脑-机接口
运动表象
脑电图
计算机科学
频道(广播)
人工智能
接口(物质)
水准点(测量)
软件可移植性
模式识别(心理学)
互操作性
可穿戴计算机
语音识别
嵌入式系统
地理
程序设计语言
并行计算
最大气泡压力法
气泡
大地测量学
精神科
操作系统
计算机网络
心理学
作者
Pasquale Arpaïa,Francesco Rigoli,Antonio Espósito,Marco Parvis
标识
DOI:10.1142/s0129065721500039
摘要
A method for selecting electroencephalographic (EEG) signals in motor imagery-based brain-computer interfaces (MI-BCI) is proposed for enhancing the online interoperability and portability of BCI systems, as well as user comfort. The attempt is also to reduce variability and noise of MI-BCI, which could be affected by a large number of EEG channels. The relation between selected channels and MI-BCI performance is therefore analyzed. The proposed method is able to select acquisition channels common to all subjects, while achieving a performance compatible with the use of all the channels. Results are reported with reference to a standard benchmark dataset, the BCI competition IV dataset 2a. They prove that a performance compatible with the best state-of-the-art approaches can be achieved, while adopting a significantly smaller number of channels, both in two and in four tasks classification. In particular, classification accuracy is about 77–83% in binary classification with down to 6 EEG channels, and above 60% for the four-classes case when 10 channels are employed. This gives a contribution in optimizing the EEG measurement while developing non-invasive and wearable MI-based brain-computer interfaces.
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