Control of a nursing bed based on a hybrid brain-computer interface

Peng, Nengneng; Zhang, Rui; Zeng, Haihua; Wang, Fei; Li, Kai; Li, Yuanqing; Zhuang, Xiaobin

doi:10.1109/embc.2016.7591008

Cited by 11 publications

(3 citation statements)

References 8 publications

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“…Rehabilitation using BCI is still not used in clinical practice [97]. "Go" and "Stop" movement P300 + SSVEP [54] Speed SSVEP + MI [65] Multi-degree SSVEP + MI [86] Speed and direction SSVEP + MI [63] P300 + MI [87,88] Autonomous navigation P300 + MI [89] Computer cursor 2-D SSVEP + MI [52,90] P300 + MI [72] Speller Spelling accuracy P300 + SSVEP [55,68] P300 + MI [71,73] Artificial limb Upper limb SSVEP + MI [62,64] Multidimensional robotic arm [91] Functional electrical therapy Advanced SSVEP + MI [66] ALS patients Communication P300 + MI [92] Awareness P300 + SSVEP [93] Virtual environment Smart home P300 + SSVEP [94,95] Intelligent nursing bed [96] in EEG acquisition due to electrodes placement, skull muscle movement, environment noises, limitations in hardware, and their calibrations. Two or more tasks need to be performed simultaneously in hybrid that might increase mental workload and cause discomfort to some users.…”

Section: Limitationsmentioning

confidence: 99%

Hybrid Brain-Computer Interface Systems: Approaches, Features, and Trends

Guragain¹,

Haider²,

Fazel-Rezai³

2018

Evolving BCI Therapy - Engaging Brain State Dynamics

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Brain-computer interface (BCI) is an emerging field, and an increasing number of BCI research projects are being carried globally to interface computer with human using EEG for useful operations in both healthy and locked persons. Although several methods have been used to enhance the BCI performance in terms of signal processing, noise reduction, accuracy, information transfer rate, and user acceptability, the effective BCI system is still in the verge of development. So far, various modifications on single BCI systems as well as hybrid are done and the hybrid BCIs have shown increased but insufficient performance. Therefore, more efficient hybrid BCI models are still under the investigation by different research groups. In this review chapter, single BCI systems are briefly discussed and more detail discussions on hybrid BCIs, their modifications, operations, and performances with comparisons in terms of signal processing approaches, applications, limitations, and future scopes are presented.

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Section: Limitationsmentioning

confidence: 99%

Hybrid Brain-Computer Interface Systems: Approaches, Features, and Trends

Guragain¹,

Haider²,

Fazel-Rezai³

2018

Evolving BCI Therapy - Engaging Brain State Dynamics

View full text Add to dashboard Cite

show abstract

“…The system needs to detect one control state against 'multiple' idle states by increasing pseudo patterns. The systems using this method are based on exogenous signals such as event-related potential (ERP) (He et al 2016, Peng et al 2016, Zhang et al 2017 and SSVEP (Pan et al 2013). For example, Pan et al developed an SSVEP-based brain switch with one target key and three 'pseudo keys' , in which subjects only focused on the target key during the experiment.…”

Section: Introductionmentioning

confidence: 99%

A high-performance brain switch based on code-modulated visual evoked potentials

Zheng

Gao

Zhang³

et al. 2022

J. Neural Eng.

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Objective. Asynchronous brain-computer interfaces (BCIs) are more practical and natural compared to synchronous BCIs. A brain switch is a standard asynchronous BCI, which can automatically detect the specified change of the brain and discriminate between the control state and the idle state. The current brain switches still face challenges on relatively long reaction time (RT) and high false positive rate (FPR). Approach. In this paper, an online electroencephalography-based brain switch is designed to realize a fast reaction and keep long idle time (IDLE) without false positives (FPs) using code-modulated visual evoked potentials (c-VEPs). Two stimulation paradigms were designed and compared in the experiments: multi-code concatenate modulation (concatenation mode) and single-code periodic modulation (periodic mode). Using a task-related component analysis-based detection algorithm, EEG data can be decoded into a series of code indices. Brain states can be detected by a template matching approach with a sliding window on the output series. Main results. The online experiments achieved an average RT of 1.49 seconds when the average IDLE for each FP was 68.57 minutes (1.46e-2 FP/min) or an average RT of 1.67 seconds without FPs. Significance. This study provides a practical c-VEP based brain switch system with both fast reaction and low FPR during idle state, which can be used in various BCI applications.

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“…The results proposed the optimal assistive devices for reducing musculoskeletal burden. In the development of nursing beds, Peng et al (2016) proposed a nursing bed controlled by a hybrid brain-computer interface. The nursing bed involves a steady-state visually evoked potential and a P300 smart nursing bed system.…”

Section: Introductionmentioning

confidence: 99%

Validation of evaluation model and evaluation indicators comprised Kansei Engineering and eye movement with EEG: an example of medical nursing bed

2018

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The study is to innovate an evaluation model of medical nursing bed and verify indicators by triangulation of subjective feelings, eye movement and electroencephalography (EEG). The underlying theory is Kansei Engineering that is the technology to measure and transform consumers' perceptions of products into design elements. Kansei Engineering comprises external physiological sensory measurement and intrinsic psychological susceptibility measurement. The evaluation of medical nursing bed should measure subjective and objective data. The current evaluation method of medical nursing bed is very subjective and cannot be tested objectively. It is rationale to apply Kansei Engineering in medical nursing bed evaluation comprised subjective feelings and eye movement with electroencephalography. The methodology is the experimental research in which four design proposals of medical nursing bed were compared by 20 participants. Data collection adopted Tobii X2 eye tracker and NeurOne EEG recording system except subjective feelings. D-lab, ERGO-LAB, and EEGLAB was applied to process data of eye movement and EEG. Correlation of triangulation was analyzed for criterion-related validity. Cronbach's Alpha was used as a measure of internal consistency for reliability. Findings indicated design proposal 2 contained a rounded rectangular and line surface organically combined was optimal. Triangulation was supported as indicators for more objective and persuasive evaluation of medical nursing beds. Implications for practice will be applied in NCC Medical Co.

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Control of a nursing bed based on a hybrid brain-computer interface

Cited by 11 publications

References 8 publications

Hybrid Brain-Computer Interface Systems: Approaches, Features, and Trends

Hybrid Brain-Computer Interface Systems: Approaches, Features, and Trends

A high-performance brain switch based on code-modulated visual evoked potentials

Validation of evaluation model and evaluation indicators comprised Kansei Engineering and eye movement with EEG: an example of medical nursing bed

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