A Tactile Brain-Computer Interface for Virtual Wheelchair Control at Home

Eidel, Matthias; Troger, Wolfgang; Winterholler, Martin; Giesler, T.; Kübler, Andrea

doi:10.1109/bci51272.2021.9385341

Cited by 13 publications

(7 citation statements)

References 18 publications

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“…Surprisingly, only around one-fifth of EVE-BCI studies were carried out for patients. These patients suffered from multiple levels of motor functioning loses caused by different diseases including the unresponsive wakefulness syndrome (UWS) [ 8 , 79 ], disorder of consciousness (DOC) [ 9 , 49 , 61 , 63 , 70 ], amyotrophic lateral sclerosis (ALS) [ 54 , 57 , 87 , 89 , 103 ], locked-in syndrome (LIS) [ 58 , 63 , 70 , 78 ], stroke [ 94 ], and minimally conscious state (MCS) [ 86 ]. On the other hand, most studies (83.54% of cases) alternatively focused on healthy subjects covering adults of all ages, including young people, the middle-aged, and elders.…”

Section: Resultsmentioning

confidence: 99%

“…Unlike the vibration frequency or the SOA, the location of vibration draws a lot of interest from researchers in the EVE-BCI area. Many locations throughout the body including fingers [ 30 , 31 , 50 , 95 ], wrist [ 8 , 9 ], back [ 37 , 48 , 58 ], foot [ 43 , 61 ], ankle [ 1 , 63 ], shoulder [ 76 , 88 ], waist [ 37 , 60 , 62 ], arm [ 34 , 57 ], leg [ 34 , 88 ], cheek [ 1 ], neck [ 66 , 86 ], chest [ 73 ], elbow [ 76 , 92 ], hand palms [ 89 ], scapula [ 96 ] and knee [ 103 ] were explored in publications. More than 1/3 (N = 30) of articles carried out the experiments with tactors attached in multiple parts of the body such as the wrist-back [ 78 , 79 , 87 ], finger-toe [ 24 , 42 ], arm-waist-leg [ 34 , 88 ].…”

Section: Resultsmentioning

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%

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

Huang

Liang

et al. 2022

PLoS ONE

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“…There are other systems based on motor imagery task stimulation for individuals with severe motor impairment, such as amyotrophic lateral sclerosis (ALS). In one such study Eidel, Tröger [31] worked on a participant who had restricted motor function. Vibrotactile stimuli were applied on four body positions of this patient, and EEG data were recorded from 12 positions (Fz, FC1, FC2, C3, Cz, C4, P3, Pz, P4, O1, Oz, and O2) using a g.GAMMAcap.…”

Section: Wheelchair Mobilitymentioning

confidence: 99%

A Comprehensive Survey of Brain-Computer Interface Technology in Healthcare: Research Perspectives

Vimal Cruz,

Jamal,

Sethuraman

2024

Preprint

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The Brain-Computer Interface (BCI) technology has emerged as a groundbreaking innovation with profound implications across diverse domains, particularly in healthcare. By establishing a direct communication pathway between the human brain and external devices, BCI systems offer unprecedented opportunities for diagnosis, treatment, and rehabilitation, thereby reshaping the landscape of medical practice. However, despite its immense potential, the widespread adoption of BCI technology in clinical settings faces several challenges. These include the need for robust signal acquisition and processing techniques, ensuring user safety and privacy, addressing ethical considerations, and optimizing user training and adaptation. Overcoming these challenges is crucial to unleashing the complete potential of BCI technology in healthcare and realizing its promise of personalized, patient-centric care. This review work underscores the transformative potential of BCI technology in revolutionizing medical practice. This paper offers a comprehensive analysis of medical-oriented BCI applications by exploring the various uses of BCI technology and its potential to transform patient care.

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“…There are various implementations of BCI but in smart wheelchair research, most will prefer to use the non-invasive EEG type. Going into the implementation of BCI, Eidel et al present a case study of a particular end user operating the smart wheelchair in a simulation environment [12]. The user was in locked-in state and training to operate a virtual vibrotactile BCI wheelchair on a weekly basis for several months.…”

Section: B Brain-computer Interface (Bci)mentioning

confidence: 99%

Smart Wheelchairs: A Review on Control Methods

Aziz

Khusaini

Mohamed

et al. 2022

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

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Recent advancement in research fields involving computer science and Artificial Intelligence has influence the rapid growth in smart wheelchair research. Another factor adding to this growth is the evolution of sensor and computing technology. Basically, a smart wheelchair is a powered wheelchair that has been modified by adding necessary sensors and instruments that able to read, collect and send information that can be used to modify the status of the wheelchair, as well as interacting with the environment or the user. The modification can be done on various part of the wheelchair system, and can be categorized with their input methods, operating system or their navigation system itself. This paper discussing on various input methods used in recent smart wheelchair research and summarizes the ideas presented.

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A Tactile Brain-Computer Interface for Virtual Wheelchair Control at Home

Cited by 13 publications

References 18 publications

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

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

A Comprehensive Survey of Brain-Computer Interface Technology in Healthcare: Research Perspectives

Smart Wheelchairs: A Review on Control Methods

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