Deep Belief Networks Classification Optimization of Motor Imagination Brain Signals

‍ ‍The extraction of motor imagery （MI） features in brain computer interface （BCI） systems has always been a challenge. Compared to other BCI modes such as SSVEP， AEP， and P300， the classification accuracy of MI is relatively low and there is a lack of effective recognition schemes. This article proposes a feature extraction and classification recognition algorithm SSA-DBN that combines Deep Belief Network （DBN） and Sparrow search algorithm （SSA）. Integrating the advantages of SSA and DBN can improve the accuracy of feature extraction and classification recognition while maintaining low computational complexity. This study first utilizes the fully adaptive noise set empirical mode decomposition （CEEMDAN） method to extract the intrinsic mode function （IMF） features of signals. Then， the selected IMF components suitable for classification and recognition are combined with the Hilbert Huang Transform （HHT） method to extract the feature space vectors of different lead time-frequency signals and perform superposition averaging. Finally， input the feature vectors into the SSA-DBN algorithm for classification processing. To ensure fairness， a representative BCI Competition IV dataset 2a was selected during the validation of algorithm performance. The performance of other algorithms was compared， and the parameter tuning method was explained in detail. In order to avoid the overfitting problem， you can consider using a larger data set for testing， such as PhysioNet or BCI2000 and other public data set libraries. When presenting the experimental results， in addition to demonstrating the performance of the SSA-DBN algorithm， the performance of other algorithms under the same dataset and evaluation indicators was also demonstrated for fair comparison. The results show that the SSA-DBN algorithm has the highest average accuracy of 86.35% in four classification tasks， which is significantly improved compared to other methods. It provides a new and effective way to improve the system performance of MI signal brain computer interface.