School of Computer Science & Engineering, Taylor's University, Jalan Taylors, Subang Jaya 47500, Malaysia.
School of Computer Science, Nusa Putra University, Jl. Raya Cibolang No.21, Sukabumi 43152, Indonesia.
Sensors (Basel). 2021 Dec 15;21(24):8370. doi: 10.3390/s21248370.
In real-life applications, electroencephalogram (EEG) signals for mental stress recognition require a conventional wearable device. This, in turn, requires an efficient number of EEG channels and an optimal feature set. This study aims to identify an optimal feature subset that can discriminate mental stress states while enhancing the overall classification performance. We extracted multi-domain features within the time domain, frequency domain, time-frequency domain, and network connectivity features to form a prominent feature vector space for stress. We then proposed a hybrid feature selection (FS) method using minimum redundancy maximum relevance with particle swarm optimization and support vector machines (mRMR-PSO-SVM) to select the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art metaheuristic methods. The proposed model significantly reduced the features vector space by an average of 70% compared with the state-of-the-art methods while significantly increasing overall detection performance.
在实际应用中，用于识别精神压力的脑电图 (EEG) 信号需要传统的可穿戴设备。这反过来又需要高效数量的 EEG 通道和最佳的特征集。本研究旨在确定一个最佳的特征子集，在增强整体分类性能的同时，可以区分精神压力状态。我们在时域、频域、时频域和网络连通性特征内提取了多域特征，形成了一个突出的压力特征向量空间。然后，我们提出了一种混合特征选择 (FS) 方法，使用最小冗余最大相关性与粒子群优化和支持向量机 (mRMR-PSO-SVM) 来选择最佳特征子集。使用四个数据集，即 EDMSS、DEAP、SEED 和 EDPMSC，评估和验证了所提出方法的性能。为了进一步巩固，将所提出的方法与最先进的元启发式方法的有效性进行了比较。与最先进的方法相比，所提出的模型平均将特征向量空间减少了 70%，同时显著提高了整体检测性能。
