Double Deep Q-Network Decoder Based on EEG Brain-Computer Interface

Brain-computer interfaces(BCI)use neural activity as a control signal to enable direct communication between the human brain and external devices.The electrical signals generated by the brain are captured through electroencephalogram(EEG)and translated into neu-ral intentions reflecting the user's behavior.Correct decoding of the neural intentions then facilitates the control of external devices.Rein-forcement learning-based BCIs enhance decoders to complete tasks based only on feedback signals(rewards)from the environment,building a general framework for dynamic mapping from neural intentions to actions that adapt to changing environments.However,using traditional reinforcement learning methods can have challenges such as the curse of dimensionality and poor generalization.Therefore,in this paper,we use deep reinforcement learning to construct decoders for the correct decoding of EEG signals,demonstrate its feasibility through experi-ments,and demonstrate its stronger generalization on motion imaging(MI)EEG data signals with high dynamic characteristics.