Low-Cost Robotic Guide Based on a Motor Imagery Brain–Computer Interface for Arm Assisted Rehabilitation

Quiles, Eduardo; Suay, Francisco Montes; Candela, Gemma; Chio, N.; Jiménez, Manuel; Álvarez-Kurogi, Leandro

doi:10.3390/ijerph17030699

Cited by 17 publications

(18 citation statements)

References 56 publications

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“…Additionally, we would expect the accuracy for motor activity to be greater than that of motor imagery, but it had an accuracy 20.7% less than the motor imagery paradigm. The results here are near the lower end of the range of 65% to 95% accuracy for classification of left versus right hand movements via dry EEG electrodes in the literature [Quiles et al, 2020, Irimia et al, 2018.…”

Section: Resultssupporting

confidence: 53%

“…The maximum accuracies of P300, a SSVEP, and a MI BCI spellers as reported by Rezeika et al [2018] were 99.7%, 98.78%, and 85%, respectively. BCIs for motor imagery tasks, such as imagining using one's left or right hand, have yielded accuracies in the range of roughly 65% to 95% in the literature, but these systems often require long training periods for reliable use [Quiles et al, 2020, Irimia et al, 2018. The P300 and SSVEP control paradigms are also beneficial, because the P300 and SSVEP artifacts in EEG signals occur naturally and therefore these BCI do not require much training [McFarland andWolpaw, 2017, Rezeika et al, 2018].…”

Section: Introductionmentioning

confidence: 99%

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Performance of OpenBCI EEG Binary Intent Classification with Laryngeal Imagery

Kuhn¹,

George²

2021

Preprint

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One of the greatest goals of neuroscience in recent decades has been to rehabilitate individuals who no longer have a functional relationship between their mind and their body. Although neuroscience has produced technologies which allow the brains of paralyzed patients to accomplish tasks such as spell words or control a motorized wheelchair, these technologies utilize parts of the brain which may not be optimal for simultaneous use. For example, if you needed to look at flashing lights to spell words for communication, it would be difficult to simultaneously look at where you are moving. To improve upon this issue, this study developed and tested the foundation for a speech prosthesis paradigm which would utilize the innate neurophysiology of the human brain's speech system. In this experiment, two participants were asked to respond to a yes or no question via an EEG-based BCI of three different types; SSVEP-based, motor imagery-based, and laryngeal-imagery-based. By comparing the accuracy of the two established BCI paradigms to the novel laryngeal-imagery paradigm, we can establish the relative effectiveness of the novel paradigm. Machine learning algorithms were used to classify the EEG signals which had been transformed into frequency space (spectrograms) and common spatial pattern (CSP) dimensions. The SSVEP control task was able to be classified with better accuracy (62.5%) than the no information rate of 50% on the test set, but motor activity/imagery and laryngeal activity/imagery control tasks were not. Although the laryngeal methods did not produce accuracies above the no information rate, it is possible that with a larger amount of higher-quality data, this could prove otherwise. In the future, similar research should focus on reproducing the methods used here with better quality and more data.

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Section: Resultssupporting

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Performance of OpenBCI EEG Binary Intent Classification with Laryngeal Imagery

Kuhn¹,

George²

2021

Preprint

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“…There is no disability of patient muscles or limbs in apraxia, and muscle power may not be reduced [ 7 ]. Motor deficiencies are affecting millions of people worldwide [ 8 ]. People with such disabilities experience an encumbrance in performing their daily functions.…”

Section: Introductionmentioning

confidence: 99%

A BCI System Based on Motor Imagery for Assisting People with Motor Deficiencies in the Limbs

et al. 2020

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Motor deficiencies constitute a significant problem affecting millions of people worldwide. Such people suffer from a debility in daily functioning, which may lead to decreased and incoherence in daily routines and deteriorate their quality of life (QoL). Thus, there is an essential need for assistive systems to help those people achieve their daily actions and enhance their overall QoL. This study proposes a novel brain–computer interface (BCI) system for assisting people with limb motor disabilities in performing their daily life activities by using their brain signals to control assistive devices. The extraction of useful features is vital for an efficient BCI system. Therefore, the proposed system consists of a hybrid feature set that feeds into three machine-learning (ML) classifiers to classify motor Imagery (MI) tasks. This hybrid feature selection (FS) system is practical, real-time, and an efficient BCI with low computation cost. We investigate different combinations of channels to select the combination that has the highest impact on performance. The results indicate that the highest achieved accuracies using a support vector machine (SVM) classifier are 93.46% and 86.0% for the BCI competition III–IVa dataset and the autocalibration and recurrent adaptation dataset, respectively. These datasets are used to test the performance of the proposed BCI. Also, we verify the effectiveness of the proposed BCI by comparing its performance with recent studies. We show that the proposed system is accurate and efficient. Future work can apply the proposed system to individuals with limb motor disabilities to assist them and test their capability to improve their QoL. Moreover, the forthcoming work can examine the system’s performance in controlling assistive devices such as wheelchairs or artificial limbs.

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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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Low-Cost Robotic Guide Based on a Motor Imagery Brain–Computer Interface for Arm Assisted Rehabilitation

Cited by 17 publications

References 56 publications

Performance of OpenBCI EEG Binary Intent Classification with Laryngeal Imagery

Performance of OpenBCI EEG Binary Intent Classification with Laryngeal Imagery

A BCI System Based on Motor Imagery for Assisting People with Motor Deficiencies in the Limbs

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

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