Emotion-Inspired Deep Structure (EiDS) for EEG Time Series Forecasting

The machine learning (ML) community is interested in developing high-generalization ML algorithms by taking inspiration from cognitive systems. Such ML algorithms can be referred to, variously, as "neuroscience-inspired artificial intelligence" [1], a biologically inspired ML algorithm, a computational intelligence paradigm, or a braininspired ML algorithm (i.e., the terminology of this paper). The first step to developing a brain-inspired ML algorithm is to select a cognitive system that has three following criteria (the interested readers may refer to [2]). The first criterion is that the underlying structure of the cognitive system should encompass several components. The second point is that the cognitive system should fulfill a goal-based (e.g., cognitive) or state-based (e.g., emotional) function and through interaction between its components.