# DGAFF: Deep Genetic Algorithm Fitness Formation for EEG Bio-Signal Channel Selection

arXiv: https://arxiv.org/abs/2202.10034 · HTML: https://mlsanity.com/paper/2202.10034/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces](https://mlsanity.com/paper/2006.00622/) (2020)
- [A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers](https://mlsanity.com/paper/1905.04149/) (2019)
- [Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features](https://mlsanity.com/paper/1806.06823/) (2018)

## Built on by (most cited first, 1 of 1)

- [Cancer-inspired Genomics Mapper Model for the Generation of Synthetic DNA Sequences with Desired Genomics Signatures](https://mlsanity.com/paper/2305.01475/) (2023)

## Abstract (arXiv)

Brain-computer interface systems aim to facilitate human-computer interactions in a great deal by direct translation of brain signals for computers. Recently, using many electrodes has caused better performance in these systems. However, increasing the number of recorded electrodes leads to additional time, hardware, and computational costs besides undesired complications of the recording process. Channel selection has been utilized to decrease data dimension and eliminate irrelevant channels while reducing the noise effects. Furthermore, the technique lowers the time and computational costs in real-time applications. We present a channel selection method, which combines a sequential search method with a genetic algorithm called Deep GA Fitness Formation (DGAFF). The proposed method accelerates the convergence of the genetic algorithm and increases the system's performance. The system evaluation is based on a lightweight deep neural network that automates the whole model training process. The proposed method outperforms other channel selection methods in classifying motor imagery on the utilized dataset.