Ispir Hamza Polat Vocational College, Atatürk University, Erzurum, Turkey.
Department of Electrical and Electronics Engineering, Karadeniz Technical University, Trabzon, Turkey.
PLoS One. 2022 Apr 12;17(4):e0265904. doi: 10.1371/journal.pone.0265904. eCollection 2022.
The event related P300 potentials, positive waveforms in electroencephalography (EEG) signals, are often utilized in brain computer interfaces (BCI). Many studies have been carried out to improve the performance of P300 speller systems either by developing signal processing algorithms and classifiers with different architectures or by designing new paradigms. In this study, a new paradigm is proposed for this purpose. The proposed paradigm combines two remarkable properties of being a 3D animation and utilizing column-only flashings as opposed to classical paradigms which are based on row-column flashings in 2D manner. The new paradigm is utilized in a traditional two-layer artificial neural networks model with a single output neuron, and numerous experiments are conducted to evaluate and compare the performance of the proposed paradigm with that of the classical approach. The experimental results, including statistical significance tests, are presented for single and multiple EEG electrode usage combinations in 1, 3 and 15 flashing repetitions to detect P300 waves as well as to recognize target characters. Using the proposed paradigm, the best average classification accuracy rates on the test data are improved from 89.97% to 93.90% (an improvement of 4.36%) for 1 flashing, from 97.11% to 98.10% (an improvement of 1.01%) for 3 flashings and from 99.70% to 99.81% (an improvement of 0.11%) for 15 flashings when all electrodes, included in the study, are utilized. On the other hand, the accuracy rates are improved by 9.69% for 1 flashing, 4.72% for 3 flashings and 1.73% for 15 flashings when the proposed paradigm is utilized with a single EEG electrode (P8). It is observed that the proposed speller paradigm is especially useful in BCI systems designed for few EEG electrodes usage, and hence, it is more suitable for practical implementations. Moreover, all participants, given a subjective test, declared that the proposed paradigm is more user-friendly than classical ones.
事件相关 P300 电位是脑电图（EEG）信号中的正波，常用于脑机接口（BCI）。许多研究通过开发具有不同架构的信号处理算法和分类器或设计新范式来提高 P300 拼写器系统的性能。为此，本研究提出了一种新的范式。该范式结合了成为 3D 动画和使用柱状闪烁的两个显著特性，与基于 2D 行-列闪烁的经典范式相反。新范式用于具有单个输出神经元的传统两层人工神经网络模型，并进行了大量实验来评估和比较所提出的范式与经典方法的性能。实验结果包括统计意义检验，用于在 1、3 和 15 次闪烁重复中使用单个和多个 EEG 电极组合来检测 P300 波以及识别目标字符。使用所提出的范式，在使用所有研究中包含的电极时，最佳平均分类准确率在 1 次闪烁时从 89.97%提高到 93.90%（提高了 4.36%），在 3 次闪烁时从 97.11%提高到 98.10%（提高了 1.01%），在 15 次闪烁时从 99.70%提高到 99.81%（提高了 0.11%）。另一方面，当使用单个 EEG 电极（P8）时，准确率在 1 次闪烁时提高了 9.69%，在 3 次闪烁时提高了 4.72%，在 15 次闪烁时提高了 1.73%。观察到，所提出的拼写器范式特别适用于使用少数 EEG 电极的 BCI 系统，因此更适合实际应用。此外，所有参与者在进行主观测试时都表示，所提出的范式比经典范式更友好。
