Learning from rewards and punishments is essential to survival and facilitates flexible human behavior. It is widely appreciated that multiple cognitive and reinforcement learning systems contribute to decision-making, but the nature of their interactions is elusive. Here, we leverage methods for extracting trial-by-trial indices of reinforcement learning (RL) and working memory (WM) in human e...

The analysis of epilepsy electro-encephalography (EEG) signals is great significance for the diagnosis epilepsy, which one common neurological diseases all age groups. With developments machine learning, many data-driven models have achieved performance in EEG classification. However, it difficult to select appropriate hyperparameters file a specific task. In this paper, an evolutionary algorit...

People with physical disabilities face a lot of problems in communication with their fellow human beings. In this paper, the design of an eye-controlled system with an emphasis on the Human-Computer-Interface (HCI) based on Electroencephalography (EEG) has been presented. The basis for this design is the electric potentials generated by the human eye. After this potential is sensed by electrode...

Functional magnetic resonance imaging and electro-/magneto-encephalography are some of the main neuroimaging technologies used by cognitive neuroscientists to study how the brain works. However, the methods for analysing the rich spatial and temporal data they provide are constantly evolving, and these new methods in turn allow new scientific questions to be asked about the brain. In this brief...