Ratcliffe Liam, Puthusserypady Sadasivan
University of Glasgow, University Avenue, Glasgow G12 8QQ, United Kingdom.
Department of Health Technology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark.
Comput Biol Med. 2020 Feb;117:103599. doi: 10.1016/j.compbiomed.2019.103599. Epub 2020 Jan 3.
Develop an effective and intuitive Graphical User Interface (GUI) for a Brain-Computer Interface (BCI) system, that achieves high classification accuracy and Information Transfer Rates (ITRs), while using a simple classification technique. Objectives also include the development of an output device, that is capable of real time execution of the selected commands.
A region based T9 BCI system with familiar face presentation cues capable of eliciting strong P300 responses was developed. Electroencephalogram (EEG) signals were collected from the Oz, POz, CPz and Cz electrode locations on the scalp and subsequently filtered, averaged and used to extract two features. These feature sets were classified using the Nearest Neighbour Approach (NNA). To complement the developed BCI system, a 'drone prototype' capable of simulating six different movements, each over a range of eight distinct selectable distances, was also developed. This was achieved through the construction of a body with 4 movable legs, capable of tilting the main body forward, backward, up and down, as well as a pointer capable of turning left and right.
From ten participants, with normal or corrected to normal vision, an average accuracy of 91.3 ± 4.8% and an ITR of 2.2 ± 1.1 commands/minute (12.2 ± 6.0 bits/minute) was achieved.
The proposed system was shown to elicit strong P300 responses. When compared to similar P300 BCI systems, which utilise a variety of more complex classifiers, competitive accuracy and ITR results were achieved, implying the superiority of the proposed GUI.
This study supports the hypothesis that more research, time and care should be taken when developing GUIs for BCI systems.
为脑机接口（BCI）系统开发一个有效且直观的图形用户界面（GUI），该界面在使用简单分类技术的同时，能实现高分类准确率和信息传输率（ITR）。目标还包括开发一种输出设备，能够实时执行所选命令。
开发了一种基于区域的T9 BCI系统，该系统具有熟悉的面部呈现线索，能够引发强烈的P300反应。从头皮上的Oz、POz、CPz和Cz电极位置采集脑电图（EEG）信号，随后进行滤波、平均，并用于提取两个特征。这些特征集使用最近邻方法（NNA）进行分类。为了完善所开发的BCI系统，还开发了一个“无人机原型”，它能够模拟六种不同的运动，每种运动在八个不同的可选距离范围内。这是通过构建一个带有4条可移动腿的机身来实现的，该机身能够使主体向前、向后、向上和向下倾斜，以及一个能够向左和向右转的指针。
对10名视力正常或矫正后正常的参与者进行测试，平均准确率达到91.3±4.8%，信息传输率为2.2±1.1条命令/分钟（12.2±6.0比特/分钟）。
所提出的系统被证明能引发强烈的P300反应。与使用各种更复杂分类器的类似P300 BCI系统相比，该系统取得了具有竞争力的准确率和信息传输率结果，这意味着所提出的GUI具有优越性。
本研究支持这样一种假设，即在为BCI系统开发GUI时，应该进行更多的研究、投入更多的时间并更加谨慎。
