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Objective: Nonmanual human–machine interfaces (HMIs) have been studied for wheelchair control with the aim of helping severely paralyzed individuals regain some mobility. The challenge is to rapidly, accurately, and sufficiently produce control commands, such as left and right turns, forward and backward motions, acceleration, deceleration, and stopping. In this paper, a novel electrooculogram (EOG) based HMI is proposed for wheelchair control. Methods: A total of 13 flashing buttons, each of which corresponds to a command, are presented in the graphical user interface. These buttons flash on a one-by-one manner in a predefined sequence. The user can select a button by blinking in sync with its flashes. The algorithm detects the eye blinks from a channel of vertical EOG data and determines the user's target button based on the synchronization between the detected blinks and the button's flashes. Results: For healthy subjects/patients with spinal cord injuries, the proposed HMI achieved an average accuracy of 96.7% / 91.7% and a response time of 3.53 s/3.67 s with 0 false positive rates (FPRs). Conclusion: Using one channel of vertical EOG signals associated with eye blinks, the proposed HMI can accurately provide sufficient commands with a satisfactory response time. Significance: The proposed HMI provides a novel nonmanual approach for severely paralyzed individuals to control a wheelchair. Compared with a newly established EOG-based HMI, the proposed HMI can generate more commands with higher accuracy, lower FPR, and fewer electrodes. [ABSTRACT FROM AUTHOR]

Objective: A challenging task for an electroencephalography (EEG)-based asynchronous brain-computer interface (BCI) is to effectively distinguish between the idle state and the control state while maintaining a short response time and a high accuracy when commands are issued in the control state. This study proposes a novel hybrid asynchronous BCI system based on a combination of steady-state visual evoked potentials (SSVEPs) in the EEG signal and blink-related electrooculography (EOG) signals. Methods: Twelve buttons corresponding to 12 characters are included in the graphical user interface (GUI). These buttons flicker at different fixed frequencies and phases to evoke SSVEPs and are simultaneously highlighted by changing their sizes. The user can select a character by focusing on its frequency-phase stimulus and simultaneously blinking his/her eyes in accordance with its highlighting as his/her EEG and EOG signals are recorded. A multifrequency band-based canonical correlation analysis (CCA) method is applied to the EEG data to detect the evoked SSVEPs, whereas the EOG data are analyzed to identify the user's blinks. Finally, the target character is identified based on the SSVEP and blink detection results. Results: Ten healthy subjects participated in our experiments and achieved an average information transfer rate (ITR) of 105.52 bits/min, an average accuracy of 95.42%, an average response time of 1.34 s and an average false-positive rate (FPR) of 0.8%. Conclusion: The proposed BCI generates multiple commands with a high ITR and low FPR. Significance: The hybrid asynchronous BCI has great potential for practical applications in communication and control. [ABSTRACT FROM AUTHOR]

Objective: This paper presents an asyn-chronous electrooculography (EOG)-based human–machine interface (HMI) for smart home environmental control with the purpose of providing daily assistance for severe spinal cord injury (SCI) patients. Methods: The proposed HMI allows users to interact with a smart home environment through eye blinking. Specifically, several buttons, each corresponding to a control command, randomly flash on a graphical user interface. Each flash of the buttons functions as a visual cue for the user to blink. To issue a control command, the user can blink synchronously with the flashes of the corresponding button. Through detecting blinks based on the recorded EOG signal, the target button and its corresponding control command are determined. Seven SCI patients participated in an online experiment, during which the patients were required to control a smart home environment including household electrical appliances, an intelligent wheelchair, as well as a nursing bed via the proposed HMI. Results: The average false operation ratio in the control state was 4.1%, whereas during the idle state, no false operations occurred. Conclusion: All SCI patients were able to control the smart home environment using the proposed EOG-based HMI with satisfactory performance in terms of the false operation ratio in both the control and the idle states. Significance: The proposed HMI offers a simple and effective approach for patients with severe SCIs to control a smart home environment. Therefore, it is promising to assist severe SCI patients in their daily lives. [ABSTRACT FROM AUTHOR]