Brain‐computer interfaces for amyotrophic lateral sclerosis

McFarland, Dennis J.

doi:10.1002/mus.26828

Cited by 21 publications

(17 citation statements)

References 58 publications

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“…◆ Several studies contained both offline and online evaluations. The online evaluation performance with the highest corrected accuracy [ 45 ] is used as the representative for this study since the online evaluation is more realistic than the offline one.…”

Section: Methodsmentioning

confidence: 99%

EEG-based vibrotactile evoked brain-computer interfaces system: A systematic review

Huang

Liang

et al. 2022

PLoS ONE

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Recently, a novel electroencephalogram-based brain-computer interface (EVE-BCI) using the vibrotactile stimulus shows great potential for an alternative to other typical motor imagery and visual-based ones. (i) Objective: in this review, crucial aspects of EVE-BCI are extracted from the literature to summarize its key factors, investigate the synthetic evidence of feasibility, and generate recommendations for further studies. (ii) Method: five major databases were searched for relevant publications. Multiple key concepts of EVE-BCI, including data collection, stimulation paradigm, vibrotactile control, EEG signal processing, and reported performance, were derived from each eligible article. We then analyzed these concepts to reach our objective. (iii) Results: (a) seventy-nine studies are eligible for inclusion; (b) EEG data are mostly collected among healthy people with an embodiment of EEG cap in EVE-BCI development; (c) P300 and Steady-State Somatosensory Evoked Potential are the two most popular paradigms; (d) only locations of vibration are heavily explored by previous researchers, while other vibrating factors draw little interest. (e) temporal features of EEG signal are usually extracted and used as the input to linear predictive models for EVE-BCI setup; (f) subject-dependent and offline evaluations remain popular assessments of EVE-BCI performance; (g) accuracies of EVE-BCI are significantly higher than chance levels among different populations. (iv) Significance: we summarize trends and gaps in the current EVE-BCI by identifying influential factors. A comprehensive overview of EVE-BCI can be quickly gained by reading this review. We also provide recommendations for the EVE-BCI design and formulate a checklist for a clear presentation of the research work. They are useful references for researchers to develop a more sophisticated and practical EVE-BCI in future studies.

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Section: Methodsmentioning

confidence: 99%

EEG-based vibrotactile evoked brain-computer interfaces system: A systematic review

Huang

Liang

et al. 2022

PLoS ONE

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Section: Materials and Methodologymentioning

confidence: 99%

“…23,24 The relevant features are taken for the given set of data and the dominant features are taken into consideration by the feature selection method for the classification of the Diabetic retinopathy disorder. 25,26 Electrode placement system and Recording procedure. There is an interrelation between the type of movement of the eyes and the application of the study that is conducted.…”

Section: Block Diagram Of the Proposed Systemmentioning

confidence: 99%

Non-invasive technique to detect diabetic retinopathy based on Electrooculography signal using machine learning classifiers

Archana

Rajalakshmi

2022

Proc Inst Mech Eng H

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Single-channel Electrooculogram (EOG) is proposed for detecting diabetic retinopathy. The Corneal-retinal potential of the eyes plays a vital role in the acquisition of Electrooculography. Diabetes is the most prevalent disease and for one out of three people with diabetes above 40 years, diabetic retinopathy occurs. It is necessary for the early detection of diabetic retinopathy as it is one of the primary reasons for blindness in the population. The potential difference between cornea and retina leads to the acquisition of EOG signal. The proposed study aims to design a low-cost miniaturized hardware circuit to obtain EOG signal using second order filters without compromising in accuracy of the outcome signal and to classify the signal into normal and diabetic retinopathy subjects by extracting the statistical features like kurtosis, mean, median absolute deviation, standard deviation, and range from software filtered EOG signal. Among the classifiers used, Support vector machine (SVM) shows a higher accuracy of 93.33%. The sensitivity, specificity and Area Under Curve (AUC) values of SVM are 96.43%, 90.625%, 0.93% is considered as more favorable outcome for the proposed method and it supports the developed prototype and processing methodology. The novelty of the research is based on proposing and exploring a non-invasive methodology for Diabetic retinopathy diagnosis based on EOG signal. Thus, the designed hardware is simple in operation and cost effective, provides an affordable and non-invasive diagnostic tool for diabetic retinopathy patients

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“…Brain-computer interface (BCI) is currently a research hotspot in the multidisciplinary cross-field (Abiri et al, 2019 ; Mcfarland, 2020 ; Pan et al, 2020 ), which aims to establish a direct communication channel between the brain and external devices without relying on peripheral nerve and muscle tissue (Zhou et al, 2014 ; Edelman et al, 2019 ; Li et al, 2019 ; Jin et al, 2020 ; Lu et al, 2020 ; Ge et al, 2021 ). At present, the BCI system is widely used in medical rehabilitation, mechanical control, speller, etc.…”

Section: Introductionmentioning

confidence: 99%

A flexible speller based on time-space frequency conversion SSVEP stimulation paradigm under dry electrode

Zhang

Zhao

et al. 2023

Front. Comput. Neurosci.

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IntroductionSpeller is the best way to express the performance of the brain-computer interface (BCI) paradigm. Due to its advantages of short analysis time and high accuracy, the SSVEP paradigm has been widely used in the BCI speller system based on the wet electrode. It is widely known that the wet electrode operation is cumbersome and that the subjects have a poor experience. In addition, in the asynchronous SSVEP system based on threshold analysis, the system flickers continuously from the beginning to the end of the experiment, which leads to visual fatigue. The dry electrode has a simple operation and provides a comfortable experience for subjects. The EOG signal can avoid the stimulation of SSVEP for a long time, thus reducing fatigue.MethodsThis study first designed the brain-controlled switch based on continuous blinking EOG signal and SSVEP signal to improve the flexibility of the BCI speller. Second, in order to increase the number of speller instructions, we designed the time-space frequency conversion (TSFC) SSVEP stimulus paradigm by constantly changing the time and space frequency of SSVEP sub-stimulus blocks, and designed a speller in a dry electrode environment.ResultsSeven subjects participated and completed the experiments. The results showed that the accuracy of the brain-controlled switch designed in this study was up to 94.64%, and all the subjects could use the speller flexibly. The designed 60-character speller based on the TSFC-SSVEP stimulus paradigm has an accuracy rate of 90.18% and an information transmission rate (ITR) of 117.05 bits/min. All subjects can output the specified characters in a short time.DiscussionThis study designed and implemented a multi-instruction SSVEP speller based on dry electrode. Through the combination of EOG and SSVEP signals, the speller can be flexibly controlled. The frequency of SSVEP stimulation sub-block is recoded in time and space by TSFC-SSVEP stimulation paradigm, which greatly improves the number of output instructions of BCI system in dry electrode environment. This work only uses FBCCA algorithm to test the stimulus paradigm, which requires a long stimulus time. In the future, we will use trained algorithms to study stimulus paradigm to improve its overall performance.

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Brain‐computer interfaces for amyotrophic lateral sclerosis

Cited by 21 publications

References 58 publications

EEG-based vibrotactile evoked brain-computer interfaces system: A systematic review

EEG-based vibrotactile evoked brain-computer interfaces system: A systematic review

Non-invasive technique to detect diabetic retinopathy based on Electrooculography signal using machine learning classifiers

A flexible speller based on time-space frequency conversion SSVEP stimulation paradigm under dry electrode

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