BCI MI signal Classification Framework using Fuzzy integrals.

Paper: Ko, L. W., Lu, Y. C., Bustince, H., Chang, Y. C., Chang, Y., Ferandez, J., ... & Lin, C. T. (2019). Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface. IEEE Computational Intelligence Magazine, 14(1), 96-106.

**TridentNet** is an object detection architecture that aims to generate scale-specific feature
maps with a uniform representational power.  A parallel multi-branch architecture is constructed in which each branch shares the same transformation parameters but with different receptive fields. A scale-aware training scheme is used to specialize each branch by sampling object instances of proper scales for training.

TridentNet

Scale-Aware Trident Networks for Object Detection

Choquet integral in decision analysis - lessons from the axiomatization

A **Convolutional Gated Recurrent Unit** is a type of [GRU](https://paperswithcode.com/method/gru) that combines GRUs with the [convolution](https://paperswithcode.com/method/convolution) operation. The update rule for input $x\_{t}$ and the previous output $h\_{t-1}$ is given by the following:

$$ r = \sigma\left(W\_{r} \star\_{n}\left[h\_{t-1};x\_{t}\right] + b\_{r}\right) $$

$$ u = \sigma\left(W\_{u} \star\_{n}\left[h\_{t-1};x\_{t}\right] + b\_{u} \right) $$

$$ c = \rho\left(W\_{c} \star\_{n}\left[x\_{t}; r \odot h\_{t-1}\right] + b\_{c} \right) $$

$$ h\_{t} = u \odot h\_{t-1} + \left(1-u\right) \odot c $$

In these equations $\sigma$ and $\rho$ are the elementwise sigmoid and [ReLU](https://paperswithcode.com/method/relu) functions respectively and the $\star\_{n}$ represents a convolution with a kernel of size $n \times n$. Brackets are used to represent a feature concatenation.

Source	Choquet integral in decision analysis - lessons from the axiomatization
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com