A Low-Cost Lower-Limb Brain-Machine Interface Triggered by Pedaling Motor Imagery for Post-Stroke Patients Rehabilitation

Romero-Laiseca, Maria Alejandra; Delisle-Rodríguez, Denis; Cardoso, Vivianne; Gurve, Dharmendra; Loterio, Flávia Aparecida; Nascimento, Jorge Henrique Posses; Krishnan, Sridhar; Frizera, Anselmo; Bastos, Teodiano

doi:10.1109/tnsre.2020.2974056

Cited by 53 publications

(45 citation statements)

References 49 publications

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“…A clinical study showed substantial improvement of arm motor function in an MI rehabilitation group, compared to a passive control group [9]. However, the recently proposed lower limb rehabilitation systems were binary (rest vs. imagery) [10], [11]. Bipedal MI training was near to impossible because the foot motor regions are close to each other.…”

Section: Introductionmentioning

confidence: 99%

Global Cortical Network Distinguishes Motor Imagination of the Left and Right Foot

Phang

2020

IEEE Access

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Conventional passive lower limb rehabilitation is suboptimal since the brain is not actively involved in the training. An autonomous motor imagery brain-computer interface (MI-BCI) could potentially improve rehabilitation outcomes. However, motor cortex regions associated with the individual feet are anatomically close to each other. This presents a difficulty in distinguishing the left and right foot MI during rehabilitation therapy. To overcome this difficulty, we extracted functional connectivity to measure the global cortical network via electroencephalography (EEG) signals.

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Section: Introductionmentioning

confidence: 99%

Global Cortical Network Distinguishes Motor Imagination of the Left and Right Foot

Phang

2020

IEEE Access

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“…The Model mean accuracy that has been achieved is 69.00%. On the other hand, tables 5 and 6 give more enhanced results compared with [57] developed model when we applied the DBN algorithm. Ko et al [58] developed a hybrid SSVEP-RSVP BCI model to improve the performance of classifying the target/non-target objects in a multi-target scenario by using 12-EEG channels.…”

Section: B Experimentsmentioning

confidence: 99%

“…Romero-Laiseca et al [57] proposed a BCI model based on EEG for lower-limb motor recovery of post-stroke patients was implemented using Riemannian geometry for feature extraction, Pair-Wise Feature Proximity for feature selection, and LDA for pedaling imagery recognition. The Model mean accuracy that has been achieved is 69.00%.…”

Section: B Experimentsmentioning

confidence: 99%

A Deep Learning Approach for Brain Computer Interaction-Motor Execution EEG Signal Classification

et al. 2021

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Recently Noninvasive Electroencephalogram (EEG) systems are gaining much attention. Brain-computer Interface (BCI) systems rely on EEG analysis to identify the mental state of the user, change in cognitive state and response to the events. Motor Execution (ME) is a very important control paradigm. This paper introduces a robust and useful User-Independent Hybrid Brain-computer Interface (UIHBCI) model to classify signals from fourteen EEG channels that are used to record the reactions of the brain neurons of nine subjects. Through this study the researchers identified relevant multisensory features of multi-channel EEG that represent the specific mental processes depending on two different evaluation models (Audio/Video) and (Male/Female). The Deep Belief Network (DBN) was applied independently on the two models where, the overall achieved classification rates were better in ME classification compared to the state of art. For evaluation four models were tested in addition to the proposed model, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Brain-computer Interface Lower-Limb Motor Recovery (BCI LLMR) and Hybrid Steady-State Visual Evoked Potential Rapid Serial Visual Presentation Brain-computer Interface (Hybrid SSVEP-RSVP BCI). Results indicated the proposed model, LDA, SVM, BCI LLMR and Hybrid SSVEP-RSVP BCI accuracies for (A/V) model are 94.44%, 66.67%, 61.11%, 83.33% and 89.67% respectively, while for (M/F) model, the overall accuracies are 94.44%, 88.89%, 83.331%, 85.44% and 89.45%. Finally, the proposed model achieved superiority over the state of art algorithms in both (A/V) and (M/F) models. INDEX TERMS Deep neural networks, independent component analysis, machine learning, motor execution.

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“…g.Tec equipment was used in 54 articles for the EEG method , 33 articles used Emotiv equipment , and 24 articles used Compumedics Neuroscan equipment . Further, 16 articles used Brain Products equipment [191][192][193][194][195][196][197][198][199][200][201][202][203][204][205][206], 13 articles used NeuroSky equipment [207][208][209][210][211][212][213][214][215][216][217][218][219], and Neuroelectrics [220][221][222][223][224][225][226][227] and OpenBCI [228][229][230][231][232][233][234][235] equipment were used in eight articles each. Moreover, seven articles used Biosemi equipment [236][237]…”

Section: Rq1: What Are the Publication Trends Based On Eeg Equipment?mentioning

confidence: 99%

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Jamil

Belkacem

Ouhbi

et al. 2021

Sensors

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Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

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A Low-Cost Lower-Limb Brain-Machine Interface Triggered by Pedaling Motor Imagery for Post-Stroke Patients Rehabilitation

Cited by 53 publications

References 49 publications

Global Cortical Network Distinguishes Motor Imagination of the Left and Right Foot

Global Cortical Network Distinguishes Motor Imagination of the Left and Right Foot

A Deep Learning Approach for Brain Computer Interaction-Motor Execution EEG Signal Classification

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

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