Volitional and Real-Time Control Cursor Based on Eye Movement Decoding Using a Linear Decoding Model

Zhang, Jinhua; Wang, Baozeng; Zhang, Cheng; Hong, Jun

doi:10.1155/2016/4069790

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…The aim of mMHI combining two or more user modes such as eye movements, hand gestures, and motor imagery in a coordinated approach is to increase the number of instructions and enhance classification accuracy, reduce errors, and overcome the specific disadvantages of each individual mode of BCI (Amiri et al, 2013; Zhang et al, 2016). For example, Edlinger et al introduced a system employing real-time analysis of EEG, EMG, EOG, and motion sensors to implement three different types of navigation optimally suited to a user's needs for a specific control task.…”

Section: Introductionmentioning

confidence: 99%

An EEG/EMG/EOG-Based Multimodal Human-Machine Interface to Real-Time Control of a Soft Robot Hand

et al. 2019

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Brain-computer interface (BCI) technology shows potential for application to motor rehabilitation therapies that use neural plasticity to restore motor function and improve quality of life of stroke survivors. However, it is often difficult for BCI systems to provide the variety of control commands necessary for multi-task real-time control of soft robot naturally. In this study, a novel multimodal human-machine interface system (mHMI) is developed using combinations of electrooculography (EOG), electroencephalography (EEG), and electromyogram (EMG) to generate numerous control instructions. Moreover, we also explore subject acceptance of an affordable wearable soft robot to move basic hand actions during robot-assisted movement. Six healthy subjects separately perform left and right hand motor imagery, looking-left and looking-right eye movements, and different hand gestures in different modes to control a soft robot in a variety of actions. The results indicate that the number of mHMI control instructions is significantly greater than achievable with any individual mode. Furthermore, the mHMI can achieve an average classification accuracy of 93.83% with the average information transfer rate of 47.41 bits/min, which is entirely equivalent to a control speed of 17 actions per minute. The study is expected to construct a more user-friendly mHMI for real-time control of soft robot to help healthy or disabled persons perform basic hand movements in friendly and convenient way.

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Section: Introductionmentioning

confidence: 99%

An EEG/EMG/EOG-Based Multimodal Human-Machine Interface to Real-Time Control of a Soft Robot Hand

et al. 2019

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“…When we try to pick these signals from our body, many artifacts or noises are present in the signal, which makes the signal-to-noise ratio poor. Among these signals, the electrooculogram is the most straightforward bioelectrical signal with a high signal-to-noise ratio and can be used to control several applications [2,3].…”

Section: Introductionmentioning

confidence: 99%

Study of electrooculography signal acquisition sites for assistive device applications

Raj¹

2021

IJATEE

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Electrooculography (EOG) is a technique that involves the measurement of the corneo-retinal standing potential of the eye. The human eye acts as a dipole between the cornea (positive potential) and the retina (negative potential), creating an electric field around the eyeball. The resulting electric signal obtained from this field is called electrooculogram. These signals, generated by eye movements, could be measured by employing different electrode placement configurations for the acquisition. The properties of these signals change depending on the number and placement of the electrodes. The study conducted here describes EOG signal acquisition using new electrode placement configurations that employ fewer facial electrodes placed on the patient. Three pre-gelled disposable electrodes were utilized for this purpose. Only one electrode was placed on a facial location, enhancing patient comfort during the acquisition procedure. To support this study, a lowcost signal acquisition hardware was developed. Using active filtering and amplification, appropriate signal processing techniques were executed upon the horizontal EOG signal acquired to reduce noise and interference due to external conditions. Hence, this paper presents the findings of new electrode placement sites for the acquisition of EOG signals which could be used for assistive device applications while restricting the number of facial electrodes to one. Most of the studies regarding the EOG signal acquisition had been using all electrodes in the face region. In contrast, we reduced the number of electrodes in the face region, thereby providing patient comfort. The comparison was made mainly on the acquired data from these new locations to discover the configuration with the optimal signal response using data obtainedfrom seven healthy subjects. The amplitude values were being compared from the new locations with the standard acquisition sites. The findings of this study were found to have a productive result. The total gain of the system required for new electrode placement configurations was two times more than the total gain required for standard acquisition sites, and also, the amplitude was less but can be helpful for assistive device applications. The average peak to peak amplitude value of the EOG signal for the new site came around about 1.25 volt.

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Development of an electrooculogram-based human-computer interface using involuntary eye movement by spatially rotating sound for communication of locked-in patients

Kim

Han

2018

Sci Rep

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Individuals who have lost normal pathways for communication need augmentative and alternative communication (AAC) devices. In this study, we propose a new electrooculogram (EOG)-based human-computer interface (HCI) paradigm for AAC that does not require a user’s voluntary eye movement for binary yes/no communication by patients in locked-in state (LIS). The proposed HCI uses a horizontal EOG elicited by involuntary auditory oculogyric reflex, in response to a rotating sound source. In the proposed HCI paradigm, a user was asked to selectively attend to one of two sound sources rotating in directions opposite to each other, based on the user’s intention. The user’s intentions could then be recognised by quantifying EOGs. To validate its performance, a series of experiments was conducted with ten healthy subjects, and two patients with amyotrophic lateral sclerosis (ALS). The online experimental results exhibited high-classification accuracies of 94% in both healthy subjects and ALS patients in cases where decisions were made every six seconds. The ALS patients also participated in a practical yes/no communication experiment with 26 or 30 questions with known answers. The accuracy of the experiments with questionnaires was 94%, demonstrating that our paradigm could constitute an auxiliary AAC system for some LIS patients.

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Volitional and Real-Time Control Cursor Based on Eye Movement Decoding Using a Linear Decoding Model

Cited by 5 publications

References 28 publications

An EEG/EMG/EOG-Based Multimodal Human-Machine Interface to Real-Time Control of a Soft Robot Hand

An EEG/EMG/EOG-Based Multimodal Human-Machine Interface to Real-Time Control of a Soft Robot Hand

Study of electrooculography signal acquisition sites for assistive device applications

Development of an electrooculogram-based human-computer interface using involuntary eye movement by spatially rotating sound for communication of locked-in patients

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