Exploring the frontier: Transformer-based models in EEG signal analysis for brain-computer interfaces.

This review systematically explores the application of transformer-based models in EEG signal processing and brain-computer interface (BCI) development, with a distinct focus on ensuring methodological rigour and adhering to empirical validations within the existing literature. By examining various transformer architectures, such as the Temporal Spatial Transformer Network (TSTN) and EEG Conformer, this review delineates their capabilities in mitigating challenges intrinsic to EEG data, such as noise and artifacts, and their subsequent implications on decoding and classification accuracies across disparate mental tasks. The analytical scope extends to a meticulous examination of attention mechanisms within transformer models, delineating their role in illuminating critical temporal and spatial EEG features and facilitating interpretability in model decision-making processes.

An EEG-based brain-computer interface for real-time multi-task robotic control.

The Brain Computer Interface (BCI) is the communication between the human brain and the computer. Electroencephalogram (EEG) is one of the biomedical signals which can be obtained by attaching electrodes to the scalp. Some EEG related applications can be developed to help disabled people, such as EEG based wheelchair or robotic arm.