Validation loss and validation accuracy stay the same in NN model
You say it's a regression task, predicting valence and arousal values, although you use accuracy as a performance metric. This does not make much sense, so your accuracy graph doesn't really say much. MSE is a valid performance metric for regression tasks in general, so your loss graph is more descriptive of what is going on. The loss-graph most definitely displays the characteristic of over-fitting, so I would recommend you to add regularization to your model.
This can be done by for example incorporating dropout and L1/L2-regularisation.