脑-机接口
计算机科学
信息传递
语音识别
接口(物质)
拼写
刺激
脑电图
人工智能
模式识别(心理学)
神经科学
心理学
电信
并行计算
最大气泡压力法
语言学
哲学
气泡
作者
Xiaogang Chen,Zhikai Chen,Shangkai Gao,Xiaorong Gao
标识
DOI:10.1080/2326263x.2014.944469
摘要
Spelling is an important application of brain-computer interfaces (BCIs). Previous BCI spellers were not suited for widespread use due to their low information transfer rate (ITR). In this study, we constructed a high-ITR BCI speller based on the steady-state visual evoked potential (SSVEP). A 45-target BCI speller was implemented with a frequency resolution of 0.2 Hz. A sampled sinusoidal stimulation method was used to present visual stimuli on a conventional LCD screen. The online results revealed that the proposed BCI speller had a good performance, reaching a high average accuracy (84.1% for 2 s stimulation time; 90.2% for 3 s stimulation time) and the corresponding high ITR (105 bits/min for 2 s stimulation time, 82 bits/min for 3 s stimulation time) during the low-frequency stimuli, while 88.7% and 61 bits/min were achieved for a 4 s time window during the high-frequency stimuli.
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