Classificação multiclasse de sinais de eletroencefalograma para tarefas de imaginação motora utilizando processamento estatístico de sinais e deep learning

Abstract

Research Interests: Efficient classification of electroencephalogram (EEG) signals is crucial for the development of brain-computer interface systems. However, the complexity and variability of EEG signals pose significant challenges for accurate classification. Additionally, this study has social relevance as it can contribute to the development of assistive brain-computer interfaces, benefiting individuals with severe motor impairments, such as those who have experienced a stroke. These interfaces have the potential to improve the quality of life for these individuals by enabling communication and device control through brain activity. Objectives: This study aimed to compare the performance and computational cost of an artificial neural network using different signal processing techniques for the classification of resting state and left/right wrist movement imagination states from EEG signals. Three statistical signal processing techniques, Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Singular Spectrum Analysis (SSA), were explored in conjunction with a Convolutional Neural Network (CNN) to enhance the classification of EEG signals. Results Obtained: The results revealed that the PCA technique led to a reduction in training time of up to 63.5% without significantly compromising performance in terms of classification accuracy. PCA proved to be a promising approach, capturing relevant information from the EEG signals and improving the CNN’s ability to classify accurately. On the other hand, both ICA and SSA techniques did not yield promising results. ICA had negative effects on feature extraction, resulting in decreased classification accuracy by the CNN. SSA, on the other hand, showed consistently low performance across all evaluated metrics, indicating challenges in capturing discriminative information from the EEG-IM signals.