Studying the ability to control human phantom fingers in P300 brain-computer interface

Kaplan, Arline; Zhigulskaya, D.; Kirjanov, D A

doi:10.24075/brsmu.2016-02-03

Cited by 10 publications

(3 citation statements)

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“…Moreover, they generally do not report the same performance metrics used in spelling systems. Such is the case of Kaplan et al (2016), who developed a P300based BCI system to control phantom fingers using visual stimuli placed over them, as an "ideomotor training simulator." On the other hand, Giménez et al (2011) presented the electronic design of a functional electrical stimulation (FES) system and its interface with a BCI based on P300.…”

Section: Visual Brain-computer Interface For Motor Related Applicationsmentioning

confidence: 99%

“…Moreover, they generally do not report the same performance metrics used in spelling systems. Such is the case of Kaplan et al (2016) , who developed a P300-based BCI system to control phantom fingers using visual stimuli placed over them, as an “ideomotor training simulator.” On the other hand, Giménez et al (2011) presented the electronic design of a functional electrical stimulation (FES) system and its interface with a BCI based on P300. However, these works focus on the integration of the BCI commands with the actuator, but there is a lack of information about the feature extraction methods, the AI-based classifiers, and the performance metrics they used.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions

Gutiérrez-Martínez

Mercado-Gutierrez

Carvajal-Gámez

et al. 2021

Front. Hum. Neurosci.

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Brain-Computer Interface (BCI) is a technology that uses electroencephalographic (EEG) signals to control external devices, such as Functional Electrical Stimulation (FES). Visual BCI paradigms based on P300 and Steady State Visually Evoked potentials (SSVEP) have shown high potential for clinical purposes. Numerous studies have been published on P300- and SSVEP-based non-invasive BCIs, but many of them present two shortcomings: (1) they are not aimed for motor rehabilitation applications, and (2) they do not report in detail the artificial intelligence (AI) methods used for classification, or their performance metrics. To address this gap, in this paper the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was applied to prepare a systematic literature review (SLR). Papers older than 10 years, repeated or not related to a motor rehabilitation application, were excluded. Of all the studies, 51.02% referred to theoretical analysis of classification algorithms. Of the remaining, 28.48% were for spelling, 12.73% for diverse applications (control of wheelchair or home appliances), and only 7.77% were focused on motor rehabilitation. After the inclusion and exclusion criteria were applied and quality screening was performed, 34 articles were selected. Of them, 26.47% used the P300 and 55.8% the SSVEP signal. Five applications categories were established: Rehabilitation Systems (17.64%), Virtual Reality environments (23.52%), FES (17.64%), Orthosis (29.41%), and Prosthesis (11.76%). Of all the works, only four performed tests with patients. The most reported machine learning (ML) algorithms used for classification were linear discriminant analysis (LDA) (48.64%) and support vector machine (16.21%), while only one study used a deep learning algorithm: a Convolutional Neural Network (CNN). The reported accuracy ranged from 38.02 to 100%, and the Information Transfer Rate from 1.55 to 49.25 bits per minute. While LDA is still the most used AI algorithm, CNN has shown promising results, but due to their high technical implementation requirements, many researchers do not justify its implementation as worthwile. To achieve quick and accurate online BCIs for motor rehabilitation applications, future works on SSVEP-, P300-based and hybrid BCIs should focus on optimizing the visual stimulation module and the training stage of ML and DL algorithms.

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Section: Visual Brain-computer Interface For Motor Related Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions

Gutiérrez-Martínez

Mercado-Gutierrez

Carvajal-Gámez

et al. 2021

Front. Hum. Neurosci.

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“…All of these factors make the P300 paradigm an attractive tool for creating a closed-loop BCI-based rehabilitation system with high reliability in realizing the patient’s intentions. However, P300-based BCIs have only recently been considered as a tool for motor rehabilitation ( Kaplan et al, 2016 ). So, Delijorge et al (2020) proposed using the P300-based BCI to control a hand exoskeleton, which allows users to control all exoskeleton fingers with high accuracy, while MI-based paradigms are limited in their ability to provide fine movements of finger control.…”

Section: Introductionmentioning

confidence: 99%

Beyond passive observation: feedback anticipation and observation activate the mirror system in virtual finger movement control via P300-BCI

Syrov

Yakovlev

Miroshnikov

et al. 2023

Front. Hum. Neurosci.

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Action observation (AO) is widely used as a post-stroke therapy to activate sensorimotor circuits through the mirror neuron system. However, passive observation is often considered to be less effective and less interactive than goal-directed movement observation, leading to the suggestion that observation of goal-directed actions may have stronger therapeutic potential, as goal-directed AO has been shown to activate mechanisms for monitoring action errors. Some studies have also suggested the use of AO as a form of Brain–computer interface (BCI) feedback. In this study, we investigated the potential for observation of virtual hand movements within a P300-based BCI as a feedback system to activate the mirror neuron system. We also explored the role of feedback anticipation and estimation mechanisms during movement observation. Twenty healthy subjects participated in the study. We analyzed event-related desynchronization and synchronization (ERD/S) of sensorimotor EEG rhythms and Error-related potentials (ErrPs) during observation of virtual hand finger flexion presented as feedback in the P300-BCI loop and compared the dynamics of ERD/S and ErrPs during observation of correct feedback and errors. We also analyzed these EEG markers during passive AO under two conditions: when subjects anticipated the action demonstration and when the action was unexpected. A pre-action mu-ERD was found both before passive AO and during action anticipation within the BCI loop. Furthermore, a significant increase in beta-ERS was found during AO within incorrect BCI feedback trials. We suggest that the BCI feedback may exaggerate the passive-AO effect, as it engages feedback anticipation and estimation mechanisms as well as movement error monitoring simultaneously. The results of this study provide insights into the potential of P300-BCI with AO-feedback as a tool for neurorehabilitation.

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Decoding Lower-Limbs Kinematics from EEG Signals While Walking with an Exoskeleton

Juan

Ossa

Íáñez

et al. 2022

Lecture Notes in Computer Science

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Studying the ability to control human phantom fingers in P300 brain-computer interface

Cited by 10 publications

References 0 publications

Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions

Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions

Beyond passive observation: feedback anticipation and observation activate the mirror system in virtual finger movement control via P300-BCI

Decoding Lower-Limbs Kinematics from EEG Signals While Walking with an Exoskeleton

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