Detailed observation of human activity involves recording and analyzing different physiological and behavioral data. Electroencephalogram (EEG) is a measure of the patient’s brain activity and cognitive abilities. However, other signals such as electrocardiogram (ECG) or electromyogram (EMG) can provide complementary information about the patient’s state. Consequently, their combined analysis is particularly interesting. However, merging different types of data is not an easy task. New approaches and methods have emerged in recent years to tackle the fusion task, especially within deep neural networks. We carried out a literature review targeting the fusion of EEG and other data in the context of medical applications. The fusion of data can be done at multiple levels of the architecture. Input-level fusion suggests that multimodal signals are processed jointly from the first layer; at the decision-level, input signals are analyzed independently of each other in parallel networks and their results are combined as a final step of the architecture. However, both approaches do not guarantee that the relations between the signals are properly learned. This is why mid-level features fusion is proposed here: features from multimodal signals are preliminary extracted before being fused at an intermediary-level of the architecture using feature-level or score-level fusions. The literature shows that such techniques can greatly improve the performance of the medical system by enhancing learning and representation. Thus, Brain-Computer Interfaces can benefit significantly from the integration of additional physiological data at mid or final level.