Electrooculography based electronic communication device for individuals with ALS

Larson, Alex; Herrera, Joshua; George, Kiran; Matthews, Aaron

doi:10.1109/sas.2017.7894062

Cited by 22 publications

(9 citation statements)

References 2 publications

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“…The development of an Analog Front-End (AFE) for EOG recording is a challenging task and an active area of research; however, most work has not been developed or marketed as the BlueGain bioamplifier from Cambridge Research Systems [16] for a general purpose. The acquisition stage is usually oriented to a specific application, mainly for the control of human-computer interfaces, and oriented to individuals with amyotrophic lateral sclerosis [17,18]. Furthermore, most of these studies that can be found in the literature do not justify the design criteria followed in the proposed acquisition module.…”

Section: Figurementioning

confidence: 99%

High-Performance Analog Front-End (AFE) for EOG Systems

et al. 2020

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Electrooculography is a technique for measuring the corneo-retinal standing potential of the human eye. The resulting signal is called the electrooculogram (EOG). The primary applications are in ophthalmological diagnosis and in recording eye movements to develop simple human–machine interfaces (HCI). The electronic circuits for EOG signal conditioning are well known in the field of electronic instrumentation; however, the specific characteristics of the EOG signal make a careful electronic design necessary. This work is devoted to presenting the most important issues related to the design of an EOG analog front-end (AFE). In this respect, it is essential to analyze the possible sources of noise, interference, and motion artifacts and how to minimize their effects. Considering these issues, the complete design of an AFE for EOG systems is reported in this work.

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Section: Figurementioning

confidence: 99%

High-Performance Analog Front-End (AFE) for EOG Systems

et al. 2020

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“…In recent years, there has been much research on control systems for people with ALS (amyotrophic lateral sclerosis) and other severe physical disabilities that use biological information such as eye movements, EEG, and EMG directly for HCI, without the use of hands and feet [6][7][8][9][10][11][12]. As some particular interest research works are studies on eye gaze input and eye blink, etc., which transmit information through the human eye.…”

Section: Introductionmentioning

confidence: 99%

A Human-Computer Control System Based on Intelligent Recognition of Eye Movements and Its Application in Wheelchair Driving

Luo

Cao

Ishikawa

et al. 2021

MTI

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This paper presents a practical human-computer interaction system for wheelchair motion through eye tracking and eye blink detection. In this system, the pupil in the eye image has been extracted after binarization, and the center of the pupil was localized to capture the trajectory of eye movement and determine the direction of eye gaze. Meanwhile, convolutional neural networks for feature extraction and classification of open-eye and closed-eye images have been built, and machine learning was performed by extracting features from multiple individual images of open-eye and closed-eye states for input to the system. As an application of this human-computer interaction control system, experimental validation was carried out on a modified wheelchair and the proposed method proved to be effective and reliable based on the experimental results.

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“…The brain-computer interface (BCI) is a modern technology used for communication between humans and external devices via brain signals [ 1 , 2 ]. BCIs are widely and continuously used in many kinds of applications, such as biometric, prevention, economic, education, sports, and medical applications, including diagnosis, assistive technology, and rehabilitation [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. BCIs can be divided into invasive and noninvasive BCIs according to the acquisition technique.…”

Section: Introductionmentioning

confidence: 99%

“…After that, they generated an estimate by themselves without receiving feedback and then compared the two values. The subjects accurately predicted the effectiveness of motor imagery-based BCIs [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Exploration of illusory visual motion stimuli: An EEG-based brain-computer interface for practical assistive communication systems

Punsawad

Siribunyaphat

Wongsawat

2021

Heliyon

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This paper presents an illusory visual motion stimulus-based brain-computer interface (BCI). We aim to use the proposed system to enhance the motor imagery (MI) modality. Since motor imagery requires a long time for training, a stimulation method with external stimuli through the sensory system is an alternative method for increasing efficiency. The research is divided into two parts. First, we observed the visual motion illusion pattern based on brain topographic maps for the novel BCI modality. Second, we implemented the illusory visual motion stimulus-based BCI system. Arrow and moving-arrow patterns were used to modulate alpha rhythms at the visual and motor cortex. The arrow pattern had an average classification accuracy of approximately 78.5%. Additionally, illusory visual motion stimulus-based BCI systems are proposed using the proposed feature extraction and decision-making algorithm. This proposed BCI system can control the cursor moving in the left or right direction with the designed algorithm to create five commands for assistive communication. Ten volunteers participated in the experiment, and a brain-computer interface system with motor imagery and an illusory visual motion stimulus were used to compare efficiencies. The results showed that the proposed method achieved approximately 4% higher accuracy than motor imagery. The accuracy of the proposed illusory visual motion stimulus and algorithm was approximately 80.3%. Therefore, an illusory visual motion stimulus hybrid BCI system can be incorporated into the MI-based BCI system for beginner motor imagery. Based on the results, the proposed assistive communication system can be used to enhance communication in people with severe disabilities.

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Electrooculography based electronic communication device for individuals with ALS

Cited by 22 publications

References 2 publications

High-Performance Analog Front-End (AFE) for EOG Systems

High-Performance Analog Front-End (AFE) for EOG Systems

A Human-Computer Control System Based on Intelligent Recognition of Eye Movements and Its Application in Wheelchair Driving

Exploration of illusory visual motion stimuli: An EEG-based brain-computer interface for practical assistive communication systems

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