Sensor Space Time-Varying Information Flow Analysis of Multiclass Motor Imagery through Kalman Smoother and EM Algorithm

被引：0

作者：

Hamedi, Mahyar ^{[1
]}

Salleh, Sh-Hussain ^{[1
]}

Ting, Chee-Ming ^{[1
]}

Samdin, S. Balqis ^{[1
]}

Noor, Alias Mohd ^{[1
]}

机构：

[1] Univ Teknol Malaysia, Ctr Biomed Engn, Transportat Res Alliance, Johor Baharu, Malaysia

来源：

2015 INTERNATIONAL CONFERENCE ON BIOSIGNAL ANALYSIS, PROCESSING AND SYSTEMS (ICBAPS) | 2015年

关键词：

brain connectivity analysis; motor imagery movement; sensor space connectivity; electroencephalogram; state space model; EEG ACTIVITY; PROPAGATION; MOVEMENT;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Inter-channel time-varying (TV) relationships of scalp neural recordings offer deep understanding of the brain sensory and cognitive functions. This paper develops a state space-based TV multivariate autoregressive (MVAR) model for estimating TV-information flow (IF) recruited by different motor imagery (MI) movements. TV model coefficients are computed through Kalman filter (KF) by incorporating Kalman smoothing approach and expectation-maximization algorithm for model parameter estimation, KS-EM. Volume conduction (VC) problem is also addressed by considering full noise covariate in observation equation. An automated model initialization is also implemented to deliver optimal estimates. TV-partial directed coherence derived from the proposed model is applied for IF analysis. The performance of KS-EM is assessed and compared with dual extended KF and overlapping sliding window-based MVAR models using simulated data. Finally, TV-IF during four different MI movements is studied. Results show the superiority of KS-EM for tracking the rapid signal parameter changes and eliminating the VC effect in the sensor space EEG. Differences in contralateral/ipsilateral TV-IF around alpha and lower beta bands during each MI task reveal the high potential of this feature for BCI applications.

引用

页数：5