With the continuous development of the brain-computer interface (BCI) technology, the lower limb rehabilitation system based on Motor Imagery (MI) has gradually become a research hotspot in the field of rehabilitation. To recognize the lower limbs MI, this paper designed an experimental paradigm for MI lower limb MI and used the 1D-CNN-LSTM deep learning algorithm to classify lower limb movement features from MI EEG signals. Compared with classical machine learning algorithms, the results showed that 1D-CNN-LSTM has relatively higher accuracy. Meanwhile, the paper built a real-time lower limb rehabilitation system based on the 1D-CNN-LSTM algorithm, which verifies the effectiveness and feasibility of the algorithm. The system provides an advanced and effective solution for brain-computer interfaces based on MI.
Attention is closely related to human life. To detect attention states quickly and accurately with fewer resources, this research proposes a method for attention state detection, it is based on differential entropy (DE) and power spectral density (PSD). Electroencephalogram (EEG) data is from 15 participants. It was processed using the Fast Fourier Transform (FFT) to extract DE and PSD features, which was normalized. These features were input into a Support Vector Machine (SVM). After optimizing the model parameters, it achieved a well-performing attention state detection model. The proposed method achieved a maximum classification accuracy of 85% and an average accuracy of 67%, The model described in the statement surpasses traditional SVM models that are trained solely on DE or PSD features, as well as single-channel or multi-channel SVM models. The new method can be used to learn additional features for attention verification and generalizes well for the task of developing a robust deep learning system.
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