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We investigated the correlation among brain and leg muscle activations by analyzing Electroencephalogram (EEG) and Electromyogram (EMG) signals in different conditions. Twelve subjects performed four tasks, including (1) quarter turns, (2) U-turns, (3) bypass obstacles, and (4) repeating quarter turns and U-turns two times. Then, we quantified the alterations of the complexity of these signals by computing the fractal dimension and sample entropy. The results showed that EEG and EMG signals in the case of the first task are more complex than the second task, in which they are more complex than the third task. Furthermore, the brain and muscle signals show the least complexity in the case of the fourth task. Moreover, we found strong correlations in the variations of fractal dimension ($r=0.9835$) and sample entropy ($r=0.9168$) between EEG and EMG signals in various tasks. Therefore, brain and muscle activations are strongly correlated in different tasks. Similar analyses can be conducted in the case of other organs to decode their correlations.

The study of correlations between different organs under various conditions is a prominent field in biomedical science and engineering. This paper explores the relationship between brain and muscle activities during rest and various limb movements including plantar flexion and knee flexion. We employed complexity measures, calculating the fractal dimension (FD) and sample entropy (SampEn) of electroencephalogram (EEG) and electromyogram (EMG) signals, which serve as indicators of brain and muscle activities, respectively. Our analysis focused on how the complexity variations in these signals correlate across different tasks. The results revealed opposite trends in the complexity of EEG and EMG signals. Specifically, the complexity of EEG signals increased from initial rest to final rest, plantar flexion, and knee flexion, suggesting heightened neural activity likely due to motor planning and execution. Conversely, the complexity of EMG signals decreased, indicating more synchronized and consistent muscle contractions during these movements, reflecting coordinated motor control and reduced variability in muscular activity. This analytical approach can be applied to study the correlations between different organs’ reactions and brain activity across various tasks.

Britton Chance has pioneered magnetic resonance spectroscopy (MRS) and near-infrared (NIR) spectroscopy (NIRS) as noninvasive methods for measuring muscle metabolism in vivo from the late 1970s. This review honoring Britton Chance will highlight the progress that has been made in developing and utilizing MRS and NIRS technologies for evaluating skeletal muscle O₂ dynamics and energetics. Adaptation of MRS and NIRS technology has focused on the validity and reliability of the measurements and extending the methods in physiological and clinical research. Britton Chance has conducted MRS and NIRS research on elite athletes and a number of chronic health conditions, including patients with chronic heart failure, peripheral vascular disease, and neuromuscular myopathies. As MRS and NIRS technologies are practical and useful for measuring human muscle metabolism, we will strive to continue Chance's legacy by advancing muscle MRS and NIRS studies.