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首页> 外文期刊>Journal of Neuroscience Methods >Bilateral adaptation and neurofeedback for brain computer interface system.
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Bilateral adaptation and neurofeedback for brain computer interface system.

机译：脑计算机接口系统的双边适应和神经反馈。

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摘要

Brain computer interface (BCI) provides an alternative communication pathway between human brain and external devices without the participation of peripheral nerves and muscles. Although the BCI techniques have been developing quickly in recent decades, there still exist a number of unsolved problems, such as instability, unreliability and low transmission rate in real time applications of BCI. In the present study, we design a bilateral training framework for both human and the BCI system to improve recognition accuracy and to reduce the impact caused by non-stationary EEG signal. The statistical analysis is used to test whether there is an obvious improvement in recognition performance after using the bilateral adaptation strategy. The statistical analysis indicates that our algorithm is significantly different from the existing method in both conditions of trials (p=0.0073) and sliding time windows (p=0.00077). The results of statistical analysis reconfirm that performance using our algorithm is distinctly improved. The online experiments also demonstrate that the proposed algorithm achieves higher prediction accuracy and reliability compared with the existing method. The objective of our research is to transfer this strategy to some practical applications (e.g., electrical wheelchair control) for the better performance.
机译：大脑计算机接口（BCI）提供了人脑与外部设备之间的另一种通信途径，而没有周围神经和肌肉的参与。尽管近几十年来BCI技术发展迅速，但是在BCI的实时应用中仍然存在许多未解决的问题，例如不稳定，不可靠和低传输速率。在本研究中，我们为人和BCI系统设计了一个双边训练框架，以提高识别准确性并减少由非平稳EEG信号引起的影响。统计分析用于检验使用双边适应策略后识别性能是否有明显改善。统计分析表明，我们的算法在试验条件（p = 0.0073）和滑动时间窗（p = 0.00077）方面均与现有方法有显着差异。统计分析的结果再次证明，使用我们的算法可以显着提高性能。在线实验还表明，与现有方法相比，该算法具有更高的预测精度和可靠性。我们研究的目的是将这种策略转移到某些实际应用中（例如电动轮椅控制），以获得更好的性能。

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