Kasawala Ekgari, Mouli Surej
Engineering for Health Research Group, Biomedical Engineering, Aston University, Aston Street, Birmingham B4 7ET, UK.
Sensors (Basel). 2025 Mar 14;25(6):1802. doi: 10.3390/s25061802.
In brain-computer interface (BCI) systems, steady-state visual-evoked potentials (SSVEP) and P300 responses have achieved widespread implementation owing to their superior information transfer rates (ITR) and minimal training requirements. These neurophysiological signals have exhibited robust efficacy and versatility in external device control, demonstrating enhanced precision and scalability. However, conventional implementations predominantly utilise liquid crystal display (LCD)-based visual stimulation paradigms, which present limitations in practical deployment scenarios. This investigation presents the development and evaluation of a novel light-emitting diode (LED)-based dual stimulation apparatus designed to enhance SSVEP classification accuracy through the integration of both SSVEP and P300 paradigms. The system employs four distinct frequencies-7 Hz, 8 Hz, 9 Hz, and 10 Hz-corresponding to forward, backward, right, and left directional controls, respectively. Oscilloscopic verification confirmed the precision of these stimulation frequencies. Real-time feature extraction was accomplished through the concurrent analysis of maximum Fast Fourier Transform (FFT) amplitude and P300 peak detection to ascertain user intent. Directional control was determined by the frequency exhibiting maximal amplitude characteristics. The visual stimulation hardware demonstrated minimal frequency deviation, with error differentials ranging from 0.15% to 0.20% across all frequencies. The implemented signal processing algorithm successfully discriminated between all four stimulus frequencies whilst correlating them with their respective P300 event markers. Classification accuracy was evaluated based on correct task intention recognition. The proposed hybrid system achieved a mean classification accuracy of 86.25%, coupled with an average ITR of 42.08 bits per minute (bpm). These performance metrics notably exceed the conventional 70% accuracy threshold typically employed in BCI system evaluation protocols.
在脑机接口（BCI）系统中，稳态视觉诱发电位（SSVEP）和P300响应因其卓越的信息传输速率（ITR）和最低的训练要求而得到广泛应用。这些神经生理信号在外部设备控制中表现出强大的功效和通用性，展示出更高的精度和可扩展性。然而，传统的实现方式主要采用基于液晶显示器（LCD）的视觉刺激范式，这在实际部署场景中存在局限性。本研究介绍了一种新型基于发光二极管（LED）的双刺激装置的开发和评估，该装置旨在通过整合SSVEP和P300范式来提高SSVEP分类准确率。该系统采用四个不同的频率——7赫兹、8赫兹、9赫兹和10赫兹，分别对应向前、向后、向右和向左的方向控制。示波器验证证实了这些刺激频率的精度。通过同时分析最大快速傅里叶变换（FFT）幅度和P300峰值检测来完成实时特征提取，以确定用户意图。方向控制由表现出最大幅度特征的频率决定。视觉刺激硬件的频率偏差极小，所有频率的误差差异在0.15%至0.20%之间。所实施的信号处理算法成功地区分了所有四个刺激频率，并将它们与各自的P300事件标记相关联。基于正确的任务意图识别来评估分类准确率。所提出的混合系统实现了86.25%的平均分类准确率，平均ITR为每分钟42.08比特（bpm）。这些性能指标显著超过了BCI系统评估协议中通常采用的70%准确率阈值。
