Editorial for the Special Issue "Sensing Brain Activity Using EEG and Machine Learning".

Sensing brain activity to reveal, analyze and recognize brain activity patterns has become a topic of great interest and ongoing research [...

Integrating EEG and Machine Learning to Analyze Brain Changes during the Rehabilitation of Broca's Aphasia.

The fusion of electroencephalography (EEG) with machine learning is transforming rehabilitation. Our study introduces a neural network model proficient in distinguishing pre- and post-rehabilitation states in patients with Broca's aphasia, based on brain connectivity metrics derived from EEG recordings during verbal and spatial working memory tasks. The Granger causality (GC), phase-locking value (PLV), weighted phase-lag index (wPLI), mutual information (MI), and complex Pearson correlation coefficient (CPCC) across the delta, theta, and low- and high-gamma bands were used (excluding GC, which spanned the entire frequency spectrum).

Assessment of Model Accuracy in Eyes Open and Closed EEG Data: Effect of Data Pre-Processing and Validation Methods.

Eyes open and eyes closed data is often used to validate novel human brain activity classification methods. The cross-validation of models trained on minimally preprocessed data is frequently utilized, regardless of electroencephalography data comprised of data resulting from muscle activity and environmental noise, affecting classification accuracy. Moreover, electroencephalography data of a single subject is often divided into smaller parts, due to limited availability of large datasets.

Dynamic Connectivity Analysis Using Adaptive Window Size.

In this paper, we propose a new method to study and evaluate the time-varying brain network dynamics. The proposed method (relative intersection of confidence intervals for the imaginary component of the complex Pearson correlation coefficient) is based on an adaptive window size and the imaginary part of the complex Pearson correlation coefficient. It reduces the weaknesses of the existing method of constant sliding window analysis with narrow and wide windows.

Complex Pearson Correlation Coefficient for EEG Connectivity Analysis.

In the background of all human thinking-acting and reacting are sets of connections between different neurons or groups of neurons. We studied and evaluated these connections using electroencephalography () brain signals. In this paper, we propose the use of the complex Pearson correlation coefficient , which provides information on connectivity with and without consideration of the volume conduction effect.

Correction to: Image registration in dynamic renal MRI-current status and prospects.

The article Image registration in dynamic renal MRI-current status and prospects, written by Frank G. Zöllner, Amira Šerifović‑Trbalić, Gordian Kabelitz, Marek Kociński, Andrzej Materka and Peter Rogelj, was originally published electronically on the publisher's internet portal on 9 October 2019 without open access.With the author(s)' decision to opt for Open Choice the copyright of the article changed on 24 April 2020 to ©.

Image registration in dynamic renal MRI-current status and prospects.

Magnetic resonance imaging (MRI) modalities have achieved an increasingly important role in the clinical work-up of chronic kidney diseases (CKD). This comprises among others assessment of hemodynamic parameters by arterial spin labeling (ASL) or dynamic contrast-enhanced (DCE-) MRI. Especially in the latter, images or volumes of the kidney are acquired over time for up to several minutes.

Uncertainties of target volume delineation in MRI guided adaptive brachytherapy of cervix cancer: a multi-institutional study.

Background And Aim: We aimed to quantify target volume delineation uncertainties in cervix cancer image guided adaptive brachytherapy (IGABT).

Materials And Methods: Ten radiation oncologists delineated gross tumour volume (GTV), high- and intermediate-risk clinical target volume (HR CTV, IR CTV) in six patients. Their contours were compared with two reference delineations (STAPLE-Simultaneous Truth and Performance Level Estimation and EC- expert consensus) by calculating volumetric and planar conformity index (VCI and PCI) and inter-delineation distances (IDD).

Distance deviation measure of contouring variability.

Background: Several methods that are currently used for contouring analysis have problems providing reliable and/or meaningful results. In this paper a solution to these problems is proposed in a form of a novel measure, which was developed based on requirements defined for contouring studies.

Materials And Methods: The proposed distance deviation measure can be understood as an extension of the closest point measures in such a way that it does not measure only distances between points on contours but rather analyse deviation of distances to both/all contours from each image point/voxel.

Comparison of 3D MRI with high sampling efficiency and 2D multiplanar MRI for contouring in cervix cancer brachytherapy.

Background: MRI sequences with short scanning times may improve accessibility of image guided adaptive brachytherapy (IGABT) of cervix cancer. We assessed the value of 3D MRI for contouring by comparing it to 2D multi-planar MRI.

Patients And Methods: In 14 patients, 2D and 3D pelvic MRI were obtained at IGABT.

Assessment of 3D DCE-MRI of the kidneys using non-rigid image registration and segmentation of voxel time courses.

We have applied automated image analysis methods in the assessment of human kidney perfusion based on 3D dynamic contrast-enhanced MRI data. This approach consists of non-rigid 3D image registration of the moving kidney followed by k-means clustering of the voxel time courses with split between left and right kidney. This method was applied to four data sets acquired from healthy volunteers, using 1.

Symmetric image registration.

This paper presents an original non-rigid image registration approach, which tends to improve the registration by establishing a symmetric image interdependence. In order to gather more information about the image transformation it measures the image similarity in both registration directions. The presented solution is based on the interaction between the images involved in the registration process.

Voxelwise mapping of magnetic resonance ventilation-perfusion ratio in a porcine model by multimodality registration: technical note.

Rationale And Objectives: The purpose of this study was to demonstrate the construction of voxelwise ventilation-perfusion (V/Q) ratio maps in a porcine model by nonrigidly aligning the respective ventilation and perfusion images using a multimodality registration algorithm.

Materials And Methods: The first-pass contrast agent technique for a blood flow map and 3He used for ventilation imaging were performed using a normal porcine model. The registered 3He-ventilation image was then aligned to the blood flow map using a multimodality registration algorithm.