EOG-Based Human–Computer Interface: 2000–2020 Review

Electro-oculography (EOG)-based brain-computer interface (BCI) is a relevant technology influencing physical medicine, daily life, gaming and even the aeronautics field. EOG-based BCI systems record activity related to users' intention, perception and motor decisions. It converts the bio-physiological signals into commands for external hardware, and it executes the operation expected by the user through the output device. EOG signal is used for identifying and classifying eye movements through active or passive interaction. Both types of interaction have the potential for controlling the output device by performing the user's communication with the environment. In the aeronautical field, investigations of EOG-BCI systems are being explored as a relevant tool to replace the manual command and as a communicative tool dedicated to accelerating the user's intention. This paper reviews the last two decades of EOG-based BCI studies and provides a structured design space with a large set of representative papers. Our purpose is to introduce the existing BCI systems based on EOG signals and to inspire the design of new ones. First, we highlight the basic components of EOG-based BCI studies, including EOG signal acquisition, EOG device particularity, extracted features, translation algorithms, and interaction commands. Second, we provide an overview of EOG-based BCI applications in the real and virtual environment along with the aeronautical application. We conclude with a discussion of the actual limits of EOG devices regarding existing systems. Finally, we provide suggestions to gain insight for future design inquiries

EOG kontrollü çok yönlü tekerlekli sandalye

xx, 33 sayfa: şekil29 cm. 1 CDÖZETBu tezde, EOG sinyalleri kullanılarak hareket yeteneği kısıtlı engelli bireyler için gerçek zamanlı çalışmalara uygulanması kolay bir tekerlekli sandalye kontrol algoritması geliştirilmiştir.Bu çalışmada, yaşları 20-26 arasında değişen 26 bireyden EOG sinyalleri alınmıştır. EOG sinyalleri olarak yatay EOG, sağ göz düşey EOG ve sol göz düşey EOG sinyalleri kayıt edilmiştir. Daha sonra her bir EOG kanalı 150 ms’lik medyan süzgecine tabi tutulmuştur. Böylece istemsiz yapılan göz kırpmaları ve çeşitli diğer gürültüler sinyalden arındırılabilmiştir. Geliştirilen algoritmaların girişine, her deneğin kendi EOG sinyallerinin maksimum ve minimum değerlerinin orta değerinin yarısı eşik değer olacak şekilde uygulanmıştır.ABSTRACTIn this thesis, a wheelchair control algorithm that is easy to apply to real-time studies has been developed for restricted individuals with disabilities by using EOG signals.In this study, EOG signals were acquired from 26 individuals with the age between 20 and 26 years. EOG signals of horizontal EOG, vertical EOG of the right eye, and vertical EOG of the left eye were recorded. Medial filter with 150-ms duration was applied to all EOG channels. Hence, involuntary blinkings and various other noises were filtered from the signal. The mid-value of the maximum and minimum values of each subject's EOG signals are determined as the threshold value that was applied to the input of provided algorithms

Securing teleoperated robot: Classifying human operator identity and emotion through motion-controlled robotic behaviors

Teleoperated robotic systems allow human operators to control robots from a distance, which mitigates the constraints of physical distance between the operators and offers invaluable applications in the real world. However, the security of these systems is a critical concern. System attacks and the potential impact of operators’ inappropriate emotions can result in misbehavior of the remote robots, which poses risks to the remote environment. These concerns become particularly serious when performing mission-critical tasks, such as nuclear cleaning. This thesis explored innovative security methods for the teleoperated robotic system. Common methods of security that can be used for teleoperated robots include encryption, robot misbehavior detection and user authentication. However, they have limitations for teleoperated robot systems. Encryption adds communication overheads to the systems. Robot misbehavior detection can only detect unusual signals on robot devices. The user authentication method secured the system primarily at the access point. To address this, we built motioncontrolled robot platforms that allow for robot teleoperation and proposed methods of performing user classification directly on remote-controlled robotic behavioral data to enhance security integrity throughout the operation. We discussed in Chapter 3 and conducted 4 experiments. Experiments 1 and 2 demonstrated the effectiveness of our approach, achieving user classification accuracy of 95% and 93% on two platforms respectively, using motion-controlled robotic end-effector trajectories. The results in experiment 3 further indicated that control system performance directly impacts user classification efficacy. Additionally, we deployed an AI agent to protect user biometric identities, ensuring the robot’s actions do not compromise user privacy in the remote environment in experiment 4. This chapter provided a foundation of methodology and experiment design for the next work. Additionally, Operators’ emotions could pose a security threat to the robot system. A remote robot operator’s emotions can significantly impact the resulting robot’s motions leading to unexpected consequences, even when the user follows protocol and performs permitted tasks. The recognition of a user operator’s emotions in remote robot control scenarios is, however, under-explored. Emotion signals mainly are physiological signals, semantic information, facial expressions and bodily movements. However, most physiological signals are electrical signals and are vulnerable to motion artifacts, which can not acquire the accurate signal and is not suitable for teleoperated robot systems. Semantic information and facial expressions are sometimes not accessible and involve high privacy issues and add additional sensors to the teleoperated systems. We proposed the methods of emotion recognition through the motion-controlled robotic behaviors in Chapter 4. This work demonstrated for the first time that the motioncontrolled robotic arm can inherit human operators’ emotions and emotions can be classified through robotic end-effector trajectories, achieving an 83.3% accuracy. We developed two emotion recognition algorithms using Dynamic Time Warping (DTW) and Convolutional Neural Network (CNN), deriving unique emotional features from the avatar’s end-effector motions and joint spatial-temporal characteristics. Additionally, we demonstrated through direct comparison that our approach is more appropriate for motion-based telerobotic applications than traditional ECG-based methods. Furthermore, we discussed the implications of this system on prominent current and future remote robot operations and emotional robotic contexts. By integrating user classification and emotion recognition into teleoperated robotic systems, this thesis lays the groundwork for a new security paradigm that enhances both the safety of remote operations. Recognizing users and their emotions allows for more contextually appropriate robot responses, potentially preventing harm and improving the overall quality of teleoperated interactions. These advancements contribute significantly to the development of more adaptive, intuitive, and human-centered HRI applications, setting a precedent for future research in the field

Multimodal interactions in virtual environments using eye tracking and gesture control.

Multimodal interactions provide users with more natural ways to interact with virtual environments than using traditional input methods. An emerging approach is gaze modulated pointing, which enables users to perform virtual content selection and manipulation conveniently through the use of a combination of gaze and other hand control techniques/pointing devices, in this thesis, mid-air gestures. To establish a synergy between the two modalities and evaluate the affordance of this novel multimodal interaction technique, it is important to understand their behavioural patterns and relationship, as well as any possible perceptual conflicts and interactive ambiguities. More specifically, evidence shows that eye movements lead hand movements but the question remains that whether the leading relationship is similar when interacting using a pointing device. Moreover, as gaze modulated pointing uses different sensors to track and detect user behaviours, its performance relies on users perception on the exact spatial mapping between the virtual space and the physical space. It raises an underexplored issue that whether gaze can introduce misalignment of the spatial mapping and lead to users misperception and interactive errors. Furthermore, the accuracy of eye tracking and mid-air gesture control are not comparable with the traditional pointing techniques (e.g., mouse) yet. This may cause pointing ambiguity when fine grainy interactions are required, such as selecting in a dense virtual scene where proximity and occlusion are prone to occur. This thesis addresses these concerns through experimental studies and theoretical analysis that involve paradigm design, development of interactive prototypes, and user study for verification of assumptions, comparisons and evaluations. Substantial data sets were obtained and analysed from each experiment. The results conform to and extend previous empirical findings that gaze leads pointing devices movements in most cases both spatially and temporally. It is testified that gaze does introduce spatial misperception and three methods (Scaling, Magnet and Dual-gaze) were proposed and proved to be able to reduce the impact caused by this perceptual conflict where Magnet and Dual-gaze can deliver better performance than Scaling. In addition, a coarse-to-fine solution is proposed and evaluated to compensate the degradation introduced by eye tracking inaccuracy, which uses a gaze cone to detect ambiguity followed by a gaze probe for decluttering. The results show that this solution can enhance the interaction accuracy but requires a compromise on efficiency. These findings can be used to inform a more robust multimodal inter- face design for interactions within virtual environments that are supported by both eye tracking and mid-air gesture control. This work also opens up a technical pathway for the design of future multimodal interaction techniques, which starts from a derivation from natural correlated behavioural patterns, and then considers whether the design of the interaction technique can maintain perceptual constancy and whether any ambiguity among the integrated modalities will be introduced

EEG-based classification of visual and auditory monitoring tasks

Using EEG signals for mental workload detection has received particular attention in passive BCI research aimed at increasing safety and performance in high-risk and safety-critical occupations, like pilots and air traffic controllers. Along with detecting the level of mental workload, it has been suggested that being able to automatically detect the type of mental workload (e.g., auditory, visual, motor, cognitive) would also be useful. In this work, a novel experimental protocol was developed in which subjects performed a task involving one of two different types of mental workload (specifically, auditory and visual), each under two different levels of task demand (easy and difficult). The tasks were designed to be nearly identical in terms of visual and auditory stimuli, and differed only in the type of stimuli the subject was monitoring/attending to. EEG power spectral features were extracted and used to train linear and non-linear classifiers. Preliminary results on six subjects suggested that the auditory and visual tasks could be distinguished from one another, and individually from a baseline condition (which also contained nearly identical stimuli that the subject did not need to attend to at all), with accuracy significantly exceeding chance. This was true when classification was done within a workload level, and when data from the two workload levels were combined. Preliminary results also showed that tasks with easy and difficult trials could be distinguished from one another, each within a sensory domain (auditory and visual) as well as with both domains combined. Though further investigation is required, these preliminary results are promising, and suggest the feasibility of a passive BCI for detecting both type and level of mental workload

Spatial Displays and Spatial Instruments

The conference proceedings topics are divided into two main areas: (1) issues of spatial and picture perception raised by graphical electronic displays of spatial information; and (2) design questions raised by the practical experience of designers actually defining new spatial instruments for use in new aircraft and spacecraft. Each topic is considered from both a theoretical and an applied direction. Emphasis is placed on discussion of phenomena and determination of design principles

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Life Sciences Program Tasks and Bibliography for FY 1996

This document includes information on all peer reviewed projects funded by the Office of Life and Microgravity Sciences and Applications, Life Sciences Division during fiscal year 1996. This document will be published annually and made available to scientists in the space life sciences field both as a hard copy and as an interactive Internet web page