An Eye Tracking and Brain–Computer Interface-Based Human–Environment Interactive System for Amyotrophic Lateral Sclerosis Patients

Wang, Jiaqi; Xu, Shuoyan; Dai, Yanning; Gao, Shuo

doi:10.1109/jsen.2022.3223878

Cited by 6 publications

(3 citation statements)

References 70 publications

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“…A combined BCI and eye-tracking system was presented by Wang et al in 2022 [16]. They aimed to help ALS patients control home appliances using a MindWave Mobile EEG headset that contains one electrode to detect EEG signals; a pair of eye-tracking glasses which are used to detect the gaze point; and an intelligent Human-Computer Interaction (HCI) screen, which can help to control appliances using voice commands.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards Design and Implementation of an EEG-based BCI TV Remote Control

Ali,

Vasilateanu

et al. 2023

Int. J. Onl. Eng.

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BCI is a rapidly growing field within biomedical engineering as it enables a direct connection between the central nervous system and an external device. BCI detects brain signals using biosensors (electrodes) installed on the head’s scalp or implanted inside the brain. EEG is a non-invasive method for detecting and monitoring the brain’s activity. Using EEG-based BCI in the medical field can significantly help disabled people to perform daily activities. In this context, it is very important to support and enable paralysed people to interact with multimedia devices like televisions by developing suitable solutions. This paper proposes an EEG mind-controlled TV remote control system prototype. The proposed prototype uses affordable hardware components to perform its task. A quantitative questionnaire has been conducted to identify the system’s functional and non-functional requirements. The system can send four different signals to power on/off, change the channel, raise and reduce the volume of the TV. The system has been tested by 20 subjects. The testing results show that the accuracy of the system is 74.9%. Despite the system being able to control only four TV functions, the system is scalable, and more commands can be added in the future. Also, using Raspberry Pi in the system gives a great possibility to eliminate the computer and to use Raspberry Pi directly with the headset. This paper demonstrates the approach’s feasibility and opens the route for enhancing the system and using EEG-based BCI with more and different devices.

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Section: Literature Reviewmentioning

confidence: 99%

Towards Design and Implementation of an EEG-based BCI TV Remote Control

Ali,

Vasilateanu

et al. 2023

Int. J. Onl. Eng.

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“…These patients were able to learn eye movement-directed responding to indicate their basic needs, respond to rating scales, as well as respond to quality of life and self-esteem questionnaires [18]. Similarly, eye tracking was used to control household electric appliances by ALS patients [19]. Second, eye movements indicate what the examinee is paying attention to while engaging with a task and offering a glimpse into both cognitive and processes [20,21].…”

Section: Introductionmentioning

confidence: 99%

Integrating the Numerical Pain Rating Scale (NPRS) with an Eye Tracker: Feasibility and Initial Validation

Braw,

Ratmansky,

Goor-Aryeh

2024

Pain Management - From Acute to Chronic and Beyond

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This chapter details the integration of a Numerical Rating Scale (NPRSETI) with a portable eye tracker, enabling the assessment of pain in conditions in which verbal communication and use of hands are limited (e.g., advanced Amyotrophic lateral sclerosis, ALS). After detailing the construction of the NPRSETI, we describe its validation in an outpatient pain clinic. More specifically, thirty chronic pain patients performed the NPRSETI and filled a conventional NPRS (order was pseudo-randomized). Eye movements, including gaze direction and additional eye movement measures (e.g., saccade rate), were recorded, while participants rated their pain using the NPRSETI. The study’s findings indicated no significant differences in pain severity ratings of the NPRSETI and conventional NPRS. Notably, ratings using the two scales were highly correlated (r = 0.99). NPRSETI’s ratings were also strongly associated with participants’ currently experienced pain rating using the Brief Pain Inventory (BPI). The findings provide initial proof of concept for integrating conventional pain rating scales with an eye tracker and validate the NPRSETI compared with the well-validated and commonly used NPRS. Enhanced usability and decreasing costs of eye trackers will ease the additional research mandated to validate these preliminary findings and hopefully advance their integration into clinical practice.

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“…Eye tracking has traditionally been used in wearable AR and VR devices to mitigate 3D fatigue [6], [7] and enable effective human-computer interaction (HCI) [8]. Most research and development in this area focus on using near-infrared (NIR) cameras, especially in AR/VR wearable glasses that are worn closely [9].…”

Section: Introductionmentioning

confidence: 99%

Head Pose-Aware Regression for Pupil Localization From a-Pillar Cameras

Kang,

Kang

2024

IEEE Access

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In vehicular applications, remote eye pupil tracking is essential, particularly for advanced augmented reality (AR) 3D head-up displays (HUDs), and driver monitoring systems (DMS). However, achieving accurate pupil center localization under varied head poses presents significant challenges, especially when cameras are positioned on a vehicle's A-pillar. This placement introduces substantial head pose variations, complicating traditional tracking methods. In response, this study presents a remote pupil localization method designed to address the unique challenges posed by a camera situated on a vehicle's Apillar, a spot causing significant head pose variations. The proposed technique relies on a head pose-aware pupil localization strategy utilizing A-pillar cameras. Our pupil localization algorithm adopts a Transformer regression approach, into which we integrate head pose estimation data, enhancing its capability across diverse head poses. To further enhance our approach, we used an optimized nine-point eye-nose landmark set, to minimize the pupil center localization loss. To demonstrate the robustness of our method, we conducted evaluations using both the public WIDER Facial Landmarks in-the-wild (WFLW) dataset and a custom inhouse dataset focused on A-pillar camera captures. Results indicate a Normalized Mean Error (NME) of 2.79% and a failure rate (FR) of 1.28% on the WFLW dataset. On our in-house dataset, the method achieved an NME of 2.96% and a FR of 0.72%. These impressive results demonstrate the robustness and efficacy of our method, suggesting its potential for implementation in commercial eye tracking systems using A-pillar mounted cameras, especially for AR 3D HUD and DMS applications.INDEX TERMS Pupil center localization, remote eye tracking, head pose-aware pupil regression, eyenose points regression, head pose estimation, A-pillar camera, augmented reality (AR) 3D head-up displays (HUDs), driver monitoring system (DMS).

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An Eye Tracking and Brain–Computer Interface-Based Human–Environment Interactive System for Amyotrophic Lateral Sclerosis Patients

Cited by 6 publications

References 70 publications

Towards Design and Implementation of an EEG-based BCI TV Remote Control

Towards Design and Implementation of an EEG-based BCI TV Remote Control

Integrating the Numerical Pain Rating Scale (NPRS) with an Eye Tracker: Feasibility and Initial Validation

Head Pose-Aware Regression for Pupil Localization From a-Pillar Cameras

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