Convolutional Neural Network for Imagine Movement Classification for Neurorehabilitation of Upper Extremities Using Low-Frequency EEG Signals for Spinal Cord Injury

Gualsaquí, Mario G.; Delgado, Alejandro S.; González, Lady L.; Vaca, Giovana F.; Almeida-Galárraga, Diego; Salum, Graciela Marisa; Cadena-Morejón, Carolina; Tirado-Espín, Andrés; Villalba-Meneses, Fernando

doi:10.1007/978-3-030-99170-8_20

Cited by 7 publications

(2 citation statements)

References 46 publications

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“…On the other hand, BCI applications based on MI are classified into two main groups, medical and non-medical applications. Medical applications focus on restoring motor functions lost during spinal cord injury through stimulation for brain regeneration and plasticity [ 28 ], another medical application is the substitution of movement through BCI for control or communication, such is the case of [ 29 ]. On the other hand, non-medical applications are used to increase human capabilities and entertainment; such as the case of control of robotic arms and exoskeletons [ 30 ], control of ground vehicles [ 31 ], drones [ 32 ], and games in virtual environments [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI

Rodríguez-Azar,

Mejía-Muñoz,

Cruz-Mejía

et al. 2023

Sensors

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Brain-computer interfaces use signals from the brain, such as EEG, to determine brain states, which in turn can be used to issue commands, for example, to control industrial machinery. While Cloud computing can aid in the creation and operation of industrial multi-user BCI systems, the vast amount of data generated from EEG signals can lead to slow response time and bandwidth problems. Fog computing reduces latency in high-demand computation networks. Hence, this paper introduces a fog computing solution for BCI processing. The solution consists in using fog nodes that incorporate machine learning algorithms to convert EEG signals into commands to control a cyber-physical system. The machine learning module uses a deep learning encoder to generate feature images from EEG signals that are subsequently classified into commands by a random forest. The classification scheme is compared using various classifiers, being the random forest the one that obtained the best performance. Additionally, a comparison was made between the fog computing approach and using only cloud computing through the use of a fog computing simulator. The results indicate that the fog computing method resulted in less latency compared to the solely cloud computing approach.

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Section: Introductionmentioning

confidence: 99%

Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI

Rodríguez-Azar,

Mejía-Muñoz,

Cruz-Mejía

et al. 2023

Sensors

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“…Keratoconus is an ocular pathology that occurs in the cornea of the eye, produces image distortion, and progressively reduces vision. It is a bilateral, non-inflammatory corneal ectasia that causes myopic astigmatism and decreased visual acuity [4]. This degenerative disease arises from a constant thinning of the thickness of the cornea in the central and paracentral areas, in addition to a change in the normal spherical curvature of the eye, thus creating a cone-shaped cornea [5].…”

Section: Introductionmentioning

confidence: 99%

Biomechanical Study of the Eye with Keratoconus-Type Corneal Ectasia Using a 3D Geometric Model

Sánchez-Real,

Otuna-Hernández,

Fajardo-Cabrera

et al. 2023

Applied Sciences

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Keratoconus is an eye disease that distorts the shape of the cornea. This study aimed to analyze the effect of an increase in intraocular pressure applied to eyes with different severity of keratoconus disease using patient-specific models. Finite element models of the normal eye, eye with keratoconus, and eye with keratoglobus were constructed. The loading conditions considered the intraocular pressure increment as well as their physiological intraocular pressure. The analysis was performed with distinct materials for normal and keratoconic eyes. The finite element analysis revealed differences in the three models in terms of their deformation and maximum principal stress, and differences were observed in corneal curvature and thickness. These findings could enhance research in the biomechanical area, leading to more successful treatment options and a more individualized approach in the field of practical ophthalmology. Further investigation with larger sample sizes and more precise data on eye material would allow us to evaluate whether these disparities could inform the diagnosis of keratoconus.

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