BCI MI framework to classifiy brain signals using a multimodal decission making phase, with an addtional differentiation of the signal.

**BARThez** is a self-supervised transfer learning model for the French language based on [BART](https://paperswithcode.com/method/bart). Compared to existing [BERT](https://paperswithcode.com/method/bert)-based French language models such as [CamemBERT](https://paperswithcode.com/paper/camembert-a-tasty-french-language-model) and [FlauBERT](https://paperswithcode.com/paper/flaubert-unsupervised-language-model-pre), BARThez is well-suited for generative tasks, since not only its encoder but also its decoder is pretrained.

mBARTHez

BARThez: a Skilled Pretrained French Sequence-to-Sequence Model

Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions

**Seesaw Loss** is a loss function for long-tailed instance segmentation. It dynamically re-balances the gradients of positive and negative samples on a tail class with two complementary factors: mitigation factor and compensation factor. The mitigation factor reduces punishments to tail categories w.r.t the ratio of cumulative training instances between different categories. Meanwhile, the compensation factor increases the penalty of misclassified instances to avoid false positives of tail categories. The synergy of the two factors enables Seesaw Loss to mitigate the overwhelming punishments on tail classes as well as compensate for the risk of misclassification caused by diminished penalties.

$$ L\_{seesaw}\left(\mathbf{x}\right) = - \sum^{C}\_{i=1}y\_{i}\log\left(\hat{\sigma}\_{i}\right) $$

$$ \text{with } \hat{\sigma\_{i}} = \frac{e^{z\_{i}}}{- \sum^{C}\_{j\neq{1}}\mathcal{S}\_{ij}e^{z\_{j}}+e^{z\_{i}} } $$

Here $\mathcal{S}\_{ij}$ works as a tunable balancing factor between different classes. By a careful design of $\mathcal{S}\_{ij}$, Seesaw loss adjusts the punishments on class j from positive samples of class $i$. Seesaw loss determines $\mathcal{S}\_{ij}$ by a mitigation factor and a compensation factor, as:

$$ \mathcal{S}\_{ij} =\mathcal{M}\_{ij} · \mathcal{C}\_{ij}  $$

The mitigation factor $\mathcal{M}\_{ij}$ decreases the penalty on tail class $j$ according to a ratio of instance numbers between tail class $j$ and head class $i$. The compensation factor $\mathcal{C}\_{ij}$ increases the penalty on class $j$ whenever an instance of class $i$ is misclassified to class $j$.

Source	Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com

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What is: Enhanced-Multimodal Fuzzy Framework?

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