Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX

07/15/2022
by   Xia Chen, et al.
0

The development of brain-computer interfaces (BCI) has facilitated our study of mental representations in the brain. Neural networks (NNs) have been widely used in BCI due to their decent pattern learning capabilities; however, to our best knowledge, a comprehensive comparison between various neural network models has not been well addressed, due to the interdisciplinary difficulty and case-based study in the domain. Here, we tested the capabilities of common NN architectures in deciphering mental representations from electroencephalogram (EEG) signals, which were recorded in representative classification tasks. In this study, we: 1. Construct 20 mechanism-wise different, typical NN types and their variants on decoding various EEG datasets to show a comprehensive performance comparison regarding their EEG information representation capability. 2. Lighten an efficient pathway based on the analysis results to gradually develop general improvements and propose a novel NN architecture: EEGNeX. 3. We open-sourced all models in an out-of-the-box status, to serve as the benchmark in the BCI community. The performance benchmark contributes as an essential milestone to filling the gap between domains understanding and support for further interdisciplinary studies like analogy investigations between the brain bioelectric signal generation process and NN architecture. All benchmark models and EEGNeX source code is available at:https://github.com/chenxiachan/EEGNeX.

READ FULL TEXT
research
01/21/2022

Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting

Electroencephalogram (EEG) is an important diagnostic test that physicia...
research
10/07/2020

Interpreting Imagined Speech Waves with Machine Learning techniques

This work explores the possibility of decoding Imagined Speech (IS) sign...
research
07/20/2023

Decoding the Enigma: Benchmarking Humans and AIs on the Many Facets of Working Memory

Working memory (WM), a fundamental cognitive process facilitating the te...
research
06/29/2023

DreamDiffusion: Generating High-Quality Images from Brain EEG Signals

This paper introduces DreamDiffusion, a novel method for generating high...
research
02/03/2020

Siamese Neural Networks for EEG-based Brain-computer Interfaces

Motivated by the inconceivable capability of the human brain in simultan...
research
03/17/2021

FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface

Lack of adequate training samples and noisy high-dimensional features ar...
research
11/17/2020

A Deep Neural Network for SSVEP-based Brain Computer Interfaces

The target identification in brain-computer interface (BCI) speller syst...

Please sign up or login with your details

Forgot password? Click here to reset