The field of brain-computer interfaces (BCIs) and neuroengineering is rapidly advancing, with a focus on improving the accuracy and responsiveness of BCIs for various applications, including prosthetic control, motor rehabilitation, and assistive robotics. Recent developments have highlighted the importance of incorporating neurophysiological insights into force control and decoding dynamic grasp force. Additionally, there is a growing trend towards calibration-free BCIs, continual online adaptation, and personalized music-based interventions for motor rehabilitation. These advancements have the potential to significantly improve the effectiveness of BCIs and enhance the quality of life for individuals with motor disorders. Noteworthy papers include: Improving Continuous Grasp Force Decoding from EEG with Time-Frequency Regressors and Premotor-Parietal Network Integration, which proposes an EEG-based methodology for decoding continuous grasp force, and EDAPT, a task- and model-agnostic framework for calibration-free BCIs with continual online adaptation. CognitiveArm is also notable for its real-time EEG-controlled prosthetic arm using embodied machine learning.