Using finite state machine and a hybrid of EEG signal and EOG artifacts for an asynchronous wheelchair navigation

Highlights

• Asynchronous wheelchair navigation system using EEG signals and EOG artifacts.

• Horizontal gazes are observed at C3, C4 and eyelid positions at O2.

• The system is modeled as a finite state machine with three states for each mode.

• Total of six different directions move in forward and backward.

• Actual navigation tasks are performed and registered an average accuracy of 97.5%.

Abstract

In this study, an asynchronous wheelchair navigation system using a hybrid of EEG signal and EOG artifacts embedded in EEG signals is demonstrated. The EEG signals are recorded at three different locations on the scalp in the occipital and motor cortex regions. First, an EEG signal related to eyelid position is analyzed and used to determine whether the eyes are closed or open. If the eyes are closed, no wheelchair movement is allowed. If the eyes are open, EOG traces (artifacts) from two other EEG signals are examined to infer the gaze direction of the eyes. A sliding window is utilized to position important cues in the trace signals at the center of the window for effective classification. The variance of the EEG signal is used to determine the eyelid position by thresholding. Then, features extracted from the EOG traces are used as inputs to a pair of minimum distance classifiers whose outputs reveal the gaze shift performed by the eyes. The wheelchair navigation system is designed to move forward and backward in a total of six different directions. However, the number of distinct gaze direction that can be used as commands to move the wheelchair is only three. Therefore, we model the system as a finite state machine with three modes, each containing three states to overcome this deficiency. The system is equipped with proximity sensor to avoid collision with obstacles. A stop command is also available for safety measures. In a real-time experiment involving 20 participants, the system performed well as it registered a high accuracy of 97.88% with an average of computational time less than 1 s. The system was also tested by five participants in a navigation experiment where each participant successfully completed all tasks without collision while showing improvement in maneuvering ability over attempts.

Introduction

Assisting the physically disabled and aging community to perform daily activities is challenging as their needs vary depending on their physical limitations. Until today, various forms of human machine interface (HMI) systems have been developed to help the physically challenged to regain some mobility and improve the quality of their lives. A typical HMI system normally takes a signal or a combination of signals as inputs and transform them into a command to be executed by an actuator. These signals can come in different forms like sip and puff (Mougharbel, El-Hajj, Ghamlouch, & Monacelli, 2013), eye tracking control (Nguyen & Jo, 2012), tongue control (Kim et al., 2013), head gesture (Halawani, ur Réhman, Li, & Anani, 2012) electromyogram (EMG) (Xu, Zhang, Luo, & Chen, 2013), electrooculogram (EOG) (Ianez, Ubeda, Azorin, & Perez-Vidal, 2012) and electroencephalogram (EEG) (Perdikis et al., 2014).

A brain-actuated wheelchair is an example of an HMI that can be used for assistive purpose. This paper presents an approach to navigate an asynchronous wheelchair using finite state machine and an EEG signal from the alpha band (8–13 Hz) signal and EOG traces extracted from EEG delta band (<3 Hz) signals. The alpha signal is recorded at the occipital region while the delta signals are obtained from the motor cortex area. From these signals, the eyelid positions (closed or open) and the gaze directions (straight, right or left) can be determined by a classifier. Depending on the eyelid positions and gaze directions, a command is issued to move the wheelchair. The wheelchair navigation system is modeled as a finite state machine to allow it to move in three directions forwardly or backwardly, using three gaze directions only. The user can also instruct the wheelchair to stop when needed.

The remainder of this paper is organized as follows. Section 2 describes the related works that use EEG and EOG signals to control an automated wheelchair. In Section 3, the methodology of the proposed system is presented where details of the data acquisition, sliding window, feature classification, finite state machine model and hardware implementation are given. Section 4 describes the experiments performed and the results achieved. Finally in Section 5, conclusions are drawn and future works outlined.