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A P300-Based Brain-Computer Interface for Chinese Character Input
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AI
DOI：10.1080/10447318.2016.1203529.png)
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Abstract
En 中文
The majority of previously developed assistive communication brain-computer interface systems have primarily focused on languages that are written in alphabetic scripts. However, languages that are written in logographic scripts, such as those in Chinese hanzi (or sinograms), pose a challenge for the implementation of visual spelling systems because it is impossible to simultaneously display thousands of items in a stimulus matrix of a reasonable size. In this study, a P300 visual spelling system that uses a novel method to input Chinese sinograms developed with a Hanyu Pinyin-based method is presented. This method transcribes a Chinese Pinyin into initial consonant and vowel components according to its Mandarin pronunciation. In this paradigm, each sinogram is input by selecting the initial consonant and then the vowel components and subsequently selecting the sinogram itself. Ten healthy subjects participated in the study and achieved an average offline accuracy of 92.6% with a mean information transfer rate of 39.2bits/min and an average online input speed of one sinogram per 43.9s. The preliminary results presented here indicated that the online input of Chinese text using a Pinyin-based visual speller is feasible.
Keywords:
EMPIRICAL MODE DECOMPOSITION
P300 SPELLER
DESTINATION SELECTION
BCI SYSTEM
EEG DATA
PERFORMANCE
SIGNALS
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