Ogino Mikito, Kanoga Suguru, Muto Masatane, Mitsukura Yasue
Dentsu ScienceJam Inc., Tokyo, Japan.
National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.
Front Hum Neurosci. 2019 Jul 25;13:250. doi: 10.3389/fnhum.2019.00250. eCollection 2019.
An electroencephalogram (EEG)-based brain-computer interface (BCI) is a tool to non-invasively control computers by translating the electrical activity of the brain. This technology has the potential to provide patients who have severe generalized myopathy, such as those suffering from amyotrophic lateral sclerosis (ALS), with the ability to communicate. Recently, auditory oddball paradigms have been developed to implement more practical event-related potential (ERP)-based BCIs because they can operate without ocular activities. These paradigms generally make use of clinical (over 16-channel) EEG devices and natural sound stimuli to maintain the user's motivation during the BCI operation; however, most ALS patients who have taken part in auditory ERP-based BCIs tend to complain about the following factors: (i) total device cost and (ii) setup time. The development of a portable auditory ERP-based BCI could overcome considerable obstacles that prevent the use of this technology in communication in everyday life. To address this issue, we analyzed prefrontal single-channel EEG data acquired from a consumer-grade single-channel EEG device using a natural sound-based auditory oddball paradigm. In our experiments, EEG data was gathered from nine healthy subjects and one ALS patient. The performance of auditory ERP-based BCI was quantified under an offline condition and two online conditions. The offline analysis indicated that our paradigm maintained a high level of detection accuracy (%) and ITR (bits/min) across all subjects through a cross-validation procedure (for five commands: 70.0 ± 16.1 and 1.29 ± 0.93, for four commands: 73.8 ± 14.2 and 1.16 ± 0.78, for three commands: 78.7 ± 11.8 and 0.95 ± 0.61, and for two commands: 85.7 ± 8.6 and 0.63 ± 0.38). Furthermore, the first online analysis demonstrated that our paradigm also achieved high performance for new data in an online data acquisition stream (for three commands: 80.0 ± 19.4 and 1.16 ± 0.83). The second online analysis measured online performances on the different day of offline and first online analyses on a different day (for three commands: 62.5 ± 14.3 and 0.43 ± 0.36). These results indicate that prefrontal single-channel EEGs have the potential to contribute to the development of a user-friendly portable auditory ERP-based BCI.
基于脑电图（EEG）的脑机接口（BCI）是一种通过转换大脑电活动来非侵入性控制计算机的工具。这项技术有潜力为患有严重全身性肌病的患者，如肌萎缩侧索硬化症（ALS）患者，提供沟通能力。最近，听觉Oddball范式已被开发出来，以实现更实用的基于事件相关电位（ERP）的BCI，因为它们可以在无眼动的情况下运行。这些范式通常使用临床（超过16通道）EEG设备和自然声音刺激，以在BCI操作过程中维持用户的积极性；然而，大多数参与基于听觉ERP的BCI的ALS患者往往会抱怨以下因素：（i）设备总成本和（ii）设置时间。开发一种便携式基于听觉ERP的BCI可以克服阻碍该技术在日常生活通信中应用的相当大的障碍。为了解决这个问题，我们使用基于自然声音的听觉Oddball范式分析了从消费级单通道EEG设备获取的前额叶单通道EEG数据。在我们的实验中，从9名健康受试者和1名ALS患者收集了EEG数据。在离线条件和两种在线条件下对基于听觉ERP的BCI的性能进行了量化。离线分析表明，我们的范式通过交叉验证程序在所有受试者中保持了较高的检测准确率（%）和信息传输率（比特/分钟）（对于五个命令：70.0±16.1和1.29±0.93，对于四个命令：73.8±14.2和1.16±0.78，对于三个命令：78.7±11.8和0.95±0.61，对于两个命令：85.7±8.6和0.63±0.38）。此外，第一次在线分析表明，我们的范式在在线数据采集流中对新数据也实现了高性能（对于三个命令：80.0±19.4和1.16±0.83）。第二次在线分析测量了在离线和第一次在线分析不同日期的不同日期的在线性能（对于三个命令：62.5±14.3和0.43±0.36）。这些结果表明，前额叶单通道EEG有潜力有助于开发用户友好的便携式基于听觉ERP的BCI。
