WiFOG: Integrating deep learning and hybrid feature selection for accurate freezing of gait detection

Leveraging temporal dependency for cross-subject-MI BCIs by contrastive learning and self-attention

Brain-computer interfaces (BCIs) built based on motor imagery paradigm have found extensive utilization in motor rehabilitation and the control of assistive applications. However, traditional MI-BCI systems often exhibit suboptimal classification performance and require significant time for new users to collect subject-specific training data. This limitation diminishes the user-friendliness of BCIs and presents significant challenges in developing effective subject-independent models. In response to these challenges, we propose a novel subject-independent framework for learning temporal dependency for motor imagery BCIs by Contrastive Learning and Self-attention (CLS). In CLS model, we incorporate self-attention mechanism and supervised contrastive learning into a deep neural network to extract important information from electroencephalography (EEG) signals as features. We evaluate the CLS model using two large public datasets encompassing numerous subjects in a subject-independent experiment condition. The results demonstrate that CLS outperforms six baseline algorithms, achieving a mean classification accuracy improvement of 1.3 % and 4.71 % than the best algorithm on the Giga dataset and OpenBMI dataset, respectively. Our findings demonstrate that CLS can effectively learn invariant discriminative features from training data obtained from non-target subjects, thus showcasing its potential for building models for new users without the need for calibration.

Assessment of lumbar disc herniation-impaired gait by using IMU data fusion method

The inertial motion unit (IMU) is an effective tool for monitoring and assessing gait impairment in patients with lumbar disc herniation(LDH). However, the current clinical assessment methods for LDH gait focus on patients' subjective scoring indicators and lack the assessment of kinematic ability; at the same time, individual differences in the motor function degradation of the healthy and affected lower limbs of LDH patients are also ignored. To solve this problem, we propose an LDH gait feature model based on multi-source adaptive Kalman data fusion of acceleration and angular velocity. The gait phase is segmented by using an adaptive Kalman data fusion algorithm to estimate the attitude angle, and obtaining gait events through a zero-velocity update technique and a peak detection algorithm. Two IMUs were used to analyze the gait characteristics of lumbar disc patients and healthy gait people, including 12 gait characteristics such as gait spatiotemporal parameters, kinematic parameters, gait variability and stability. Statistical methods were used to analyze the characteristic model and verify the biological differences between the healthy affected side of LDH and healthy subjects. Finally, feature engineering and machine learning technology were used to identify the gait pattern of inertial movement units in patients with lumbar intervertebral disc disease, and achieved a classification accuracy of 95.50%, providing an effective gait feature set and method for clinical evaluation of LDH.