基于SSVEP的脑机接口的生物启发滤波器组
Bio-Inspired Filter Banks for SSVEP-based Brain-Computer Interfaces
摘要：
脑机接口（BCI）通过提供一种替代的通信和控制方式，有可能在未来的医疗保健技术中发挥至关重要的作用。更具体地，基于稳态视觉诱发电位（SSVEP）的脑机接口具有更高的准确性和更高的信息传送率（ITR）的优点。为了充分利用这些设备的能力，有必要了解SSVEP的特点，并设计系统考虑其生物学特性。本文介绍了一种新的SSVEP频率检测方法的生物启发滤波器组（BIFB）。已知对闪烁视觉刺激的SSVEP响应是频率选择性的，并且随着刺激频率的增加而变弱。在所提出的方法中，滤波器的增益和带宽的设计和调谐的基础上，这些特性，同时还纳入谐波SSVEP响应。该方法不仅提高了准确性，而且通过允许使用引起弱SSVEP响应的刺激频率来增加可用命令的数量。BIFB方法取得了可靠的性能进行测试时，数据集在线和比较两个著名的SSVEP频率检测方法，功率谱密度分析（PSDA）和典型相关分析（CCA）。结果表明，生物启发设计的潜力，这将被扩展到包括进一步的SSVEP特性（例如，时域波形），为未来的SSVEP为基础的BCI。
英文摘要：
Brain-computer interfaces (BCI) have the potential to play a vital role in future healthcare technologies by providing an alternative way of communication and control. More specifically, steady-state visual evoked potential (SSVEP) based BCIs have the advantage of higher accuracy and higher information transfer rate (ITR). In order to fully exploit the capabilities of such devices, it is necessary to understand the features of SSVEP and design the system considering its biological characteristics. This paper introduces bio-inspired filter banks (BIFB) for a novel SSVEP frequency detection method. It is known that SSVEP response to a flickering visual stimulus is frequency selective and gets weaker as the frequency of the stimuli increases. In the proposed approach, the gain and bandwidth of the filters are designed and tuned based on these characteristics while also incorporating harmonic SSVEP responses. This method not only improves the accuracy but also increases the available number of commands by allowing the use of stimuli frequencies elicit weak SSVEP responses. The BIFB method achieved reliable performance when tested on datasets available online and compared with two well-known SSVEP frequency detection methods, power spectral density analysis (PSDA) and canonical correlation analysis (CCA). The results show the potential of bio-inspired design which will be extended to include further SSVEP characteristic (e.g. time-domain waveform) for future SSVEP based BCIs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
