## 11 Special Issue: \"Research on Biomedical Signal Processing\"
###### Abstract  
Over recent years, the number of signals of a different type that can be acquired from the human body has increased extraordinarily. In addition, the power of digital electronic devices has exploded, making it possible to manage a complex multi-signal environment capable of estimating multivariate time-variant models in real time. Finally, advances in machine learning (ML) theory and the increased availability of large signal databases make the application of deep learning feasible. We believe that the sum of these advancements produces significant achievements in biomedical signal processing, supporting accurate diagnosis and efficient medical decision making. This Special Issue is intended to provide the reader with a collection of research articles that represent examples of a wide and varied array of signals and methods of analysis. With the development of wearable biomedical sensors, the number and duration of biomedical signals have increased significantly, but random noise and external interference affect most of them. Therefore, signal processing techniques have been applied to cancel artifacts and reduce noise, increasing the signal quality for subsequent analysis. The multisignal framework has been used to develop new artifact removal methods. One such methods is artifact subspace reconstruction (ASR). To this end, the paper of [PERSON] et al. [1] evaluated the influence of ASR on EEG signals. The paper of [PERSON] et al. [2] presented a high-computational-efficiency algorithm for QRS complex detection that had been specifically designed for ECG analysis in wearable devices. [PERSON] et al., in their paper [3], proposed continuous human cardiac monitoring by analyzing signals from accelerometers placed on the chest. [PERSON] et al. [4] estimated directed phase-amplitude interactions from EEG data through kernel-based phase transfer entropy. Cross-frequency interactions, a form of oscillatory neural activity, are thought to play an essential role in the integration of distributed information in the brain. Indeed, phase-amplitude interactions are believed to allow for the transfer of information from large-scale brain networks oscillating at low frequencies, to local, rapidly oscillating neural assemblies. Functional near-infrared spectroscopy (fNIRS) is an attractive technology, especially in the field of neuroscience, on account of its non-invasiveness, portability, cost-effectiveness and long-term monitoring capability. However, to date, fNIRS has found limited clinical use due to poor spatial resolution, shallow penetration depth, lack of anatomical specificity and low within-subject reproducibility. Current fNIRS instrumentation provides nonstandard outputs, and objective standardization of preprocessing and analysis pipelines is required to construct homogeneous and standardized databases for application to clinical research. The paper authored by [PERSON] et al. [5] addressed this issue. [PERSON] and [PERSON] [6] compared five different approaches in estimating skeletal muscle oxygen consumption using continuous-wave near-infrared Spectroscopy (CWIRS). Repeated-measures ANOVA have identified significant differences among the five oxygen consumption estimates, meaning that studies using different approaches are not directly comparable. In recent years, the scientific community has developed technologies for constructing exoskeletal devices or prostheses to compensate for the motor deficits of people with disabilities. The interfaces for controlling such robotic devices are mostly based on non-invasive electromyographic (EMG) signaling. [PERSON] et al. [7] proposed a musculoskeletal model based on muscle synergy for the continuous estimation of hand and wrist motion. To separate specific deep-muscle activation, the signals were analyzed using independent component analysis (ICA) and non-negative matrix decomposition (NMF) was then applied to extract muscle synergies. [PERSON] et al.