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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses
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Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses

机译：试用期短的混合EEG-NIRS脑机接口提高信息传输率：离线和伪在线分析

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Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are non-invasive neuroimaging methods that record the electrical and metabolic activity of the brain, respectively. Hybrid EEG-NIRS brain-computer interfaces (hBCIs) that use complementary EEG and NIRS information to enhance BCI performance have recently emerged to overcome the limitations of existing unimodal BCIs, such as vulnerability to motion artifacts for EEG-BCI or low temporal resolution for NIRS-BCI. However, with respect to NIRS-BCI, in order to fully induce a task-related brain activation, a relatively long trial length (≥10 s) is selected owing to the inherent hemodynamic delay that lowers the information transfer rate (ITR; bits/min). To alleviate the ITR degradation, we propose a more practical hBCI operated by intuitive mental tasks, such as mental arithmetic (MA) and word chain (WC) tasks, performed within a short trial length (5 s). In addition, the suitability of the WC as a BCI task was assessed, which has so far rarely been used in the BCI field. In this experiment, EEG and NIRS data were simultaneously recorded while participants performed MA and WC tasks without preliminary training and remained relaxed (baseline; BL). Each task was performed for 5 s, which was a shorter time than previous hBCI studies. Subsequently, a classification was performed to discriminate MA-related or WC-related brain activations from BL-related activations. By using hBCI in the offline/pseudo-online analyses, average classification accuracies of 90.0 ± 7.1/85.5 ± 8.1% and 85.8 ± 8.6/79.5 ± 13.4% for MA vs. BL and WC vs. BL, respectively, were achieved. These were significantly higher than those of the unimodal EEG- or NIRS-BCI in most cases. Given the short trial length and improved classification accuracy, the average ITRs were improved by more than 96.6% for MA vs. BL and 87.1% for WC vs. BL, respectively, compared to those reported in previous studies. The suitability of implementing a more practical hBCI based on intuitive mental tasks without preliminary training and with a shorter trial length was validated when compared to previous studies.
机译：脑电图（EEG）和近红外光谱（NIRS）是分别记录大脑电活动和代谢活动的非侵入性神经成像方法。最近出现了使用互补的EEG和NIRS信息来增强BCI性能的混合EEG-NIRS脑机接口（hBCI），以克服现有单峰BCI的局限性，例如针对EEG-BCI的运动伪影的脆弱性或针对NIRS的低时间分辨率-BCI。但是，对于NIRS-BCI，由于内在的血液动力学延迟会降低信息传输速率（ITR;位//），因此为了充分诱导与任务相关的大脑激活，需要选择相对较长的试验长度（≥10s）。分钟）。为了减轻ITR的降低，我们提出了一种更实用的hBCI，它通过直观的心理任务（例如心理算术（MA）和单词链（WC）任务）在较短的试用时间内（5 s）执行。另外，还评估了WC作为BCI任务的适用性，到目前为止，在BCI领域中很少使用。在该实验中，同时记录参与者的脑电图和近红外光谱数据，而参与者无需进行初步培训就可以执行MA和WC任务，并且保持放松（基线； BL）。每个任务执行5 s，比以前的hBCI研究要短。随后，进行分类以将MA相关或WC相关的大脑激活与BL相关的激活区分开。通过在离线/伪在线分析中使用hBCI，MA与BL和WC与BL的平均分类精度分别达到90.0±7.1 / 85.5±8.1％和85.8±8.6 / 79.5±13.4％。在大多数情况下，这些指标显着高于单峰EEG-或NIRS-BCI。考虑到较短的试验时间和改进的分类准确性，与以前的研究相比，MA与BL的平均ITR分别提高了96.6％以上，而WC与BL的平均ITR分别提高了87.1％以上。与以前的研究相比，已经证实了基于直观的心理任务实施更实用的hBCI的适用性，而无需进行初步培训并且具有较短的试验时间。

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