Brazilian Archives of Biology and Technology (Nov 2024)
Improved Scalp Swarm Optimization with Generative Adversarial Network for Recognition of Motor Imagery EEG
Abstract
Abstract Motor imaging (MI) has been commonly employed in the domains of nervous analysis and robot control as an essential model of impulsive brain-computer interfaces (BCIs). Several approaches for extraction and classification based on MI signals have presented by researchers. Because of its random initialization technique, deep-learning (DL) procedures like convolutional neural networks (CNNs) employed in motor imagery categorization would suffer from the problem of extracting the features and improving the classification performance. To overcome these shortcomings, the proposed work presented a technique for reconstructing MI signals using empirical mode decomposition (EMD). In which it can manage the non-stationary problem and mix their Intrinsic Mode Functions (IMFs) extended to multichannel analysis. The proposed works uses the transformation technique of discrete wavelet transform (DWT) for extracting the signal. In classification wise, the proposed work used conditional generative adversarial network (CGAN), which provides the better classification performance and reduces the computational time. The proposed work used both binary class and multi-class classification. Scalp swarm optimization (SSO) used to enhance and optimize the learning parameters of the GAN model because in multi-class subjects, the performance of CGAN gradually degrades. In this BCI experimentation, the proposed work used two BCI competition datasets, such as BCI competition three dataset III (a) and BCI competition three dataset IV (a). Furthermore, evaluate the proposed technique performance by evaluating and comparing with DL technique as CNN (Alexnet and Resnet) model. The CNN achieved a classification accuracy performance of 81.61% in multiclass, while the CGAN showed better performance at 89.11%.
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