Department of Biomedical Engineering, Kyung Hee University, Yongin, Republic of Korea.
Med Biol Eng Comput. 2012 Mar;50(3):231-41. doi: 10.1007/s11517-012-0861-4. Epub 2012 Jan 17.
P300 is a positive event-related potential used by P300-brain computer interfaces (BCIs) as a means of communication with external devices. One of the main requirements of any P300-based BCI is accuracy and time efficiency for P300 extraction and detection. Among many attempted techniques, independent component analysis (ICA) is currently the most popular P300 extraction technique. However, since ICA extracts multiple independent components (ICs), its use requires careful selection of ICs containing P300 responses, which limits the number of channels available for computational efficiency. Here, we propose a novel procedure for P300 extraction and detection using constrained independent component analysis (cICA) through which we can directly extract only P300-relevant ICs. We tested our procedure on two standard datasets collected from healthy and disabled subjects. We tested our procedure on these datasets and compared their respective performances with a conventional ICA-based procedure. Our results demonstrate that the cICA-based method was more reliable and less computationally expensive, and was able to achieve 97 and 91.6% accuracy in P300 detection from healthy and disabled subjects, respectively. In recognizing target characters and images, our approach achieved 95 and 90.25% success in healthy and disabled individuals, whereas use of ICA only achieved 83 and 72.25%, respectively. In terms of information transfer rate, our results indicate that the ICA-based procedure optimally performs with a limited number of channels (typically three), but with a higher number of available channels (>3), its performance deteriorates and the cICA-based one performs better.
P300 是一种正事件相关电位，被 P300 脑机接口（BCI）用作与外部设备进行通信的手段。任何基于 P300 的 BCI 的主要要求之一是 P300 提取和检测的准确性和时间效率。在许多尝试的技术中，独立成分分析（ICA）是目前最流行的 P300 提取技术。然而，由于 ICA 提取多个独立成分（ICs），因此需要仔细选择包含 P300 响应的 ICs，这限制了用于计算效率的通道数量。在这里，我们提出了一种使用约束独立成分分析（cICA）提取和检测 P300 的新方法，通过该方法，我们可以直接提取仅与 P300 相关的 ICs。我们在从健康和残疾受试者收集的两个标准数据集上测试了我们的程序。我们在这些数据集上测试了我们的程序，并将它们各自的性能与传统的基于 ICA 的程序进行了比较。我们的结果表明，基于 cICA 的方法更可靠，计算成本更低，能够分别从健康和残疾受试者中实现 97%和 91.6%的 P300 检测准确率。在识别目标字符和图像时，我们的方法在健康和残疾个体中分别实现了 95%和 90.25%的成功率，而仅使用 ICA 分别实现了 83%和 72.25%的成功率。就信息传输率而言，我们的结果表明，基于 ICA 的程序在有限数量的通道（通常为三个）下表现最佳，但随着可用通道数量的增加（>3），其性能会恶化，而基于 cICA 的程序表现更好。
