Hybrid MI-SSSEP Paradigm for classifying left and right movement toward BCI for exoskeleton control

Lee, Jaehyung; Cha, Kab-Mun; Kim, Hyung-Min; Choi, Joong Myung; Choong-Hyun, Kim; Lee, Song-Joo

doi:10.1109/iww-bci.2019.8737319

Cited by 6 publications

(11 citation statements)

References 6 publications

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“…As stated in the Introduction section, the proper detection for individual sensitivity to the vibration frequency remains a significant challenge in EVE-BCI. A precise detection can theoretically enhance the performance of the BCI [ 31 , 40 ]. However, in our reviewed articles, only around 18% of cases implement vibration frequency screening for individuals before the formal EEG collection.…”

Section: Resultsmentioning

confidence: 99%

“…The remaining studies either did not describe relevant details or solely applied the fixed frequency to all subjects. Several of them [ 30 , 32 , 40 , 41 , 52 , 56 , 59 , 65 ] were even in the SSSEP design. No initial frequency screening was found in ERP-based EVE-BCIs.…”

Section: Resultsmentioning

confidence: 99%

“…Also, previous studies tested various vibration characteristics to promote the BCI performance. For instance, multiple positions of vibration such as waist [ 34 , 36 , 37 ], finger [ 3 , 38 – 40 ], and wrist [ 35 , 41 ] were applied to establish the EVE-BCI. Different values of stimulus onset asynchrony (SOA), also called the total interval of on-time and off-time, were measured in experiments for the optimum BCI system.…”

Section: Introductionmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Huang

Liang

et al. 2022

PLoS ONE

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“…Such stimuli can be a set of flickering markers or multiple haptic devices vibrating on different frequencies. Attention-based BCIs are sometimes also referenced as Selective Attention BCIs [127]. SSVEPs are the potentials evoked in the human brain by attending visual stimuli flickering at some rate.…”

Section: Bci: Neurological Phenomenamentioning

confidence: 99%

“…Volitional imagination of moving a certain part of the body (tongue, left or right arm, or feet) can be tracked by the increased cortical activity in the area of Motor Cortex designated to control the involved muscles. MI-based BCIs can be used to input one of several commands (usually binary left/right) at a time, thus they have limited ITRs [127]. Usage of MI BCI is an intuitive approach to control drones or robotic manipulators: the desired direction of the movement can be perfectly harmonized with the direction of the imagined motor activity [168].…”

Section: Bci: Neurological Phenomenamentioning

confidence: 99%

Emerging ExG-based NUI Inputs in Extended Realities: A Bottom-up Survey

Shatilov

Chatzopoulos

Lee

et al. 2021

ACM Trans. Interact. Intell. Syst.

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Incremental and quantitative improvements of two-way interactions with e x tended realities (XR) are contributing toward a qualitative leap into a state of XR ecosystems being efficient, user-friendly, and widely adopted. However, there are multiple barriers on the way toward the omnipresence of XR; among them are the following: computational and power limitations of portable hardware, social acceptance of novel interaction protocols, and usability and efficiency of interfaces. In this article, we overview and analyse novel natural user interfaces based on sensing electrical bio-signals that can be leveraged to tackle the challenges of XR input interactions. Electroencephalography-based brain-machine interfaces that enable thought-only hands-free interaction, myoelectric input methods that track body gestures employing electromyography, and gaze-tracking electrooculography input interfaces are the examples of electrical bio-signal sensing technologies united under a collective concept of ExG. ExG signal acquisition modalities provide a way to interact with computing systems using natural intuitive actions enriching interactions with XR. This survey will provide a bottom-up overview starting from (i) underlying biological aspects and signal acquisition techniques, (ii) ExG hardware solutions, (iii) ExG-enabled applications, (iv) discussion on social acceptance of such applications and technologies, as well as (v) research challenges, application directions, and open problems; evidencing the benefits that ExG-based Natural User Interfaces inputs can introduce to the area of XR.

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Inter-stimulus phase coherence in steady-state somatosensory evoked potentials and its application in improving the performance of single-channel MI-BCI

Tao¹,

Yi²,

Wang³

et al. 2021

J. Neural Eng.

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Objective. With the development of clinical applications of motor imagery-based brain–computer interfaces (MI-BCIs), a single-channel MI-BCI system that can be easily assembled is an attractive goal. However, due to the low quality of the spectral power features in the traditional MI-BCI paradigm, the recognition performance of current single-channel systems is far lower than that of multi-channel systems, impeding their use in clinical applications. Approach. In this study, the subjects’ right and left hands were stimulated simultaneously at different frequencies to induce steady-state somatosensory evoked potentials (SSSEP). Subjects then performed motor imagery (MI) tasks. A new electroencephalography (EEG) index, inter-stimulus phase coherence (ISPC), was built to measure phase desynchronization of SSSEP caused by MI. Then, ISPC is introduced as a feature into left-hand and right-hand MI recognition. Main results. ISPC analysis found that left-handed MI can cause a significant decrease in phase synchronization in contralateral sensorimotor SSSEP, while right-handed MI has little effect on it, and vice versa. Combining ISPC features with traditional spectral power features, the single-channel left-hand versus right-hand MI recognition accuracy reaches 81.0%, which is much higher than that observed with traditional MI paradigms (about 60%). Significance. This work shows that the hybrid MI-SSSEP paradigm can provide more sensitive EEG features to decode motor intentions, demonstrating its potential for clinical applications.

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Hybrid MI-SSSEP Paradigm for classifying left and right movement toward BCI for exoskeleton control

Cited by 6 publications

References 6 publications

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

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

Emerging ExG-based NUI Inputs in Extended Realities: A Bottom-up Survey

Inter-stimulus phase coherence in steady-state somatosensory evoked potentials and its application in improving the performance of single-channel MI-BCI

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