A Transferable Deep Learning Prognosis Model for Predicting Stroke Patients' Recovery in Different Rehabilitation Trainings

Explainable Deep-Learning Prediction for Brain–Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion

“…Our deep learning multi-state fusion model considers the neural activation ability of patients in multiple task states, leading to more accurate predictions of future recovery. Similar > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < advantages have been observed in other studies [29,40,41]. For example, one study demonstrated the benefits of integrating pathology, radiology, and molecular data to predict cancer [40].…”

“…Another study found that a multi-graph fusion model could improve prediction accuracy [41]. Our prior research also revealed that combining biomechanical information and multiple EEG features can enhance the accuracy of motor function prediction [29]. In summary, our multi-state fusion model holds the potential to predict lower extremity gains in post-stroke BCI training.…”

“…These channels were used to compute the power spectrum density and the functional brain network. To ensure consistency in our prognostic model and displayed results, EEG data collected from patients with lesions in the Left hemisphere (singular number) were mirrored to the corresponding channels in the Right hemisphere (even number) [29].…”

“…Then the points from 1 to 90 have been selected which cover the EEG signal bands from the 0.5Hz to 45Hz. Therefore, the matrix size for EEG power spectrum density data is (29,90). The columns represent the 29 channels.…”

“…The result is a two-dimensional matrix of the size (406, 6). EEG functional connectivity was calculated using a previously described method [29]. The columns represent the 406 twoelectrode combinations.…”