Novičić Marija, Djordjević Olivera, Miler-Jerković Vera, Konstantinović Ljubica, Savić Andrej M
School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia.
Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia.
Sensors (Basel). 2024 Dec 17;24(24):8048. doi: 10.3390/s24248048.
Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users' selective tactile attention. The experimental protocol involved ten healthy subjects performing a tactile attention task, with EEG signals recorded from five EEG channels over the sensory-motor cortex. We employed sequential forward selection (SFS) of features from temporal sERP waveforms of all EEG channels. We systematically tested classification performance using machine learning algorithms, including logistic regression, k-nearest neighbors, support vector machines, random forests, and artificial neural networks. We explored the effects of the number of stimuli required to obtain sERP features for classification and their influence on accuracy and information transfer rate. Our approach indicated significant improvements in classification accuracy compared to previous studies. We demonstrated that the number of stimuli for sERP generation can be reduced while increasing the information transfer rate without a statistically significant decrease in classification accuracy. In the case of the support vector machine classifier, we achieved a mean accuracy over 90% for 10 electrical stimuli, while for 6 stimuli, the accuracy decreased by less than 7%, and the information transfer rate increased by 60%. This research advances methods for tactile BCI control based on event-related potentials. This work is significant since tactile stimulation is an understudied modality for BCI control, and electrically induced sERPs are the least studied control signals in reactive BCIs. Exploring and optimizing the parameters of sERP elicitation, as well as feature extraction and classification methods, is crucial for addressing the accuracy versus speed trade-off in various assistive BCI applications where the tactile modality may have added value.
传统的触觉脑机接口（BCI），尤其是基于稳态体感诱发电位的接口，面临着诸如准确性较低、比特率降低以及需要空间上远距离的刺激点等挑战。相比之下，使用瞬态电刺激为生成触觉BCI控制信号——体感事件相关电位（sERP）提供了一种有前景的替代方案。本研究旨在通过对sERP信号采用先进的特征提取和机器学习技术，以优化一种新型电触觉BCI的性能，用于对用户的选择性触觉注意力进行分类。实验方案包括十名健康受试者执行一项触觉注意力任务，同时从感觉运动皮层的五个脑电图通道记录脑电图信号。我们从所有脑电图通道的时间sERP波形中采用顺序前向选择（SFS）特征。我们使用包括逻辑回归、k近邻、支持向量机、随机森林和人工神经网络在内的机器学习算法系统地测试分类性能。我们探讨了获取用于分类的sERP特征所需的刺激数量的影响及其对准确性和信息传输率的影响。我们的方法表明与先前的研究相比，分类准确性有显著提高。我们证明了在不使分类准确性出现统计学显著下降的情况下，可以减少用于生成sERP的刺激数量，同时提高信息传输率。对于支持向量机分类器，在10次电刺激时我们实现了超过90%的平均准确率，而对于6次刺激，准确率下降不到7%，信息传输率提高了60%。本研究推进了基于事件相关电位的触觉BCI控制方法。这项工作意义重大，因为触觉刺激是BCI控制中一个研究较少的模式，并且电诱发的sERP是反应性BCI中研究最少的控制信号。探索和优化sERP诱发参数以及特征提取和分类方法，对于解决各种辅助BCI应用中触觉模式可能具有附加价值的准确性与速度权衡问题至关重要。
