The brain-computer interface (BCI) based on motor imagery electroencephalography (MI-EEG) enables direct information interaction between the human brain and external devices. In this paper, a multi-scale EEG feature extraction convolutional neural network model based on time series data enhancement is proposed for decoding MI-EEG signals. First, an EEG signals augmentation method was proposed that could increase the information content of training samples without changing the length of the time series, while retaining its original features completely. Then, multiple holistic and detailed features of the EEG data were adaptively extracted by multi-scale convolution module, and the features were fused and filtered by parallel residual module and channel attention. Finally, classification results were output by a fully connected network. The application experimental results on the BCI Competition IV 2a and 2b datasets showed that the proposed model achieved an average classification accuracy of 91.87% and 87.85% for the motor imagery task, respectively, which had high accuracy and strong robustness compared with existing baseline models. The proposed model does not require complex signals pre-processing operations and has the advantage of multi-scale feature extraction, which has high practical application value.
Objective To evaluate the positional relationship between protective channel and sural nerve while treating acute Achilles tendon rupture with channel assisted minimally invasive repair (CAMIR) technique based on anatomical observations of cadaver specimens. Methods Twelve adult cadaveric lower limb specimens (6 left, 6 right) were utilized. A CAMIR device was implanted at a distance of 4 cm from the proximal end of the specimen to the Achilles tendon insertion. The skin was incised along the tendon’s medial side, the sural nerve was dissected, and the positional relationship with the protective channel was observed. The distance from the sural nerve-Achilles tendon intersection to the calcaneal insertion, the vertical distance between protective channel and the calcaneal insertion, and the horizontal distance between the sural nerve and protective channel were measured by using vernier caliper. Results Anatomical examination demonstrated a variable positional relationship between the sural nerve and protective channel, with the sural nerve positioned above (8 specimens) or below (4 specimens) the protective channel. The distance from the sural nerve-Achilles tendon intersection to the calcaneal insertion was (105.67±14.94) mm, the vertical distance between protective channel and the calcaneal insertion was (93.20±9.57) mm, and the horizontal distance between the sural nerve and protective channel was (0.31±0.14) mm. Conclusion The use of CAMIR technique for the treatment of acute Achilles tendon rupture can effectively avoid iatrogenic injury to the sural nerve.