Abbas Waseem, Khan Nadeem Ahmad
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:215-218. doi: 10.1109/EMBC.2018.8512238.
Use of Motor Imagery in EEG signals is gaining importance to develop Brain Computer Interface (BCI) applications in various fields ranging from bio-medical to entertainment. Filter Bank Common Spatial Pattern (FBCSP) algorithm is a promising feature extraction technique to deal with subject-specific behavior in Motor Imagery classification. Using FBCSP on EEG we have developed an accurate but less computationally expensive approach by making use of Time Domain Parameters (TDP) and Band Power (BP) features to form a combined feature set. The novelty of our approach is also the use of optimal time segmentation to overcome non-stationary state behavior of Event-Related Desynchronization (ERD) and Event-Related Synchronization (ERS) over time. We analyzed the impact of parameter variations on classification accuracy and achieved 0.59 mean kappa value for Dataset 2a BCI competition IV, the highest reported for FBCSP approaches, along with the lowest inter-subject variation.
在脑电图（EEG）信号中使用运动想象对于开发从生物医学到娱乐等各个领域的脑机接口（BCI）应用正变得越来越重要。滤波器组公共空间模式（FBCSP）算法是一种很有前景的特征提取技术，可用于处理运动想象分类中特定于个体的行为。通过在脑电图上使用FBCSP，我们利用时域参数（TDP）和频段功率（BP）特征开发了一种准确但计算成本较低的方法，以形成一个组合特征集。我们方法的新颖之处还在于使用了最优时间分割，以克服事件相关去同步化（ERD）和事件相关同步化（ERS）随时间的非平稳状态行为。我们分析了参数变化对分类准确率的影响，对于BCI竞赛IV的数据集2a，我们实现了0.59的平均kappa值，这是FBCSP方法所报告的最高值，同时受试者间差异也是最低的。
