Neurotechnological Innovations: Unraveling the Secrets of Mental Task Classification through EEG Signals
Wei Zhang
Center for Cognitive Neuroscience and Brain-Computer Interfaces
Hui Liu
Department of Neuroengineering, Guizhou University, China
Keywords: electroencephalography, EEG, brain-computer interface, BCI, mental task classification, deep learning, neural networks
Abstract
Electroencephalography (EEG) is a non-invasive technique that measures brain activity through scalp electrodes. Due to its high temporal resolution and ease of use, EEG has become a popular tool for brain-computer interface (BCI) applications, which aim to translate brain signals into control commands for external devices. A key challenge in EEG-based BCI is accurate classification of mental tasks from EEG data. This review provides a comprehensive overview of recent innovations in EEG-based mental task classification, with a focus on deep learning techniques. We discuss various neural network architectures that have achieved state-of-the-art performance on mental task classification. These include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention-based models. We also review advanced EEG preprocessing techniques, transfer learning methods, and multi-modal integration approaches that further boost classification accuracy. In addition, we highlight techniques to improve model interpretability, including attention visualizations and layer-wise relevance propagation. Finally, we examine the advantages of deep learning for mental task classification in real-world and online BCI applications. Overall, deep learning has led to dramatic improvements in EEG decoding, allowing for more seamless BCI control. We conclude with an outlook on future challenges and opportunities at the intersection of neurotechnology and artificial intelligence.
Author Biography
Hui Liu, Department of Neuroengineering, Guizhou University, China
