Bipolar disorder is characterized by marked changes in mood and activity levels and is a leading cause of disability worldwide. We sought to investigate the application of deep learning methods to electroencephalogram (EEG) signals to predict clinical remission after 6 weeks of home-based transcranial direct current stimulation (tDCS) treatment. Pre-treatment resting-state EEG acquired from 21 bipolar participants was used for this work. A hybrid 1DCNN and GRU model, with input from power spectral density values of theta, beta and gamma frequency bands of the AF7 and TP10 electrodes, achieved a treatment remission prediction accuracy of 78.5% (sensitivity 81.4%, specificity 74.64%).