Controlling a hand orthosis by means of P300-based brain computer interface

Stan, Andrei; Irimia, Dănuţ-Constantin; Botezatu, Nicolae; Lupu, Robert Gabriel

doi:10.1109/ehb.2015.7391389

Cited by 15 publications

(22 citation statements)

References 10 publications

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“…Since EEG signals are independent of residual muscles and contain high-quality neural information on an individual's intentions, developments in BCI systems are designed to control prostheses using a subject's thoughts alone (Donchin et al, 2000; Mcmullen et al, 2014; Stan et al, 2015; Vidaurre et al, 2016), which can be divided into spontaneous BCIs [event-related (de)synchronization (ERD/ERS)] and evoked BCIs [steady-state visual evoked potential (SSVEP), P300 potential, etc.] (Pfurtscheller et al, 2000b; He et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

An Approach for Brain-Controlled Prostheses Based on a Facial Expression Paradigm

Zhang

et al. 2018

Front. Neurosci.

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One of the most exciting areas of rehabilitation research is brain-controlled prostheses, which translate electroencephalography (EEG) signals into control commands that operate prostheses. However, the existing brain-control methods have an obstacle between the selection of brain computer interface (BCI) and its performance. In this paper, a novel BCI system based on a facial expression paradigm is proposed to control prostheses that uses the characteristics of theta and alpha rhythms of the prefrontal and motor cortices. A portable brain-controlled prosthesis system was constructed to validate the feasibility of the facial-expression-based BCI (FE-BCI) system. Four types of facial expressions were used in this study. An effective filtering algorithm based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and sample entropy (SampEn) was used to remove electromyography (EMG) artifacts. A wavelet transform (WT) was applied to calculate the feature set, and a back propagation neural network (BPNN) was employed as a classifier. To prove the effectiveness of the FE-BCI system for prosthesis control, 18 subjects were involved in both offline and online experiments. The grand average accuracy over 18 subjects was 81.31 ± 5.82% during the online experiment. The experimental results indicated that the proposed FE-BCI system achieved good performance and can be efficiently applied for prosthesis control.

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Section: Introductionmentioning

confidence: 99%

An Approach for Brain-Controlled Prostheses Based on a Facial Expression Paradigm

Zhang

et al. 2018

Front. Neurosci.

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“…[86], a prototype study, was rated TRL 4 due to the study being performed on the basis of improving and assessing its safety features. It is also worth noting that while a formal safety assessment was not performed for the TRL 3 prototypes of Stan et al [79], Randazzo et al…”

Section: Discussionmentioning

confidence: 99%

“…In order to facilitate hand MI and account for significant time-points in the EEG data, all the studies employed a cue-feedback strategy during their trials. 19 of the studies presented a form of visual cue while the rest, except for two unspecified [69,87], involved cues in auditory ("bleep") [76,[80][81][82][83], textual [78,79,89] or verbal [88] forms. As for the provision of a matching sensory feedback, 16 studies presented a combination of kinaesthetic and visual feedback with some also providing auditory feedback during successful movement attempts.…”

Section: Discussionmentioning

confidence: 99%

“…A final list with 30 studies was identified as eligible for qualitative review. Among the 30 studies, 11 [60][61][62][63][64][65][66][67][68][69][70] were involved in testing the BCI-hand robot system on chronic and subacute stroke patients ( [60,65] are RCTs) while the rest involved testing on healthy participants [71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89].…”

Section: Figure 1 Study Selection Flowchartmentioning

confidence: 99%

“…, used a video of an actual hand performing the desired action. Ang et al[60] and Stan et al[79], in a different strategy, provided visual feedback through photo manipulation and textual display, respectively. While the latter two studies reported promising results (with Ang et al…”

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confidence: 99%

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Brain-Computer Interface Robotics for Hand Rehabilitation After Stroke: A Systematic Review

Baniqued

Stanyer

Awais

et al. 2019

Preprint

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Electroencephalography-based brain-computer interfaces (BCI) that allow the control of robotic devices to support stroke patients during upper limb rehabilitation are increasingly popular. Hand rehabilitation is focused on improving dexterity and fine motor control and is a core approach for helping stroke survivors regain activities of daily living. This systematic review examines recent developments in BCI-robotic systems for hand rehabilitation and identifies evidence-based clinical studies on stroke patients. A search for January 2010-October 2019 articles using Ovid MEDLINE, Embase, PEDro, PsycINFO, IEEE Xplore and Cochrane Library databases was performed. The selection criteria included BCI-hand robotic systems for rehabilitation in various development stages involving tests on healthy human subjects or stroke survivors. Data fields include those related to study design, participant characteristics, technical specifications of the system, and clinical outcome measures. 30 studies were identified as eligible for qualitative review and among these, 11 studies involved testing a BCI-hand robot on chronic and subacute stroke patients. Statistically significant improvements in motor assessment scores relative to controls were observed for two BCI-hand robot interventions. The degree of robot control for the majority of studies was limited to triggering the device to perform grasping or pinching movements using motor imagery. Most employed a combination of kinaesthetic and visual response via the robotic device and display screen, respectively, to match feedback to motor imagery. Most studies on BCI-robotic systems for hand rehabilitation report systems at prototype or pre-clinical stages of development. Some studies report statistically significant improvements in functional recovery after stroke, but there is a need to develop a standard protocol for assessing technical and clinical outcomes so that the necessary evidence base on efficiency and efficacy can be developed.

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