Human–Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot

Pérez-Reynoso, Francisco; Rodríguez-Guerrero, Liliam; Salgado-Ramírez, Julio César; Ortega-Palacios, R.

doi:10.3390/s21175882

Cited by 20 publications

(13 citation statements)

References 52 publications

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“…Conventional electrodes, wet or dry silver/silver chloride (Ag/AgCl), are generally used to measure EOG signals [ 76 , 77 , 78 , 79 , 80 ]. For example, wet Ag/AgCl electrodes are used for the analysis of various activity recognition fields [ 65 , 81 , 82 ] or HMI controllers [ 37 , 60 , 62 , 83 ]. Dry flat Ag/AgCl electrodes are used on various wearable platforms such as eyeglasses [ 35 ], head caps [ 55 ], and goggles [ 34 ].…”

Section: Eog Signalsmentioning

confidence: 99%

Section: Eog Signalsmentioning

confidence: 99%

“…Recent advances in microfabrication and print technologies enabled new ways to design micro-patterned electrodes (gold [ 1 , 30 ] and graphene [ 11 ]). Due to the development of these technologies and 3D printing, designing wearable platforms has become possible, such as eyeglass types [ 5 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ], face mask types [ 7 , 40 , 41 , 42 ], ear plug types [ 43 , 44 , 45 , 46 , 47 ], and headband types [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ] for various applications. Previously, various controllers for an HMI such as wheelchairs [ 1 , 4 , 51 , 52 ], drones [ 11 , 59 ], game interfaces [ 5 , 36 , 47 , 60 , 61 ], and virtual keyboards [ 34 , 38 , 51 , …”

Section: Introductionmentioning

confidence: 99%

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Advances in Materials, Sensors, and Integrated Systems for Monitoring Eye Movements

et al. 2022

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Eye movements show primary responses that reflect humans' voluntary intention and conscious selection. Because visual perception is one of the fundamental sensory interactions in the brain, eye movements contain critical information regarding physical/psychological health, perception, intention, and preference. With the advancement of wearable device technologies, the performance of monitoring eye tracking has been significantly improved. It also has led to myriad applications for assisting and augmenting human activities. Among them, electrooculograms, measured by skin-mounted electrodes, have been widely used to track eye motions accurately. In addition, eye trackers that detect reflected optical signals offer alternative ways without using wearable sensors. This paper outlines a systematic summary of the latest research on various materials, sensors, and integrated systems for monitoring eye movements and enabling human-machine interfaces. Specifically, we summarize recent developments in soft materials, biocompatible materials, manufacturing methods, sensor functions, systems' performances, and their applications in eye tracking. Finally, we discuss the remaining challenges and suggest research directions for future studies.

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Section: Eog Signalsmentioning

confidence: 99%

Section: Eog Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Advances in Materials, Sensors, and Integrated Systems for Monitoring Eye Movements

et al. 2022

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“…Supervised learning, a fundamental branch of machine learning, lies at the heart of many intelligent systems. It offers great advantages in many different domains, including robotics [1], geology [2], security [3], health [4,5], land cover [6], remote sensing [7], industrial applications [8], and environmental monitoring [9]. Supervised learning allows automated machinery to learn from labeled data, where inputs are paired with corresponding outputs.…”

Section: Introductionmentioning

confidence: 99%

A Novel Classification Method: Neighborhood-Based Positive Unlabeled Learning Using Decision Tree (NPULUD)

Ghasemkhani,

Balbal,

Birant

et al. 2024

Entropy

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In a standard binary supervised classification task, the existence of both negative and positive samples in the training dataset are required to construct a classification model. However, this condition is not met in certain applications where only one class of samples is obtainable. To overcome this problem, a different classification method, which learns from positive and unlabeled (PU) data, must be incorporated. In this study, a novel method is presented: neighborhood-based positive unlabeled learning using decision tree (NPULUD). First, NPULUD uses the nearest neighborhood approach for the PU strategy and then employs a decision tree algorithm for the classification task by utilizing the entropy measure. Entropy played a pivotal role in assessing the level of uncertainty in the training dataset, as a decision tree was developed with the purpose of classification. Through experiments, we validated our method over 24 real-world datasets. The proposed method attained an average accuracy of 87.24%, while the traditional supervised learning approach obtained an average accuracy of 83.99% on the datasets. Additionally, it is also demonstrated that our method obtained a statistically notable enhancement (7.74%), with respect to state-of-the-art peers, on average.

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“…Kang et al applied an ensemble network with attention mechanisms [39]. Pérez-Reynoso et al developed an EOG-controlled robotic system [40]. However, gap still exists in how deep learning can further decode complex patterns and how real-time inference can be deployed.…”

Section: Introductionmentioning

confidence: 99%

eyeSay: Brain Visual Dynamics Decoding With Deep Learning & Edge Computing

Zou

Zhang

2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Brain visual dynamics encode rich functional and biological patterns of the neural system, and if decoded, are of great promise for many applications such as intention understanding, cognitive load quantization and neural disorder measurement. We here focus on the understanding of the brain visual dynamics for the Amyotrophic lateral sclerosis (ALS) population, and propose a novel system that allows these so-called 'lock-in' patients to 'speak' with their brain visual movements. More specifically, we propose an intelligent system to decode the eye bio-potential signal, Electrooculogram (EOG), thereby understanding the patients' intention. We first propose to leverage a deep learning framework for automatic feature learning and classification of the brain visual dynamics, aiming to translate the EOG to meaningful words. We afterwards design and develop an edge computing platform on the smart phone, which can execute the deep learning algorithm, visualize the brain visual dynamics, and demonstrate the edge inference results, all in real-time. Evaluated on 4,500 trials of brain visual movements performed by multiple users, our novel system has demonstrated a high eyeword recognition rate up to 90.47%. The system is demonstrated to be intelligent, effective and convenient for decoding brain visual dynamics for ALS patients. This research thus is expected to greatly advance the decoding and understanding of brain visual dynamics, by leveraging machine learning and edge computing innovations.

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Human–Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot

Cited by 20 publications

References 52 publications

Advances in Materials, Sensors, and Integrated Systems for Monitoring Eye Movements

Advances in Materials, Sensors, and Integrated Systems for Monitoring Eye Movements

A Novel Classification Method: Neighborhood-Based Positive Unlabeled Learning Using Decision Tree (NPULUD)

eyeSay: Brain Visual Dynamics Decoding With Deep Learning & Edge Computing

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