Qiyun Huang - Academia.edu (original) (raw)

Papers by Qiyun Huang

IEEE Transactions on Biomedical Engineering, 2020

Objective: A challenging task for an electroencephalography (EEG)-based asynchronous brain-comput... more 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 falsepositive 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.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019

This paper presents a new asynchronous hybrid brain-computer interface (BCI) system that integrat... more This paper presents a new asynchronous hybrid brain-computer interface (BCI) system that integrates a speller, a web browser, an e-mail client, and a file explorer using electroencephalographic (EEG) and electrooculography (EOG) signals. More specifically, an EOG-based button selection method, which requires the user to blink his/her eyes synchronously with the target button's flashes during button selection, is first presented. Next, we propose a mouse control method by combining EEG and EOG signals, in which the left-/right-hand motor imagery (MI)related EEG is used to control the horizontal movement of the mouse and the blink-related EOG is used to control the vertical movement of the mouse and to select/reject a target. These two methods are further combined to develop the integrated hybrid BCI system. With the hybrid BCI, users can input text, access the internet, communicate with others via e-mail, and manage files in their computer using only EEG and EOG without any body movements. Ten healthy subjects participated in a comprehensive online experiment, and superior performance was achieved compared with our previously developed P300-and MI-based BCI and some other asynchronous BCIs, therefore demonstrating the system's effectiveness.

Frontiers in Neuroscience, 2019

Most existing brain-computer Interfaces (BCIs) are designed to control a single assistive device,... more Most existing brain-computer Interfaces (BCIs) are designed to control a single assistive device, such as a wheelchair, a robotic arm or a prosthetic limb. However, many daily tasks require combined functions which can only be realized by integrating multiple robotic devices. Such integration raises the requirement of the control accuracy and is more challenging to achieve a reliable control compared with the single device case. In this study, we propose a novel hybrid BCI with high accuracy based on electroencephalogram (EEG) and electrooculogram (EOG) to control an integrated wheelchair robotic arm system. The user turns the wheelchair left/right by performing left/right hand motor imagery (MI), and generates other commands for the wheelchair and the robotic arm by performing eye blinks and eyebrow raising movements. Twenty-two subjects participated in a MI training session and five of them completed a mobile self-drinking experiment, which was designed purposely with high accuracy requirements. The results demonstrated that the proposed hBCI could provide satisfied control accuracy for a system that consists of multiple robotic devices, and showed the potential of BCI-controlled systems to be applied in complex daily tasks.

Neurocomputing, 2018

Biological signals, including electroencephalography (EEG) and electrooculography (EOG), are ofte... more Biological signals, including electroencephalography (EEG) and electrooculography (EOG), are often used to develop switches, which represent a class of typical asynchronous human-computer interfaces (HCIs) in which control and idle states need to be distinguished based on a criterion. Determining a satisfactory criterion for rapid and accurate discrimination between control and idle states remains a challenging issue, as EEG signals are highly noisy and nonstationary, and EOG signals are highly affected by unintended/spontaneous eye movements. Therefore, most existing EEG-or EOG-based switches are characterized by disadvantages of long response times (RTs) or high false positive rates (FPRs). The primary contribution of this work is the development of a novel EOG-based switch design, in which a visual trigger mechanism is introduced to guide the users' blinks and to assist in detecting blinks. Specifically, the graphical user interface (GUI) includes a switch button that flashes once per 1.2 s. The user is instructed to blink synchronously with the flashes of the switch button to issue an on/off command while a single-channel EOG signal is collected. A waveform detection algorithm is applied to the ongoing EOG

Journal of Neural Engineering, 2019

Objective. In this study, we combine a wheelchair and an intelligent robotic arm based on electro... more Objective. In this study, we combine a wheelchair and an intelligent robotic arm based on electrooculogram (EOG) signal to help patients with spinal cord injuries (SCIs) accomplish a self-drinking task. The main challenge is to accurately control the wheelchair to ensure that the randomly located object is within a limited reachable space of the robotic arm (length: 0.8 m; width: 0.4 m; height: 0.6 m), which requires decimeter-level precision and is still unproved for EOG-based systems as well as EEG-based systems. Approach. A novel high-precision EOG-based human machine interface (HMI) is proposed, which can effectively translate two kinds of eye movements (i.e., blinking and raising eyebrows) into various commands. For the wheelchair, the positional precision can reach decimeter-level and the minimal steering angle is 5 •. For the intelligent robotic arm, a shared control is implemented based on the EOG-based HMI, two cameras and the arm's own intelligence. Main results. After a short training, five healthy subjects and five paralyzed patients with severe SCIs successfully completed three experiments. For healthy subjects/patients with SCIs, the system achieved an average accuracy of 99.3%/97.3%, an average response time of 1.91 s/2.02 s per command and an average stop-response time of 1.30 s/1.36 s, with a 0 false operation rate. Significance. The EOG-based HMI can provide sufficient control precisions to integrate a wheelchair and a robotic arm into a system, which can help patients with SCIs to accomplish the self-drinking task. (ChiCTR1800019764)

IEEE transactions on bio-medical engineering, Jan 9, 2018

This paper presents an asynchronous EOG-based human machine interface (HMI) for smart home enviro... more This paper presents an asynchronous 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. 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. The average false...

IEEE transactions on bio-medical engineering, Jan 27, 2017

Non-manual human-machine interfaces (HMIs) have been studied for wheelchair control with the aim ... more Non-manual 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. Thirteen flashing buttons are presented in the graphical user interface (GUI), and each of the buttons corresponds to a command. These buttons flash on a one-by-one manner in a pre-defined 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. For healthy subjects/patients with spinal cord injuries (SCIs), the proposed HMI achieved...

2016 12th World Congress on Intelligent Control and Automation (WCICA), 2016

P300, which is usually evoked by visual stimulus, is widely used in electroencephalography (EEG) ... more P300, which is usually evoked by visual stimulus, is widely used in electroencephalography (EEG) based brain computer interface (BCI) studies. As an application-oriented BCI study, the P300 speller would inevitably be used in outdoor environments. However, the visual stimulation effect might be interfered by the reflections in outdoor environment. This paper attempted to improve the outdoor P300 speller performance by using polarizer. Six subjects participated our two experiments, each experiment was conducted three times under three different conditions. The results demonstrated that a suitable polarizer can indeed improve the performance of outdoor P300 speller.