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Sensors (Basel, Switzerland) Jun 2022Electro-oculography (EOG)-based brain-computer interface (BCI) is a relevant technology influencing physical medicine, daily life, gaming and even the aeronautics field.... (Review)
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.
Topics: Algorithms; Brain-Computer Interfaces; Computers; Electroencephalography; Electrooculography; Eye Movements; Humans; User-Computer Interface
PubMed: 35808414
DOI: 10.3390/s22134914 -
Sensors (Basel, Switzerland) Jun 2019Eye movements generate electric signals, which a user can employ to control his/her environment and communicate with others. This paper presents a review of previous... (Review)
Review
Eye movements generate electric signals, which a user can employ to control his/her environment and communicate with others. This paper presents a review of previous studies on such electric signals, that is, electrooculograms (EOGs), from the perspective of human-computer interaction (HCI). EOGs represent one of the easiest means to estimate eye movements by using a low-cost device, and have been often considered and utilized for HCI applications, such as to facilitate typing on a virtual keyboard, moving a mouse, or controlling a wheelchair. The objective of this study is to summarize the experimental procedures of previous studies and provide a guide for researchers interested in this field. In this work the basic characteristics of EOGs, associated measurements, and signal processing and pattern recognition algorithms are briefly reviewed, and various applications reported in the existing literature are listed. It is expected that EOGs will be a useful source of communication in virtual reality environments, and can act as a valuable communication tools for people with amyotrophic lateral sclerosis.
Topics: Algorithms; Amyotrophic Lateral Sclerosis; Communication Aids for Disabled; Electrooculography; Eye Movements; Humans; Pattern Recognition, Automated; Signal Processing, Computer-Assisted; User-Computer Interface
PubMed: 31207949
DOI: 10.3390/s19122690 -
Micromachines Nov 2021All human activity is associated with the generation of electrical signals. These signals are collectively referred to as electrical physiology (EP) signals (e.g.,... (Review)
Review
All human activity is associated with the generation of electrical signals. These signals are collectively referred to as electrical physiology (EP) signals (e.g., electrocardiogram, electroencephalogram, electromyography, electrooculography, etc.), which can be recorded by electrodes. EP electrodes are not only widely used in the study of primary diseases and clinical practice, but also have potential applications in wearable electronics, human-computer interface, and intelligent robots. Various technologies are required to achieve such goals. Among these technologies, adhesion and stretchable electrode technology is a key component for rapid development of high-performance sensors. In last decade, remarkable efforts have been made in the development of flexible and high-adhesive EP recording systems and preparation technologies. Regarding these advancements, this review outlines the design strategies and related materials for flexible and adhesive EP electrodes, and briefly summarizes their related manufacturing techniques.
PubMed: 34945355
DOI: 10.3390/mi12121505 -
Dynamic cortical and tractography atlases of proactive and reactive alpha and high-gamma activities.Brain Communications 2023Alpha waves-posterior dominant rhythms at 8-12 Hz reactive to eye opening and closure-are among the most fundamental EEG findings in clinical practice and research...
Alpha waves-posterior dominant rhythms at 8-12 Hz reactive to eye opening and closure-are among the most fundamental EEG findings in clinical practice and research since Hans Berger first documented them in the early 20th century. Yet, the exact network dynamics of alpha waves in regard to eye movements remains unknown. High-gamma activity at 70-110 Hz is also reactive to eye movements and a summary measure of local cortical activation supporting sensorimotor or cognitive function. We aimed to build the first-ever brain atlases directly visualizing the network dynamics of eye movement-related alpha and high-gamma modulations, at cortical and white matter levels. We studied 28 patients (age: 5-20 years) who underwent intracranial EEG and electro-oculography recordings. We measured alpha and high-gamma modulations at 2167 electrode sites outside the seizure onset zone, interictal spike-generating areas and MRI-visible structural lesions. Dynamic tractography animated white matter streamlines modulated significantly and simultaneously beyond chance, on a millisecond scale. , significant alpha augmentation occurred at the occipital and frontal cortices. , alpha-based functional connectivity was strengthened, while high gamma-based connectivity was weakened extensively in both intra-hemispheric and inter-hemispheric pathways involving the central visual areas. The inferior fronto-occipital fasciculus supported the strengthened alpha co-augmentation-based functional connectivity between occipital and frontal lobe regions, whereas the posterior corpus callosum supported the inter-hemispheric functional connectivity between the occipital lobes. , significant high-gamma augmentation and alpha attenuation occurred at occipital, fusiform and inferior parietal cortices. High gamma co-augmentation-based functional connectivity was strengthened, whereas alpha-based connectivity was weakened in the posterior inter-hemispheric and intra-hemispheric white matter pathways involving central and peripheral visual areas. Our results do not support the notion that eye closure-related alpha augmentation uniformly reflects feedforward or feedback rhythms propagating from lower to higher order visual cortex, or vice versa. Rather, and alpha waves involve extensive, distinct white matter networks that include the frontal lobe cortices, along with low- and high-order visual areas. High-gamma co-attenuation coupled to alpha co-augmentation in shared brain circuitry after eye closure supports the notion of an idling role for alpha waves during eye closure. These normative dynamic tractography atlases may improve understanding of the significance of EEG alpha waves in assessing the functional integrity of brain networks in clinical practice; they also may help elucidate the effects of eye movements on task-related brain network measures observed in cognitive neuroscience research.
PubMed: 37228850
DOI: 10.1093/braincomms/fcad111 -
Biosensors Nov 2022Wearable devices are being developed faster and applied more widely. Wearables have been used to monitor movement-related physiological indices, including heartbeat,...
Wearable devices are being developed faster and applied more widely. Wearables have been used to monitor movement-related physiological indices, including heartbeat, movement, and other exercise metrics, for health purposes. People are also paying more attention to mental health issues, such as stress management. Wearable devices can be used to monitor emotional status and provide preliminary diagnoses and guided training functions. The nervous system responds to stress, which directly affects eye movements and sweat secretion. Therefore, the changes in brain potential, eye potential, and cortisol content in sweat could be used to interpret emotional changes, fatigue levels, and physiological and psychological stress. To better assess users, stress-sensing devices can be integrated with applications to improve cognitive function, attention, sports performance, learning ability, and stress release. These application-related wearables can be used in medical diagnosis and treatment, such as for attention-deficit hyperactivity disorder (ADHD), traumatic stress syndrome, and insomnia, thus facilitating precision medicine. However, many factors contribute to data errors and incorrect assessments, including the various wearable devices, sensor types, data reception methods, data processing accuracy and algorithms, application reliability and validity, and actual user actions. Therefore, in the future, medical platforms for wearable devices and applications should be developed, and product implementations should be evaluated clinically to confirm product accuracy and perform reliable research.
Topics: Humans; Reproducibility of Results; Wearable Electronic Devices; Biosensing Techniques; Athletic Performance; Sweat; Monitoring, Physiologic
PubMed: 36551064
DOI: 10.3390/bios12121097 -
Clinical Neurophysiology : Official... Oct 2021We clarified the clinical and mechanistic significance of physiological modulations of high-frequency broadband cortical activity associated with spontaneous saccadic...
OBJECTIVE
We clarified the clinical and mechanistic significance of physiological modulations of high-frequency broadband cortical activity associated with spontaneous saccadic eye movements during a resting state.
METHODS
We studied 30 patients who underwent epilepsy surgery following extraoperative electrocorticography and electrooculography recordings. We determined whether high-gamma activity at 70-110 Hz preceding saccade onset would predict upcoming ocular behaviors. We assessed how accurately the model incorporating saccade-related high-gamma modulations would localize the primary visual cortex defined by electrical stimulation.
RESULTS
The dynamic atlas demonstrated transient high-gamma suppression in the striatal cortex before saccade onset and high-gamma augmentation subsequently involving the widespread posterior brain regions. More intense striatal high-gamma suppression predicted the upcoming saccade directed to the ipsilateral side and lasting longer in duration. The bagged-tree-ensemble model demonstrated that intense saccade-related high-gamma modulations localized the visual cortex with an accuracy of 95%.
CONCLUSIONS
We successfully animated the neural dynamics supporting saccadic suppression, a principal mechanism minimizing the perception of blurred vision during rapid eye movements. The primary visual cortex per se may prepare actively in advance for massive image motion expected during upcoming prolonged saccades.
SIGNIFICANCE
Measuring saccade-related electrocorticographic signals may help localize the visual cortex and avoid misperceiving physiological high-frequency activity as epileptogenic.
Topics: Adolescent; Child; Child, Preschool; Drug Resistant Epilepsy; Electrocorticography; Female; Gamma Rhythm; Humans; Male; Saccades; Visual Cortex; Young Adult
PubMed: 34454266
DOI: 10.1016/j.clinph.2021.06.020 -
Sensors (Basel, Switzerland) Jun 2020Bioelectrical or electrophysiological signals generated by living cells or tissues during daily physiological activities are closely related to the state of the body and... (Review)
Review
Bioelectrical or electrophysiological signals generated by living cells or tissues during daily physiological activities are closely related to the state of the body and organ functions, and therefore are widely used in clinical diagnosis, health monitoring, intelligent control and human-computer interaction. Ag/AgCl electrodes with wet conductive gels are widely used to pick up these bioelectrical signals using electrodes and record them in the form of electroencephalograms, electrocardiograms, electromyography, electrooculograms, etc. However, the inconvenience, instability and infection problems resulting from the use of gel with Ag/AgCl wet electrodes can't meet the needs of long-term signal acquisition, especially in wearable applications. Hence, focus has shifted toward the study of dry electrodes that can work without gels or adhesives. In this paper, a retrospective overview of the development of dry electrodes used for monitoring bioelectrical signals is provided, including the sensing principles, material selection, device preparation, and measurement performance. In addition, the challenges regarding the limitations of materials, fabrication technologies and wearable performance of dry electrodes are discussed. Finally, the development obstacles and application advantages of different dry electrodes are analyzed to make a comparison and reveal research directions for future studies.
Topics: Electric Conductivity; Electrocardiography; Electrodes; Electroencephalography; Electromyography; Electrooculography; Humans
PubMed: 32610658
DOI: 10.3390/s20133651 -
NPJ Digital Medicine Apr 2021Sleep disorders affect a large portion of the global population and are strong predictors of morbidity and all-cause mortality. Sleep staging segments a period of sleep...
Sleep disorders affect a large portion of the global population and are strong predictors of morbidity and all-cause mortality. Sleep staging segments a period of sleep into a sequence of phases providing the basis for most clinical decisions in sleep medicine. Manual sleep staging is difficult and time-consuming as experts must evaluate hours of polysomnography (PSG) recordings with electroencephalography (EEG) and electrooculography (EOG) data for each patient. Here, we present U-Sleep, a publicly available, ready-to-use deep-learning-based system for automated sleep staging ( sleep.ai.ku.dk ). U-Sleep is a fully convolutional neural network, which was trained and evaluated on PSG recordings from 15,660 participants of 16 clinical studies. It provides accurate segmentations across a wide range of patient cohorts and PSG protocols not considered when building the system. U-Sleep works for arbitrary combinations of typical EEG and EOG channels, and its special deep learning architecture can label sleep stages at shorter intervals than the typical 30 s periods used during training. We show that these labels can provide additional diagnostic information and lead to new ways of analyzing sleep. U-Sleep performs on par with state-of-the-art automatic sleep staging systems on multiple clinical datasets, even if the other systems were built specifically for the particular data. A comparison with consensus-scores from a previously unseen clinic shows that U-Sleep performs as accurately as the best of the human experts. U-Sleep can support the sleep staging workflow of medical experts, which decreases healthcare costs, and can provide highly accurate segmentations when human expertize is lacking.
PubMed: 33859353
DOI: 10.1038/s41746-021-00440-5 -
Micromachines Aug 2022Sleep is crucial for human health from metabolic, mental, emotional, and social points of view; obtaining good sleep in terms of quality and duration is fundamental for... (Review)
Review
Sleep is crucial for human health from metabolic, mental, emotional, and social points of view; obtaining good sleep in terms of quality and duration is fundamental for maintaining a good life quality. Over the years, several systems have been proposed in the scientific literature and on the market to derive metrics used to quantify sleep quality as well as detect sleep disturbances and disorders. In this field, wearable systems have an important role in the discreet, accurate, and long-term detection of biophysical markers useful to determine sleep quality. This paper presents the current state-of-the-art wearable systems and software tools for sleep staging and detecting sleep disorders and dysfunctions. At first, the paper discusses sleep's functions and the importance of monitoring sleep to detect eventual sleep disturbance and disorders. Afterward, an overview of prototype and commercial headband-like wearable devices to monitor sleep is presented, both reported in the scientific literature and on the market, allowing unobtrusive and accurate detection of sleep quality markers. Furthermore, a survey of scientific works related the effect of the COVID-19 pandemic on sleep functions, attributable to both infection and lifestyle changes. In addition, a survey of algorithms for sleep staging and detecting sleep disorders is introduced based on an analysis of single or multiple biosignals (EEG-electroencephalography, ECG-electrocardiography, EMG-electromyography, EOG-electrooculography, etc.). Lastly, comparative analyses and insights are provided to determine the future trends related to sleep monitoring systems.
PubMed: 36014257
DOI: 10.3390/mi13081335 -
Frontiers in Immunology 2021Anti-IgLON5 disease forms an interface between neuroinflammation and neurodegeneration and includes clinical phenotypes that are often similar to those of...
OBJECTIVE
Anti-IgLON5 disease forms an interface between neuroinflammation and neurodegeneration and includes clinical phenotypes that are often similar to those of neurodegenerative diseases. An early diagnosis of patients with anti-IgLON5 disease and differentiation from neurodegenerative diseases is necessary and may have therapeutic implications.
METHODS
In our small sample size study we investigated oculomotor function as a differentiating factor between anti-IgLON5 disease and neurodegenerative disorders. We examined ocular motor and vestibular function in four patients suffering from anti-IgLON5 disease using video-oculography (VOG) and a computer-controlled rotational chair system (sampling rate 60 Hz) and compared the data with those from ten age-matched patients suffering from progressive supranuclear palsy (PSP) and healthy controls (CON).
RESULTS
Patients suffering from anti-IgLON5 disease differed from PSP most strikingly in terms of saccade velocity and accuracy, the presence of square wave jerks (SWJ) (anti-IgLON5 0/4 . PSP 9/10) and the clinical finding of supranuclear gaze palsy (anti-IgLON5 1/4). The presence of nystagmus, analysis of smooth pursuit eye movements, VOR and VOR suppression was reliable to differentiate between the two disease entities. Clear differences in all parameters, although not always significant, were found between all patients and CON.
DISCUSSION
We conclude that the use of VOG as a tool for clinical neurophysiological assessment can be helpful in differentiating between patients with PSP and patients with anti-IgLON5 disease. VOG could have particular value in patients with suspected PSP and lack of typical Parkinson's characteristics. future trials are indispensable to assess the potential of oculomotor function as a biomarker in anti-IgLON5 disease.
Topics: Aged; Autoantibodies; Autoantigens; Autoimmune Diseases of the Nervous System; Cell Adhesion Molecules, Neuronal; Diagnosis, Differential; Electrooculography; Eye-Tracking Technology; Female; Humans; Male; Middle Aged; Neurodegenerative Diseases; Neuroinflammatory Diseases; Nystagmus, Pathologic; Ocular Motility Disorders; Phenotype; Reflex, Abnormal; Reflex, Vestibulo-Ocular; Retrospective Studies; Saccades; Supranuclear Palsy, Progressive; Video Recording
PubMed: 34659261
DOI: 10.3389/fimmu.2021.753856