BCI System using a Novel Processing Technique Based on Electrodes Selection for Hand Prosthesis Control

Constantine, Alisson; Asanza, Víctor; Loayza, Francis R.; Peláez, Enrique; Peluffo-Ordóñez, Diego H.

doi:10.1016/j.ifacol.2021.10.283

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…Applications based on movement intention detection with unsupervised [11] and supervised [12] Machine Learning (ML) algorithms use EEG signals. Due to the low signalto-noise ratio of these signals, a preprocessing and feature extraction stage is required [13]. However, some studies, such as those carried out by Garcia-Moreno, use Deep Learning algorithms for the detection of movement intentions [14].…”

Section: Introductionmentioning

confidence: 99%

Identification of Lower-Limb Motor Tasks via Brain–Computer Interfaces: A Topical Overview

Asanza

Peláez

Loayza

et al. 2022

Sensors

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Recent engineering and neuroscience applications have led to the development of brain–computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.

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Section: Introductionmentioning

confidence: 99%

Identification of Lower-Limb Motor Tasks via Brain–Computer Interfaces: A Topical Overview

Asanza

Peláez

Loayza

et al. 2022

Sensors

Self Cite

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“…The capabilities of neural networks have been extensively studied in classification applications using bioelectrical signals, such as visual stimulus detection using EEG-SSVEP signals [ 65 ]; detection of imaginative-motor intentions of both hands and both feet, obtaining up to 93.7% of accuracy [ 66 ]; analysis of EMG signals to facilitate real-time, off-line monitoring of a prosthetic hand [ 67 ]; and EMG signal classification using the Wavelet transform in combination with neural networks, obtaining up to 90.7% accuracy [ 68 ].…”

Section: System Designmentioning

confidence: 99%

A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network

Avilés-Mendoza,

Gaibor-León,

Asanza

et al. 2023

Biomimetics

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About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15.

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Acquisition of Motor Images of the Left and Right Hand by Means of the Emotiv EPOC+ Headset for Their Classification Using the EEGNet Neural Network

Castellanos-Delgado,

Gallo-Sánchez,

Gonzalez-Morales

2023

Communications in Computer and Information Science

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BCI System using a Novel Processing Technique Based on Electrodes Selection for Hand Prosthesis Control

Cited by 9 publications

References 17 publications

Identification of Lower-Limb Motor Tasks via Brain–Computer Interfaces: A Topical Overview

Identification of Lower-Limb Motor Tasks via Brain–Computer Interfaces: A Topical Overview

A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network

Acquisition of Motor Images of the Left and Right Hand by Means of the Emotiv EPOC+ Headset for Their Classification Using the EEGNet Neural Network

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