An Experimental Study on Usability of Brain-Computer Interaction Technology in Human Spaceflight

Chen, Shanguang; Jiang, Jin; Tang, Jiming; Jiao, Xuejun; Qi, Hongzhi; Cao, Ye; Wang, Chunhui; Ming, Dong

doi:10.1007/978-3-319-58625-0_22

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Brain–computer interfaces (BCIs) allow users to communicate with the external devices by converting brain signals into commands [ 1 , 2 ]. BCIs can help people with neuromuscular diseases to improve the life quality [ 3 ] or help special appliance operators like astronauts whose movements were restricted by the environment to work more efficiently [ 4 , 5 ]. As a kind of brain signal that owns high temporal resolution and convenience of acquisition, electroencephalogram (EEG) is welcomed by BCI researchers.…”

Section: Introductionmentioning

confidence: 99%

Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling

Tang

Han

et al. 2020

Sensors

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The brain–computer interface (BCI) spellers based on steady-state visual evoked potentials (SSVEPs) have recently been widely investigated for their high information transfer rates (ITRs). This paper aims to improve the practicability of the SSVEP-BCIs for high-speed spelling. The system acquired the electroencephalogram (EEG) data from a self-developed dedicated EEG device and the stimulation was arranged as a keyboard. The task-related component analysis (TRCA) spatial filter was modified (mTRCA) for target classification and showed significantly higher performance compared with the original TRCA in the offline analysis. In the online system, the dynamic stopping (DS) strategy based on Bayesian posterior probability was utilized to realize alterable stimulating time. In addition, the temporal filtering process and the programs were optimized to facilitate the online DS operation. Notably, the online ITR reached 330.4 ± 45.4 bits/min on average, which is significantly higher than that of fixed stopping (FS) strategy, and the peak value of 420.2 bits/min is the highest online spelling ITR with a SSVEP-BCI up to now. The proposed system with portable EEG acquisition, friendly interaction, and alterable time of command output provides more flexibility for SSVEP-based BCIs and is promising for practical high-speed spelling.

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Section: Introductionmentioning

confidence: 99%

Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling

Tang

Han

et al. 2020

Sensors

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“…A brain-computer interface (BCI) could measure the brain signal of users and communicate with the external devices, which can help people with motor disabilities to improve the life quality [1,2]. Some special appliance operators such as astronauts whose movements were restricted by the environment could also benefit from the BCIs [3,4]. Electroencephalogram (EEG) which owns the advantage of convenience, low cost, and high temporal resolution is the most welcomed brain signal for BCIs.…”

Section: Introductionmentioning

confidence: 99%

A Brain-Computer Interface Based on Multifocal SSVEPs Detected by Inter-Task-Related Component Analysis

Tang

Liu

et al. 2020

IEEE Access

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Multifocal steady-state visual evoked potentials (mfSSVEPs) have been successfully applied to assess visual field loss in glaucoma. However, the potential of mfSSVEPs for command control has not been fully explored yet. It is significant to detect single-trial mfSSVEPs and establish a brain-computer interface (BCI) system. This study designed a stimulating paradigm that contains 32 targets, with each target composed of five fan-shaped flickers in a circle. The five flickers were modulated by five frequencies and formed a five-bit binary encoding system through controlling the ON/OFF state of each flicker. Twelve subjects participated in an offline and an online experiments. Inter-task-related component analysis (iTRCA) combined with a probabilistic model was proposed for target recognition. Notably, the training data needed for calibration corresponded to only six out of the 32 targets. It was found that the increasing number of flickers showed a negative impact on the mfSSVEP signal. The accuracy reached 80.9% ± 11.7% on average with a peak of 95.3% by iTRCA, which was significantly higher than that by a traditional method. The results indicate that the proposed stimulation and algorithm are effective for encoding and decoding BCI commands. Therefore, the mfSSVEP-based BCI enables the augmentation of the BCI instruction set without any burden of collecting extra training data.INDEX TERMS brain-computer interface (BCI), steady-state visual evoked potential (SSVEP), multifocal SSVEP (mfSSVEP), inter-task-related component analysis (iTRCA)

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An Experimental Study on Usability of Brain-Computer Interaction Technology in Human Spaceflight

Cited by 2 publications

References 19 publications

Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling

Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling

A Brain-Computer Interface Based on Multifocal SSVEPs Detected by Inter-Task-Related Component Analysis

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