Intelligent Systems Research Laboratory (LARESI), Electronics Department, University of Sciences and Technology of Oran-Mohamed Boudiaf (USTO-MB), El Mnaouar, BP 1505, Bir El Djir 31000, Oran, Algeria.
Signal-Image-Parole (SIMPA) Laboratory, Computer Science Department, University of Sciences and Technology of Oran-Mohamed Boudiaf (USTO-MB), El Mnaouar, BP 1505, Bir El Djir 31000, Oran, Algeria.
Biomed Phys Eng Express. 2024 Mar 13;10(3). doi: 10.1088/2057-1976/ad2f58.
Hybrid Brain-Computer Interface (hBCI) combines multiple neurophysiology modalities or paradigms to speed up the output of a single command or produce multiple ones simultaneously. Concurrent hBCIs that employ endogenous and exogenous paradigms are limited by the reduced set of possible commands. Conversely, the fusion of different exogenous visual evoked potentials demonstrated impressive performances; however, they suffer from limited portability. Yet, sequential hBCIs did not receive much attention mainly due to slower transfer rate and user fatigue during prolonged BCI use (Lorenz et al 2014 J. Neural Eng. 11 035007). Moreover, the crucial factors for optimizing the hybridization remain under-explored. In this paper, we test the feasibility of sequential Event Related-Potentials (ERP) and Steady-State Visual Evoked Potentials (SSVEP) hBCI and study the effect of stimulus order presentation between ERP-SSVEP and SSVEP-ERP for the control of directions and speed of powered wheelchairs or mobile robots with 15 commands. Exploiting the fast single trial face stimulus ERP, SSVEP and modern efficient convolutional neural networks, the configuration with SSVEP presented at first achieved significantly (p < 0.05) higher average accuracy rate with 76.39% ( ± 7.30 standard deviation) hybrid command accuracy and an average Information Transfer Rate (ITR) of 25.05 ( ± 5.32 standard deviation) bits per minute (bpm). The results of the study demonstrate the suitability of a sequential SSVEP-ERP hBCI with challenging dry electroencephalography (EEG) electrodes and low-compute capacity. Although it presents lower ITR than concurrent hBCIs, our system presents an alternative in small screen settings when the conditions for concurrent hBCIs are difficult to satisfy.
混合脑-机接口（hBCI）结合了多种神经生理学模式或范式，以加速单个命令的输出或同时产生多个命令。采用内源性和外源性范式的并发 hBCI 受到可能命令集的限制。相反，不同外源性视觉诱发电位的融合表现出了令人印象深刻的性能；然而，它们受到便携性的限制。然而，由于在长时间使用 BCI 期间传输率较慢和用户疲劳，顺序 hBCI 并没有受到太多关注（Lorenz 等人，2014 年，《神经工程杂志》，11，035007）。此外，优化混合的关键因素仍未得到充分探索。在本文中，我们测试了顺序事件相关电位（ERP）和稳态视觉诱发电位（SSVEP）hBCI 的可行性，并研究了 ERP-SSVEP 和 SSVEP-ERP 之间的刺激顺序呈现对控制动力轮椅或移动机器人方向和速度的影响，使用了 15 个命令。利用快速的单次试验面部刺激 ERP、SSVEP 和现代高效卷积神经网络，首先呈现 SSVEP 的配置显著（p<0.05）提高了混合命令的平均准确率，达到 76.39%（±7.30 标准差），平均信息传输率（ITR）为 25.05（±5.32 标准差）位/分钟（bpm）。研究结果表明，在具有挑战性的干脑电图（EEG）电极和低计算能力的情况下，顺序 SSVEP-ERP hBCI 是合适的。尽管它的 ITR 比并发 hBCI 低，但当并发 hBCI 的条件难以满足时，我们的系统在小屏幕设置中提供了一种替代方案。
