The **Enhanced Fusion Framework** proposes three different ideas to improve the existing MI-based BCI frameworks.

Image source: [Fumanal-Idocin et al.](https://arxiv.org/pdf/2101.06968v1.pdf)

**ProxylessNet-GPU** is a convolutional neural network architecture learnt with the [ProxylessNAS](https://paperswithcode.com/method/proxylessnas) [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) algorithm that is optimized for GPU devices. It uses inverted residual blocks (MBConvs) from [MobileNetV2](https://paperswithcode.com/method/mobilenetv2) as its basic building block.

ProxylessNet-GPU

ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

Enhanced Fusion Framework

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

**PAFNet** is an anchor-free detector for object detection that removes pre-defined anchors and regresses the locations directly, which can achieve higher efficiency. The overall network is composed of a backbone, an up-sampling module, an AGS module, a localization branch and a regression branch. Specifically,  ResNet50-vd is chosen as the backbone for server side, and [MobileNetV3](https://paperswithcode.com/method/mobilenetv3) for mobile side. Besides, for mobile side, we replace traditional [convolution](https://paperswithcode.com/method/convolution) layers with lite convolution operators.

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