Motor Imagery EEG Signal Classification Using Distinctive Feature Fusion with Adaptive Structural LASSO.

A motor imagery brain-computer interface connects the human brain and computers via electroencephalography (EEG). However, individual differences in the frequency ranges of brain activity during motor imagery tasks pose a challenge, limiting the manual feature extraction for motor imagery classification. To extract features that match specific subjects, we proposed a novel motor imagery classification model using distinctive feature fusion with adaptive structural LASSO. Specifically, we extracted spatial domain features from overlapping and multi-scale sub-bands of EEG signals and mined discriminative features by fusing the task relevance of features with spatial information into the adaptive LASSO-based feature selection. We evaluated the proposed model on public motor imagery EEG datasets, demonstrating that the model has excellent performance. Meanwhile, ablation studies and feature selection visualization of the proposed model further verified the great potential of EEG analysis.

Development and validation of a model for the prediction of disease-specific survival in patients with oral squamous cell carcinoma: based on random survival forest analysis.

OBJECTIVE: To establish a model for predicting the disease-specific survival (DSS) of patients with oral squamous cell carcinoma (OSCC). METHODS: Patients diagnosed with OSCC from the Surveillance, Epidemiology, and End Results (SEER) database were enrolled and randomly divided into development (n = 14,495) and internal validation cohort (n = 9625). Additionally, a cohort from a hospital located in Southeastern China was utilized for external validation (n = 582). RESULTS: TNM stage, adjuvant treatment, surgery, tumor sites, age, grade, and gender were used for RSF model construction based on the development cohort. The effectiveness of the model was confirmed through time-dependent ROC curves in different cohorts. The risk score exhibited an almost exponential increase in the hazard ratio of death due to OSCC. In development, internal, and external validation cohorts, the prognosis was significantly worse for patients in groups with higher risk scores (all log-rank P < 0.05). CONCLUSION: Based on RSF, a high-performance prediction model for OSCC prognosis was created and verified in this study.

Prognostic value of pretreatment lymphocyte percentage in oral cancer: A prospective cohort study.

OBJECTIVE: To assess the prognostic role of pretreatment lymphocyte percentage (LY%) for patients with oral squamous cell carcinoma (OSCC). METHODS: A large-scale prospective cohort study between July 2002 and March 2021 was conducted. Propensity score-matched (PSM) analysis and inverse probability of treatment weighting (IPTW) analysis were performed to adjust for potential confounders. Using random survival forest (RSF), the relative importance of pretreatment LY% in prognosis prediction was also assessed. RESULTS: A total of 743 patients were enrolled and followed up (median: 2.75 years, interquartile range: 1.25-4.42 years). A high pretreatment LY% was significantly associated with better disease-specific survival of patients with OSCC (Hazard ratio [HR] = 0.60, 95% confidence interval [CI]: 0.42, 0.84). The same tendency was observed in PSM (HR = 0.57, 95% CI: 0.38, 0.85) and IPTW analysis (HR = 0.57, 95% CI: 0.40, 0.82). RSF showed that LY% ranked the fifth among importance ranking of all prognostic factors. CONCLUSION: Pretreatment LY% showed a moderate predictive ability, suggesting it might be a valuable tool to predict prognosis for patients with OSCC.

Analytical design method of a device for ultrasonic elliptical vibration cutting.

Ultrasonic elliptical vibration cutting (UEVC) is effective in ultraprecision diamond cutting of hard-brittle materials and ferrous metals. However, its design is quite empirical and tedious. This paper proposes an analytical design method for developing the UEVC device which works at the Flexural-Flexural complex-mode to generate the elliptical vibration. For such UEVC device, the resonant frequencies of the two flexural vibrations are required to be the same. In addition, the nodal points of the two flexural vibrations should be coincident so that the device can be clamped without affecting the vibrations. Based on the proposed analytical design method, an UEVC device was first designed. Modal analysis of the designed UEVC device was performed by using the finite element method, which shows that the resonant frequencies coincide well with the targeted ones. Then a prototype UEVC device was fabricated, and its vibration characteristics were measured by an impedance analyzer and a laser displacement sensor. Experimental results indicate that the designed UEVC device can generate elliptical vibration with the resonant frequencies closed to the target ones. In addition, the vibration trajectory can be precisely tuned by adjusting the phase difference and the amplitude of the applied voltage. Simulation and experimental results validated the effectiveness of the analytical design method.