Brain-computer interface (BCI) technology enables direct communication between the brain and external devices, allowing individuals to control their environment using brain signals. However, existing BCI approaches face three critical challenges that hinder their practicality and effectiveness: a) time-consuming preprocessing algorithms, b) inappropriate loss function utilization, and c) less intuitive hyperparameter settings. To address these limitations, we present NeuroKinect, an innovative deep-learning model for accurate reconstruction of hand kinematics using electroencephalography (EEG) signals. NeuroKinect model is trained on the Grasp and Lift (GAL) tasks data with minimal preprocessing pipelines, subsequently improving the computational efficiency. A notable improvement introduced by NeuroKinect is the utilization of a novel loss function, denoted as LStat. This loss function addresses the discrepancy between correlation and mean square error in hand kinematics prediction. Furthermore, our study emphasizes the scientific intuition behind parameter selection to enhance accuracy. We analyze the spatial and temporal dynamics of the motor movement task by employing event-related potential and brain source localization (BSL) results. This approach provides valuable insights into the optimal parameter selection, improving the overall performance and accuracy of the NeuroKinect model. Our model demonstrates strong correlations between predicted and actual hand movements, with mean Pearson correlation coefficients of 0.92 (±0.015), 0.93 (±0.019), and 0.83 (±0.018) for the X, Y, and Z dimensions. The precision of NeuroKinect is evidenced by low mean squared errors (MSE) of 0.016 (±0.001), 0.015 (±0.002), and 0.017 (±0.005) for the X, Y, and Z dimensions, respectively. Overall, the results demonstrate unprecedented accuracy and real-time translation capability, making NeuroKinect a significant advancement in the field of BCI for predicting hand kinematics from brain signals.

The proposed EMG data acquisition band, which utilizes seven electrodes, has the potential to revolutionize gesture recognition. Unlike conventional EMG data acquisition methods that necessitate skin preparation and can cause skin irritation or adverse reactions, this band is inexpensive, user-friendly, and capable of capturing high-quality EMG signals. Our study evaluated the performance of the proposed EMG band by comparing it with established techniques on eight healthy subjects, and the results revealed that it achieved comparable or significantly better performance in terms of classification accuracy. This suggests that our EMG data acquisition band has significant practical applications, particularly in prosthetic hand control, where real-time processing is critical. Moreover, The proposed feature extraction method based on spectral moments also exhibited significant improvements in terms of classification accuracy, class separability, and processing time, making it a promising alternative for gesture recognition applications. Additionally, the proposed feature set was computationally efficient, with a maximum computation time of 250.209 milliseconds, making it suitable for real-time prosthetic hand control.