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

• URL: http://arxiv.org/abs/2202.10034v1
• Date: Mon, 21 Feb 2022 08:06:17 GMT
• Title: DGAFF: Deep Genetic Algorithm Fitness Formation for EEG Bio-Signal Channel Selection
• Authors: Ghazaleh Ghorbanzadeh, Zahra Nabizadeh, Nader Karimi, Pejman Khadivi, Ali Emami, Shadrokh Samavi
• Abstract summary: Channel selection has been utilized to decrease data dimension and eliminate irrelevant channels. We present a channel selection method, which combines a sequential search method with a genetic algorithm called Deep GA Fitness Formation. The proposed method outperforms other channel selection methods in classifying motor imagery on the utilized dataset.
• Score: 12.497603617622907
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning [97.99077847606624]
  This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs. A key challenge in the design of a wake-up radio-based neuromorphic split computing system is the selection of thresholds for sensing, wake-up signal detection, and decision making.
  arXiv  Detail & Related papers  (2024-04-02T10:19:04Z)
• Emulation Learning for Neuromimetic Systems [0.0]
  Building on our recent research on neural quantization systems, results on learning quantized motions and resilience to channel dropouts are reported. We propose a general Deep Q Network (DQN) algorithm that can not only learn the trajectory but also exhibit the advantages of resilience to channel dropout.
  arXiv  Detail & Related papers  (2023-05-04T22:47:39Z)
• A Convolutional Spiking Network for Gesture Recognition in Brain-Computer Interfaces [0.8122270502556371]
  We propose a simple yet efficient machine learning-based approach for the exemplary problem of hand gesture classification based on brain signals. We demonstrate that this approach generalizes to different subjects with both EEG and ECoG data and achieves superior accuracy in the range of 92.74-97.07%.
  arXiv  Detail & Related papers  (2023-04-21T16:23:40Z)
• Channelformer: Attention based Neural Solution for Wireless Channel Estimation and Effective Online Training [1.0499453838486013]
  We propose an encoder-decoder neural architecture (called Channelformer) to achieve improved channel estimation. We employ multi-head attention in the encoder and a residual convolutional neural architecture as the decoder. We also propose an effective online training method based on the fifth generation (5G) new radio (NR) configuration for the modern communication systems.
  arXiv  Detail & Related papers  (2023-02-08T23:18:23Z)
• Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEG [56.155331323304]
  Deep learning based electroencephalogram channels' feature level fusion is carried out in this work. Channel selection, fusion, and classification procedures were optimized by two optimization algorithms.
  arXiv  Detail & Related papers  (2021-12-18T14:17:49Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• End-to-end learnable EEG channel selection with deep neural networks [72.21556656008156]
  We propose a framework to embed the EEG channel selection in the neural network itself. We deal with the discrete nature of this new optimization problem by employing continuous relaxations of the discrete channel selection parameters. This generic approach is evaluated on two different EEG tasks.
  arXiv  Detail & Related papers  (2021-02-11T13:44:07Z)
• Revisiting the Application of Feature Selection Methods to Speech Imagery BCI Datasets [1.7403133838762446]
  We show how simple yet powerful feature selection/ranking methods can be applied to speech imagery datasets. Our primary goal is to improve the resulting classification accuracies from support vector machines, $k$-nearest neighbour, decision tree, linear discriminant analysis and long short-term memory recurrent neural network classifiers.
  arXiv  Detail & Related papers  (2020-08-17T22:48:52Z)
• Data-Driven Symbol Detection via Model-Based Machine Learning [117.58188185409904]
  We review a data-driven framework to symbol detection design which combines machine learning (ML) and model-based algorithms. In this hybrid approach, well-known channel-model-based algorithms are augmented with ML-based algorithms to remove their channel-model-dependence. Our results demonstrate that these techniques can yield near-optimal performance of model-based algorithms without knowing the exact channel input-output statistical relationship.
  arXiv  Detail & Related papers  (2020-02-14T06:58:27Z)
• Channel Assignment in Uplink Wireless Communication using Machine Learning Approach [54.012791474906514]
  This letter investigates a channel assignment problem in uplink wireless communication systems. Our goal is to maximize the sum rate of all users subject to integer channel assignment constraints. Due to high computational complexity, machine learning approaches are employed to obtain computational efficient solutions.
  arXiv  Detail & Related papers  (2020-01-12T15:54:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.