Electroencephalogram Channel Selection using Deep Q-Network

In brain-computer interfaces, electroencephalogram channel selection picks the most informative channels. To speed up the model training and improve accuracy by selecting a small number of optimal channels. In this study, we trained an agent that automatically learned the policy to choose an optimal channel, from given EEG data, even without hand engineering. We frame the problem of EEG channel selection as a Markov decision process (MDP), offer a productive method for parameterizing it, and then apply deep reinforcement learning (DRL) to solve it. After the agent has been trained, it tries to learn a policy for channel selection that directs it to choose channels sequentially while leveraging EEG signals and previously selected tracks. The study also offers two reward systems for the DRL environment simulation and analyzes them in trials. This is the first work to look at a DRL model for EEG data interpretation, opening up a new field of study and highlighting DRL’s immense potential in the brain-computer interface.