Fig 1.
Different levels of abstractions to study BCIs.
In green is highlighted the main contribution of our paper. We, however, will cover briefly abstraction levels 1 to 4 in Section 2 of this paper. We introduce the color code, so that the reader can follow the structure of the paper and deside for himself/herself if he/she would like to skip a subsection (a BCI specialist could go though the Section 2 really fast or skip it completely as he/she already has the necessary knowledge).
Fig 2.
Common BCI loop.
Table 1.
Summary of the state of the art on acquisition techniques for BCIs.
Table 2.
Comparative summary of two approaches used in BCIs nowadays.
Table 3.
Summary of advantages and drawbacks of different neural mechanisms.
Each of three main neural mechanisms is present with one of the examples. Comparison of different neural mechanisms based on the classification accuracies and training time.
Fig 3.
A matrix which contains letters of the alphabet and other symbols, which are flashed in random order (white line on the image) to elicit the P300 evoked response based on [35].
Fig 4.
Our conceptual space of EEG-based BCI applications.
Fig 5.
“Alpha WoW” application where a user avatar can transform itself in a bear depending on the users stress level [56].
Table 4.
Classification of EEG-based BCI applications within our conceptual space from 8 different applicative domains identified in section “Abstraction Level 4” of this paper.
Table 5.
Classification of EEG-based BCI applications for smart homes within our conceptual space.
Table 6.
Classification of studies that use games (both physical and virtual) using EEG-based BCI applications within our conceptual space (part 1).
Table 7.
Classification of studies that use games (both physical and virtual) using EEG-based BCI applications within our conceptual space (part 2).
Table 8.
11 CHI papers from 2010–2016 within our conceptual space.