements are proactively tracked and analyzed to infer the user's intention in ADL. Then, the inferred intention can be used to command assistive robots for proper service. The advantage of this framework is that gaze-based communication can be handled by most of the people, as it requires very little effort, and most of the elderly and impaired retain visual capability. This framework is expected to simplify HRI, consequently enhancing the adoption of assistive technologies and improving users’ independence in daily living. The testing results of this framework confirmed that a human's subtle gaze cues on visualized objects could be effectively used for human-intention communication. Results also demonstrated that the gaze-based intention communication is easy to learn and use. In this study, the relationship of visual behaviors with the mental process during human intention expression was studied for the first time to build a fundamental understanding of this process. These findings are expected to guide further design of accurate intention inference algorithms and intuitive HRI.

Implicit Intention Communication in Human–Robot Interaction Through Visual Behavior Studies

This paper presents a user-friendly human machine interface (HMI) for hands-free control of an electric powered wheelchair (EPW). Its two operation modes are based on head movements: Mode 1 uses only one head movement to give the commands, and Mode 2 employs four head movements. An EEG device, namely Emotiv EPOC, has been deployed in this HMI to obtain the head movement information of users. The proposed HMI is compared with the joystick control of an EPW in an indoor environment. The experimental results show that Control Mode 2 can be implemented at a fast speed reliably, achieving a mean time of 67.90 seconds for the two subjects. However, Control Mode 1 has inferior performance, achieving a mean time of 153.20 seconds for the two subjects although it needs only one head movement. It is clear that the proposed HMI can be effectively used to replace the traditional joystick control for disabled and elderly people.

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Head movements based control of an intelligent wheelchair in an indoor environment

This project is a smart wheelchair based on eye tracking which is designed for people with locomotor disabilities. The add-on controlled module can be used with any electrical wheelchair. The smart wheel chair consists of four modules including imaging processing module, wheelchair-controlled module, SMS manager module and appliance-controlled module. The image processing module comprises of a webcam installed on the eyeglass and C++ customized image processing software. The captured image which is transmitted to raspberry Pi microcontroller will be processed using OpenCV to derive the 2D direction of eye ball. The coordinate of eyeball movement is then wirelessly transmitted to wheelchair-controlled module to control the movement of wheel chair. The wheelchair-controlled module is two dimensional rotating stages that installed to the joystick of the electrical wheelchair to replace the manual control of the wheelchair. The motion of eyeball is also used as the cursor control on the raspberry Pi screen to control the operation of some equipped appliance and send message to smart phone.

Smart wheelchair based on eye tracking

A novel technique is implemented for the eye controlled based independent and cost effective system. The purpose of Eye movement based control electric wheelchair is to eliminate the necessity of the assistance required for the disabled person. And it provides great opportunity of the disabled to feel of independent accessible life. The implemented system will allow the disabled person to control the wheelchair without the assistance from other persons. In this system controlling of wheelchair carried out based on Eye movements. The camera is mounted in front of the user, to capture the image of any one of the Eye (either left or right) and tracks the position of eye pupil with the use of Image processing techniques. According to the position of the eye, wheelchair motor will be directed to move left, right and forward. In addition to this, for the safety purpose ultrasonic sensor is mounted in front of wheelchair to detect the obstacles and automatically stop the wheelchair movement. To make system cost effective for monitoring, a Raspberry pi board allowed to access the system without displaying unit.

Autonomous camera based eye controlled wheelchair system using raspberry-pi

This paper proposes a new algorithm, the coherence algorithm, for eye movement tracking. Researchers continue to restore some functions lost by handicapped people using some powered electrical and robotic wheelchairs. This paper presents an application of eye tracking method to such wheelchairs. The coherence algorithm tracks the movement of eye towards left or right. The eye blinking feature is also used by the algorithm to control the starting and stopping of wheelchair.

Poonam S. Gajwani

Papers

Eye motion tracking for wheelchair control