重叠特征策略与参数优化的运动想象脑电模式识别

针对运动想象脑电信号的非线性、非平稳特性,提出重叠特征策略与参数优化方法.通过重叠频带滤波(OFB)进行预处理,在滤波后的信号上提取共同空间模式特征(CSP).将OFB-CSP特征输入鲁棒支持矩阵机,完成模式识别,在模式识别中通过校正粒子群算法(CPSO)动态调整被试个体最优参数.在两个公开数据集上进行实验,分别验证OFB预处理可提升CSP特征区分度,CPSO可为个体寻找最优的鲁棒支持矩阵机分类参数.文中方法提升运动想象识别率,样本和计算资源需求较小,适合脑机接口的实际应用.

Overlapped Features Strategy and Parameters Optimization Patterns Recognition for Motor Imagery EEG

Aiming at nonlinear and non-stationary characteristics of motor imagery(MI) based electroencephalogram, a features extraction and patterns recognition algorithm is proposed. Firstly, overlapped filter bank(OFB) pre-processing is conducted. Then, the common spatial patterns(CSP) algorithm is applied to the filtered electroencephalogram(EEG) signals. Afterwards, the OFB-CSP features are incorporated into robust support matrix machine(RSMM) for MI patterns recognition, and the corrected particle swarm optimization(CPSO) algorithm is utilized to dynamically adjust the optimal parameters for RSMM classification. Experiments on two public datasets show that OFB pre-processing improves the discrimination of CSP features. Besides, the optimal parameters for EEG signals of individuals are searched by CSPO to the RSMM classifier. Compared with the state-of-the-arts algorithms, the proposed algorithm significantly improves MI classification accuracy. With less requirements of samples and computational resources, the proposed overlapped features strategy and parameters optimization algorithm is suitable for real-world brain computer interface application.