EEG Classification of Imaginary Left and Right Leg Movements based on Multivariate Empirical Mode Decomposition and Non-Linear Support Vector Machine

Motor-imagery brain-computer interfaces, as rehabilitation tools for motor-disabled individuals, could inherently enrich neuroplasticity and subsequently restore mobility. However, this endeavour's significant challenge is classifying left and right leg motor imagery tasks from non-stationary EEG signals. A subject-independent feature extraction method is essential in a BCI system, and this work involves developing a subject-independent algorithm to classify left/right leg motion intention. The Multivariate Empirical Mode Decomposition was used to decompose EEG during left and right foot movements during imagery tasks. We validated our proposed algorithm using open-access motor imagery data to detect the user's mental intention from EEG. Five subjects of various performance categories with almost 150 trials for each left/right leg MI of hand/leg/tongue, HaLT Paradigm, utilizing C3, C4, and Cz channels were examined to generalize this study to all subjects. A set of statistical features were extracted from the intrinsic mode functions, and the most relevant features were selected for classification using Sequential Floating Feature Selection. Different classifiers were trained using extracted features, and their performances' were evaluated. The findings suggest that the non-linear support vector machine is the best classification model, resulting in the mean classification sensitivity, specificity, precision, negative predictive value, F-measure, 98.15%, 90.74%, 91.97%, 98.33%, 94.72%, 94.44%, respectively. The proposed subject-independent signal processing method significantly improved the offline calibration mode by eliminating the frequency selection step, making it the common-used method for different types of MI-based BCI participants. Offline evaluations suggest that it can lead to significant increases in classification accuracy in comparison to current approaches.

Investigating exospheric Na distributions at different spatial scales to disentangle between single and double peaked global patterns

<p>Since mid ‘80s the Na exosphere of Mercury has been investigated by means of both ground-based observations and spacecraft measurements, showing a wide range of variability from tens of minutes up to seasonal variations along the planetary orbit. It has been shown that the most common Na distribution is characterized by a high latitude double peak probably related to solar wind ion precipitation through the polar cusps. However, the existence of a single peaked equatorial Na emission has been frequently observed too. Generally, it is not straightforward to recognize the contributions due to different surface release processes that produces the observed Na exospheric global image.</p> <p>Here we apply the Multivariate Empirical Mode Decomposition (MEMD) to a dataset of images of the exospheric Na emission collected by the THEMIS ground-based telescope with the goal to disentangle the different contributions operating at different scales that are expected to be responsible of the occurrence of single vs. double peaked emissions or exospheric asymmetries. In particular, we found the existence of a wide range of scales characterizing both type of spatial patterns, ranging from small scales (less than 0.5 Mercury radii) up to large scales (about 1-2 Mercury radii). These scale-dependent patterns can be linked to different source mechanisms as the variability of solar wind magnetic field, different surface release mechanisms (thermal desorption, photon-stimulated desorption, micrometeoroid impact vaporization and ion-sputtering), as well as, to the whole Na exosphere surrounding the Hermean environment. Our conclusions are double checked by applying the MEMD both on Na exospheric measurements and on simulations of the Na exosphere as created by the different source mechanisms. The positive results show the great potential of the MEMD technique to study the complex environment of planetary exospheres and recognize the different components/processes that create it.</p>

Multivariate empirical mode decomposition–based structural damage localization using limited sensors

In this article, multivariate empirical mode decomposition is proposed for damage localization in structures using limited measurements. Multivariate empirical mode decomposition is first used to decompose the acceleration responses into their mono-component modal responses. The major contributing modal responses are then used to evaluate the modal energy for the respective modes. A damage localization feature is proposed by calculating the percentage difference in the modal energies of damaged and undamaged structures, followed by the determination of the threshold value of the feature. The feature of the specific sensor location exceeding the threshold value is finally used to identify the location of structural damage. The proposed method is validated using a suite of numerical and full-scale studies. The validation is further explored using various limited measurement cases for evaluating the feasibility of using a fewer number of sensors to enable cost-effective structural health monitoring. The results show the capability of the proposed method in identifying as minimal as 2% change in global modal parameters of structures, outperforming the existing time–frequency methods to delineate such minor global damage.