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Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that leads to progressive muscle weakness and paralysis, ultimately resulting in the loss of ability to communicate and control the environment. EEG-based Brain-Computer Interface (BCI) methods have shown promise in providing communication and control with the aim of rehabilitating ALS patients. In particular, P300-based BCI has been widely studied and used for ALS rehabilitation. Other EEG-based BCI methods, such as Motor Imagery (MI) based BCI and Hybrid BCI, have also shown promise in ALS rehabilitation. Nonetheless, EEG-based BCI methods hold great potential for improvement. This review article introduces and reviews FFT, WPD, CSP, CSSP, CSP, and GC feature extraction methods. The Common Spatial Pattern (CSP) is an efficient and common technique for extracting data properties used in BCI systems. In addition, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Networks (NN), and Deep Learning (DL) classification methods were introduced and reviewed. SVM is the most appropriate classifier due to its insensitivity to the curse of dimensionality. Also, DL is used in the design of BCI systems and is a good choice for BCI systems based on motor imagery with big datasets. Despite the progress made in the field, there are still challenges to overcome, such as improving the accuracy and reliability of EEG signal detection and developing more intuitive and user-friendly interfaces By using BCI, disabled patients can communicate with their caregivers and control their environment using various devices, including wheelchairs, and robotic arms. IMPLICATIONS FOR REHABILITATION: Electroencephalography (EEG)-based Brain-Computer Interface (BCI) methods have shown promise in providing communication and control for Amyotrophic Lateral Sclerosis (ALS) patients. EEG constitutes the most significant input in BCIs and can be successfully used in the neuro-rehabilitation of patients with stroke symptoms and amyotrophic lateral sclerosis. EEG based BCIs have the potential to provide a means of communication and control for individuals with severe disabilities.a variety of EEG-based BCI methods have been developed with the aim of rehabilitating disabled patients. [ABSTRACT FROM AUTHOR]

Damage to the central nervous system causes severe consequences for patients and increases medical costs. In spinal cord injuries, due to sensitive nerves, there is a need for a high-precision and non-invasive method to transfer macromolecules to the damaged area of the spinal cord for nerve repair. The main objective of this study is a high-impact strategy for optimal delivery of macromolecules to spinal cord injuries using ultrasound and magnetic fields. Several permanent magnets and transducers of ultrasound waves with different arrangements, sizes, and angles are used. Their effect on the efficiency of delivering macromolecules to the target area is analyzed. The results determined that utilizing three or four magnets along with the transducer of ultrasound waves could have a precise performance in the delivery of macromolecules. Advances in combining ultrasound and magnetic fields in delivering macromolecules to the target area are associated with increased efficiency and accuracy. The interaction between ultrasound and magnetic forces balances the rotation of macromolecules and their useful movement toward the destination. [ABSTRACT FROM AUTHOR]

The aim of modeling musculoskeletal systems is to understand the mechanisms of locomotion control in terms of neurophysiology and neuroanatomy. The complexity and unique nature of neuromuscular systems, however, make control problems in these systems very challenging due to several characteristics including speed and precision. Thus, their investigation requires the use of simple and analyzable methods. Consequently, taking into account the central pattern generator's (CPG) function, we attempted to create a structured chaotic model of how human joints and muscles interact for the purpose of facilitating gait and rehabilitation in patients with incomplete spinal cord injury. The four muscle groups used in this model are gluteus, and hip flexor groups for flexion and extension of the hip joints as well as hamstring muscles and vasti muscles for flexion and extension of the knee joint. The results indicate that the output of the chaotic model of muscle and joint interactions in a healthy state would be chaotic, while in the incomplete spinal cord injury state, it would remain a fixed point. For model rehabilitation, afferent nerve stimulation is used in a CPG model; based on the modeling results, by applying coefficients of 1.98, 2.21, and 3.1 to the values of Ia, II, and Ib afferent nerves, the incomplete spinal cord injury model state is changed from a fix-point to periodic in a permanent fashion, suggesting locomotion with rehabilitation in our model. Based on the results obtained from the chaotic model of muscle and joint interactions as well as the comparisons made with reference papers, it can be stated that this model has acceptable output while enjoying simple computations and can predict different norms. [ABSTRACT FROM AUTHOR]

Coronary artery disease (CAD) causes oscillations in peripheral arteries. Oscillations of the walls of the brachial arteries of 51 patients were recorded [together with the electrocardiogram (ECG)] by an accelerometer at different cuff pressures. By analyzing the energy of the oscillations in the 30-250 Hz band, 16 of 22 patients with CAD and 26 of 29 non-CAD subjects were classified correctly, independent of the ECG, and with no effect of heart murmurs. Index Terms--Arterio-oscillo-graphy, biomedical acoustics, cardiovascular system, coronary artery disease, hemodynamics.