A wireless, wearable Brain-Computer Interface for neurorehabilitation at home; A feasibility study

Simon, Colin; Ruddy, Kathy

doi:10.1109/bci53720.2022.9734849

Cited by 6 publications

(6 citation statements)

References 24 publications

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“…Starting an in depth analyses on the application fields 30 on 84 researches have medical purposes [83][84][85][86][87][88][89][90][91][92][92][93][94][95][96][97][98][99][100][101][102][103][104][105][106][107][108][109][110][111].…”

Section: Bci Application and Feedbackmentioning

confidence: 99%

“…From the analysis of the literature summarized in the EEG-MI-BCI- 11 works applied temporal filtering approaches like Butterworth of different orders and cutoffs: 3rd order filter in 0.5-30Hz [144] or in 4-33Hz [119], 4th order in 16-24Hz [84], 5th order in 8-30Hz [92,110,128,138,139] or in 1-40Hz [109], biquad tweaked Butterworth in 8-13Hz [134], 6th order in 8-30Hz [149].…”

Section: Eeg Data Preprocessingmentioning

confidence: 99%

“…Besides some papers that employ different strategies at the same time [85,97,111,115,117,118,130] or multiple ML techniques [85,107,129,131,140,142,148,153,179], the authors usually prefer to concentrate on a specific technique. The traditional ML models seem to be the most used and among them the Linear Discriminant Analysis (LDA) classifier employed in its basic version [85,86,89,97,100,107,113,115,130,[146][147][148][149]154], in combination with Common Spatial Pattern (CSP) [92], or considering multiple LDA models combined through fuzzy integration, considering optimal feature selection and classification through generalized sparse LDA [168], and presenting confidence levels assigned with particle swarm optimization [124] appears to be the most used. Likewise, the Support Vector Machine (SVM) classifier is widely employed [82,85,95,107,[117][118][119]125,131,136,…”

Section: Classification and Data Analysismentioning

confidence: 99%

“…Table 6 presents a summary of the publicly available datasets, which will be described in details in the following subsections. Considering the other papers, notice that the experimental paradigms usually concern the motor imagination of left/right hand/fist [82,86,[89][90][91][92]95,97,106,108,111,115,116,119,[124][125][126][127]130,131,133,134,[137][138][139][142][143][144][145][146][147]149,150,154,157,159,161,179], both hands [95,115,116,126,138,161], dominant or single hand movements [83,98,109,…”

Section: Dataset and Experimental Paradigmsmentioning

confidence: 99%

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EEG-based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review

Saibene¹,

Caglioni²,

Corchs³

et al. 2023

Preprint

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In the last decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of Brain Computer Interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on Motor Imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims at systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.

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Section: Bci Application and Feedbackmentioning

confidence: 99%

Section: Eeg Data Preprocessingmentioning

confidence: 99%

Section: Classification and Data Analysismentioning

confidence: 99%

Section: Dataset and Experimental Paradigmsmentioning

confidence: 99%

See 2 more Smart Citations

EEG-based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review

Saibene¹,

Caglioni²,

Corchs³

et al. 2023

Preprint

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“…However, neurofeedback was not investigated in trying to enhance motor imagery detection. Recently, the feasibility of a wearable BCI for neurorehabilitation at home was proposed in [33]. Healthy participants received remote instructions on the use of an EEG device with 16 dry sensors.…”

Section: Introductionmentioning

confidence: 99%

Paving the Way for Motor Imagery-Based Tele-Rehabilitation through a Fully Wearable BCI System

Arpaïa

Coyle

Esposito

et al. 2023

Sensors

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The present study introduces a brain–computer interface designed and prototyped to be wearable and usable in daily life. Eight dry electroencephalographic sensors were adopted to acquire the brain activity associated with motor imagery. Multimodal feedback in extended reality was exploited to improve the online detection of neurological phenomena. Twenty-seven healthy subjects used the proposed system in five sessions to investigate the effects of feedback on motor imagery. The sample was divided into two equal-sized groups: a “neurofeedback” group, which performed motor imagery while receiving feedback, and a “control” group, which performed motor imagery with no feedback. Questionnaires were administered to participants aiming to investigate the usability of the proposed system and an individual’s ability to imagine movements. The highest mean classification accuracy across the subjects of the control group was about 62% with 3% associated type A uncertainty, and it was 69% with 3% uncertainty for the neurofeedback group. Moreover, the results in some cases were significantly higher for the neurofeedback group. The perceived usability by all participants was high. Overall, the study aimed at highlighting the advantages and the pitfalls of using a wearable brain–computer interface with dry sensors. Notably, this technology can be adopted for safe and economically viable tele-rehabilitation.

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Keeping Our Eyes on the Prize; Are We Losing Sight of the ‘Why’ in BCI for Neurorehabilitation?

Simon,

Ruddy

2024

SpringerBriefs in Electrical and Computer Engineering

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A wireless, wearable Brain-Computer Interface for neurorehabilitation at home; A feasibility study

Cited by 6 publications

References 24 publications

EEG-based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review

EEG-based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review

Paving the Way for Motor Imagery-Based Tele-Rehabilitation through a Fully Wearable BCI System

Keeping Our Eyes on the Prize; Are We Losing Sight of the ‘Why’ in BCI for Neurorehabilitation?

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