# A Survey on EEG Data Analysis Software

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Term Definitions

#### 2.1. Analysis of Variance (ANOVA)

#### 2.2. Analysis of Covariance (ANCOVA)

#### 2.3. Brain-Computer Interface (BCI)

#### 2.4. Common Spatial Pattern (CSP)

#### 2.5. Convolutional Neural Networks (CNN)

#### 2.6. Downsampling

#### 2.7. Fast Fourier Transform (FFT)

#### 2.8. Inverse Fast Fourier Transform (IFFT)

#### 2.9. Independent Component Analysis (ICA)

#### 2.10. Impulse Response

#### 2.11. Linear Regression or Regression

#### 2.12. Machine Learning

#### 2.13. Neuroinformatic

#### 2.14. Neural Networks

#### 2.15. Neurophysiology

#### 2.16. Passband and Bandstop

#### 2.17. Principal Component

#### 2.18. Principal Component Analysis (PCA)

#### 2.19. Recurrent Neural Network (RNN)

#### 2.20. Signal Space Projection

#### 2.21. Spectral Analysis

#### 2.22. Support Vector Machines (SVMs)

#### 2.23. Time-Frequency Analysis

#### 2.24. Wavelet

#### 2.25. Wavelet Transform

#### 2.26. Window (or Window Signal)

## 3. EEG Signal Processing Methods

#### 3.1. Bessel Filter

#### 3.2. Band Pass Filter

#### 3.3. Butterworth Filter

#### 3.4. Chebyshev Filter

#### 3.5. Finite Impulse Response (FIR) Filter

#### 3.6. High/Low Pass Filter

#### 3.7. Infinite Impulse Response (IIR) Filter

#### 3.8. Least Square Filter

#### 3.9. K-Nearest Neighbors

#### 3.10. Naive Bayes

#### 3.11. Notch Filter

#### 3.12. Non-Local Means (NLM) Filter

#### 3.13. Partial Least Squares

#### 3.14. Random Forest Classifier

#### 3.15. Regularized Discriminant Analysis

## 4. Artifact Detection and Removal

#### 4.1. Canonical Correlation Analysis

#### 4.2. Common Spatial Pattern (CSP)

#### 4.3. Empirical Mode Decomposition (EMD)

#### 4.4. Fast Fourier Transform (FFT)

#### 4.5. Independent Component Analysis (ICA)

#### 4.6. Non-linear Mode Decomposition (NMD)

#### 4.7. Principal Component Analysis (PCA)

#### 4.8. Source Imaging-Based Methods

#### 4.9. Wavelet Transform

## 5. Available Tools for EEG Signal Processing

#### 5.1. AcqKnowledge

#### 5.2. BESA

#### 5.3. BIOPAC Student Lab (BSL)

#### 5.4. BioSig

#### 5.5. BrainFlow

**Figure 4.**Control system of BrainFlow [43].

#### 5.6. Brainstorm

#### 5.7. EDF Browser

#### 5.8. EEGNET

#### 5.9. ELAN

#### 5.10. LIMO EEG

#### 5.11. MNE-MATLAB

#### 5.12. MNE-Python

#### 5.13. OpenViBE

#### 5.14. PyEEG

#### 5.15. Statistical Parametric Mapping (SPM)

#### 5.16. TAPEEG

#### 5.17. EEGLab

#### 5.18. MATLAB Plugins

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

ALLSSA | Anti-Leakage Least-Squares Spectral Analysis |

ANOVA | Analysis of Variance |

ANCOVAs | Analysis of Covariance |

BCI | Brain-Computer Interface |

BOLD | Blood Oxygen Level-Dependent |

BSF | Broad Spatial Frequency |

BSL | BIOPAC STUDENT LAB |

CCA | Canonical correlation analysis |

CLSSA | Constrained Least-Squares Spectral Analysis |

CNN | Convolutional Neural Network |

CSP | Common Spatial Pattern |

CWT | Continuous Wavelet Transform |

DFA | Detrended Fluctuation Analysis |

ECG | Electrocardiogram |

ECoG | Electrocorticogram |

EEG | Electroencephalography |

EMD | Empirical Mode Decomposition |

EMG | Electromyogram |

EOG | Electrooculogram |

ERPs | Event-Related Potentials |

FFT | Fast Fourier Transform |

FI | Fisher Information |

FIR | Finite Impulse Response |

GUI | Graphical User Interface |

HFD | Higuchi Fractal Dimension |

ICA | Independent Component Analysis |

iEEG | intracranial EEG |

IFFT | Inverse Fast Fourier Transform |

IIR | Infinite Impulse Response |

KNN | K-Nearest Neighbors |

LDA | Linear discriminant Analysis |

LFP | Local Field Potentials |

LMS | Least Mean Square |

LPP | Late Positive Potential |

LSSA | Least-Squares Spectral Analysis |

LSF | Low broad Spatial Frequency |

MALSSA | Multichannel Anti-Leakage |

MEG | Magnetoencephalogram |

NLM | Non-Local Means |

NMD | Non-linear Mode Decomposition |

PCA | Principal component analysis |

PFD | Petrosian Fractal Dimension |

PLS | Partial Least Squares |

PSD | Power Spectrum Density |

QDA | Quadratic Discriminant Analysis |

RDA | Regularized Discriminant Analysis |

RNN | Recurrent neural network |

SSP | Signal Space Projection |

SSA | Singular Spectrum Analysis |

SVM | Support Vector Machine |

SVDEn | Singular Value Decomposition Entropy |

XWT | Cross-Wavelet Transform |

## Appendix A

Name | Description |
---|---|

FIRfilt | Apply a variety of linear filters to EEGLAB data. |

CleanRawData | Cleans raw EEG data using a variety of methods, including Artifact Subspace Reconstruction. |

DIPFIT | Dipole modeling of independent data components using a spherical or boundary element head model. Uses functions from the FIELDTRIP toolbox. |

ICLabel | An automated EEG independent component classi- fier plugin for EEGLAB. |

App- MATLABViewer | The bids-matlab-tool repository contains a collection of functions to import and export BIDS (Brain Imag- ing Data Structure)-formated experiments. |

bids-matlab- tools | Import/export data in a wide variety of data formats. |

BIOSIG | Toolbox allowing data import in multiple data for- mats. It contains functions redundant with EEGLAB but also contains unique functions. |

FileIO | Import/export files from/to the Brain Vision Soft- ware Analyser suite. |

ANTeepimport | Import data files in the EEP format of the ANT EEG company. |

bva-io | Import/export files from/to the Brain Vision Soft- ware Analyser suite. |

neuroscanio | Import/export files from/to the Neuroscan software. |

MFFMATLABIO | Import/export files from/to the EGI company in MFF format. |

xdfimport | Import files in XDF (LSL) format (EEG stream and EEG marker stream only). |

Mobilab | Import files in XDF (LSL) format and allow fusing streams at different sampling rates for joint process- ing in EEGLAB |

IIRfilt | Apply short non-linear infinite impulse response filters to EEGLAB data. |

REST | A method to standardize a reference of scalp EEG recordings to a point at infinity. |

AAR | The Automatic Artifact Removal toolbox aims to integrate several state-of-the-art methods for the au- tomatic removal of ocular and muscular artifacts in the electroencephalogram (EEG). |

VisEd | The Vised Marks extension for EEGLAB adds editing functions to the native eegplot data scrolling figure. Specifically, it allows adding/editing event mark- ers, flagging channels/components, flagging time periods, and displaying the properties of the marks structure. |

get_chanlocs | The get_chanlocs EEGLAB plugin locates 3-D elec- trode positions from a 3-D scanned head image. A tutorial on how to acquire these images with off-the- shelf equipment is included. |

FMRIB | Remove fMRI-environment artifacts from EEGLAB data. This extension allows the removal of scanner- related artifacts from EEG data collected during fMRI scanning. |

BERGEN | Removal of fMRI-related gradient artifacts from si- multaneous EEG-fMRI data. The BERGEN extension for EEGLAB provides a GUI with different methods for gradient artifact correction. |

MARA | Automatic identification of artifactual independent components. MARA is a linear classifier that learns from expert ratings by extracting six features from the spatial, spectral, and temporal domains. |

FASTER | Implements a fully automated, unsupervised method for processing high-density EEG data. FASTER includes common features such as data im- porting, epoching, re-referencing, grand average cre- ation, automated channel, epoch, and artifact rejec- tion based on ICA. |

ADJUST | A completely automatic algorithm that identifies artifact-related Independent Components by com- bining stereotyped artifact-specific spatial and tem- poral features. |

CORRMAP | Semi-automatic identification of common EEG arti- facts based on a template. The CORRMAP extension consists of a set of MATLAB functions allowing the identification and clustering of independent compo- nents representing common EEG artifacts. |

CIAC | The cochlear implant artifact correction is a semi- automatic ICA-based tool for the correction of elec- trical artifacts originating from cochlear implants. |

RELICA | The goal of RELICA is to identify IC processes that are most stably separated from the decomposition data across many random bootstrap selections of its data frames or epochs. |

MP_clustering | A toolbox for Measure Projection Analysis for pro- jecting EEG measures tagged by source location into a common template brain space, testing local spatial measure consistency, and parsing measure- consistent brain areas into measure-separable domains. |

REGICA | An extension to remove EOG artifacts by regression performed on ICA components. A semi-simulated dataset that might be used in any artifact rejection study is also available. |

SASICA | SASICA is an EEGLAB plugin to help the researcher to reject/select independent components based on the various properties of these components. |

Automagic | Automagic is a MATLAB-based toolbox for prepro- cessing of EEG-datasets. It has been developed to offer user-friendly preprocessing software for big (and small) EEG datasets. |

AMICA | Adaptive Mixture Independent Component Analy- sis (AMICA) is a binary program and EEGLAB plu- gin that performs an independent component anal- ysis (ICA) decomposition on input data, potentially with multiple ICA models. Also, consider download- ing the postAmicaUtility plugin. |

## Appendix B

Name | Description |
---|---|

Signal Analyzer | A powerful toolbox of useful signal analysis functions. Provides visualization, preprocessing, filtering and analysis tools for generic signals. |

EEGLab | The most popular EEG specific MATLAB plugin. Provides extensive and state of the art EEG data processing. EEGLab is capable of performing most of the tasks described in this table, and has its own extensive ecosystem of plugins. |

NFT Toolbox | Neuroelectromagnetic Forward Head Modeling Toolbox. This tool is used to physically map EEG data to a head model. This allows for visualization and better understanding of the collected data. |

SIFT | Used for analysis of EEG data flow between multiple Sources. |

HeadIT | Database system for easy storage and retrieval of EEG data based on biomarkers and metadata. |

BCILAB | Toolbox for classification of EEG signals. Simplifies the creation of Brain Computer Interfaces by allowing MATLAB to classify EEG signals in real time. |

ERICA | Overarching framework which allows for recording, analysis, and stimulus control. |

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**Figure 2.**The discrete multiple source analysis and cortical CLARA image on the inflated cortex [37].

**Figure 3.**Architecture of the BioSig toolbox and its element [41].

**Figure 5.**General overview of the Brainstorm interface [44].

**Figure 6.**The interface of EDF Browse [46].

**Figure 7.**Pipelines of EEGNET (

**A**) EEG input signal, (

**B**) Sources reconstruction, (

**C**,

**D**) Connectivity matrices, (

**E**) 2D brain networks, (

**F**) 3D brain Network, (

**G**) Screenshot of the control panel used for tuning the different parameters (nodes, edges, threshold, surface) [49].

**Figure 8.**Overview of ELAN analysis workflow [51].

**Figure 9.**Hierarchical procedure of LIMO EEG [53].

**Figure 10.**Procedure and visualization of electrophysiology data using MNE-Python [54].

**Figure 11.**The PyEEG framework [58].

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Das, R.K.; Martin, A.; Zurales, T.; Dowling, D.; Khan, A.
A Survey on EEG Data Analysis Software. *Sci* **2023**, *5*, 23.
https://doi.org/10.3390/sci5020023

**AMA Style**

Das RK, Martin A, Zurales T, Dowling D, Khan A.
A Survey on EEG Data Analysis Software. *Sci*. 2023; 5(2):23.
https://doi.org/10.3390/sci5020023

**Chicago/Turabian Style**

Das, Rupak Kumar, Anna Martin, Tom Zurales, Dale Dowling, and Arshia Khan.
2023. "A Survey on EEG Data Analysis Software" *Sci* 5, no. 2: 23.
https://doi.org/10.3390/sci5020023