Classification of lower limb motor imagery based on iterative EEG source localization and feature fusion

Motor imagery (MI) brain–computer interface (BCI) systems have broad application prospects in rehabilitation and other fields. However, to achieve accurate and practical MI-BCI applications, there are still several critical issues, such as channel selection, electroencephalogram (EEG) feature extraction and EEG classification, needed to be better resolved. In this paper, these issues are studied for lower limb MI which is more difficult and less studied than upper limb MI. First, a novel iterative EEG source localization method is proposed for channel selection. Channels FC1, FC2, C1, C2 and Cz, instead of the commonly used traditional channel set (TCS) C3, C4 and Cz, are selected as the optimal channel set (OCS). Then, a multi-domain feature (MDF) extraction algorithm is presented to fuse single-domain features into multi-domain features. Finally, a particle swarm optimization based support vector machine (SVM) method is utilized to classify the EEG data collected by the lower limb MI experiment designed by us. The results show that the classification accuracy is 88.43%, 3.35–5.41% higher than those of using traditional SVM to classify single-domain features on the TCS, which proves that the combination of OCS and MDF can not only reduce the amount of data processing, but also retain more feature information to improve the accuracy of EEG classification.

High-Resolution EEG Source Localization in Segmentation-Free Head Models Based on Finite-Difference Method and Matching Pursuit Algorithm

Electroencephalogram (EEG) is a method to monitor electrophysiological activity on the scalp, which represents the macroscopic activity of the brain. However, it is challenging to identify EEG source regions inside the brain based on data measured by a scalp-attached network of electrodes. The accuracy of EEG source localization significantly depends on the type of head modeling and inverse problem solver. In this study, we adopted different models with a resolution of 0.5 mm to account for thin tissues/fluids, such as the cerebrospinal fluid (CSF) and dura. In particular, a spatially dependent conductivity (segmentation-free) model created using deep learning was developed and used for more realist representation of electrical conductivity. We then adopted a multi-grid-based finite-difference method (FDM) for forward problem analysis and a sparse-based algorithm to solve the inverse problem. This enabled us to perform efficient source localization using high-resolution model with a reasonable computational cost. Results indicated that the abrupt spatial change in conductivity, inherent in conventional segmentation-based head models, may trigger source localization error accumulation. The accurate modeling of the CSF, whose conductivity is the highest in the head, was an important factor affecting localization accuracy. Moreover, computational experiments with different noise levels and electrode setups demonstrate the robustness of the proposed method with segmentation-free head model.

Violence Exposure Is Associated With Atypical Appraisal of Threat Among Women: An EEG Study

IntroductionThe present study investigates the association of lifetime interpersonal violence (IPV) exposure, related posttraumatic stress disorder (IPV-PTSD), and appraisal of the degree of threat posed by facial avatars.MethodsWe recorded self-rated responses and high-density electroencephalography (HD-EEG) among women, 16 of whom with lifetime IPV-PTSD and 14 with no PTSD, during a face-evaluation task that displayed male face avatars varying in their degree of threat as rated along dimensions of dominance and trustworthiness.ResultsThe study found a significant association between lifetime IPV exposure, under-estimation of dominance, and over-estimation of trustworthiness. Characterization of EEG microstates supported that lifetime IPV-PTSD modulates emotional appraisal, specifically in encoding and decoding processing associated with N170 and LPP evoked potentials. EEG source localization demonstrated an overactivation of the limbic system, in particular the parahippocampal gyrus, in response to non-threatening avatars. Additionally, dysfunctional involvement of attention-related processing anterior prefrontal cortex (aPFC) was found in response to relatively trustworthy avatars in IPV-PTSD individuals compared with non-PTSD controls.DiscussionThis study showed that IPV exposure and related PTSD modulate individuals’ evaluation of facial characteristics suggesting threat. Atypical processing of these avatar characteristics was marked by group differences in brain regions linked to facial processing, emotion regulation, and memory.