• Corpus ID: 246706217

Efficacy of Transformer Networks for Classification of Raw EEG Data

@article{Siddhad2022EfficacyOT,
  title={Efficacy of Transformer Networks for Classification of Raw EEG Data},
  author={Gourav Siddhad and Anmol Gupta and Debi Prosad Dogra and Partha Pratim Roy},
  journal={ArXiv},
  year={2022},
  volume={abs/2202.05170}
}
With the unprecedented success of transformer networks in natural language processing (NLP), recently, they have been successfully adapted to areas like computer vision, generative adversarial networks (GAN), and reinforcement learning. Classifying electroencephalogram (EEG) data has been challenging and researchers have been overly dependent on pre-processing and hand-crafted feature extraction. Despite having achieved automated feature extraction in several other domains, deep learning has… 

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References

SHOWING 1-10 OF 44 REFERENCES

Complex networks and deep learning for EEG signal analysis.

The results demonstrate that complex networks and deep learning can effectively implement functional complementarity for better feature extraction and classification, especially in EEG signal analysis, and develop a framework combining recurrence plots and convolutional neural network to achieve fatigue driving recognition.

DeprNet: A Deep Convolution Neural Network Framework for Detecting Depression Using EEG

A DL-based convolutional neural network (CNN) called DeprNet is proposed for classifying the EEG data of depressed and normal subjects and the results suggest that CNN trained on recordwise split data gets overtrained on EEG data with a small number of subjects.

Classification of EEG Signals for Cognitive Load Estimation Using Deep Learning Architectures

Two deep learning models are studied, namely stacked denoising autoencoder followed by a multilayer perceptron (MLP) and long short term memory (LSTM) followed by an MLP to classify cognitive load data.

EEG-Based Age and Gender Prediction Using Deep BLSTM-LSTM Network Model

With the rapid development of brain–computer interfaces (BCI), the number of applications that use BCI technology is increasingly thick and fast. Prediction of age and gender of a person through EEG

WLnet: Towards an Approach for Robust Workload Estimation Based on Shallow Neural Networks

Four methods were used to extract workload EEG features and the new proposed shallow convolutional neural network for workload estimation (WLnet) was compared, demonstrating that the proposed WLnet achieved the best detection accuracy in both stress and non-stress conditions.

Convolution- and Attention-Based Neural Network for Automated Sleep Stage Classification

A neural network based on a convolutional network and attention mechanism to perform automatic sleep staging and the attention mechanism excels in learning inter- and intra-epoch features is proposed.

EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces

This work introduces EEGNet, a compact convolutional neural network for EEG-based BCIs, and introduces the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI.

Toward the Development of Versatile Brain–Computer Interfaces

A novel automated framework is proposed that reveals the importance of multidomain features with feature selection to increase the performance of a learning algorithm for motor imagery electroencephalogram task classification on the utility of signal decomposition methods.

Age and gender classification using brain–computer interface

This paper presents an automatic age and gender prediction framework of users based on their neurosignals captured during eyes closed resting state using EEG sensor and it has been analyzed that oscillations in beta and theta band waves show maximum age prediction, whereas delta rhythm leads to highest gender classification rates.

An Attention-based Bi-LSTM Method for Visual Object Classification via EEG