The evolution of AI approaches for motor imagery EEG-based BCIs

The Motor Imagery (MI) electroencephalography (EEG) based Brain Computer Interfaces (BCIs) allow the direct communication between humans and machines by exploiting the neural pathways connected to motor imagination. Therefore, these systems open the possibility of developing applications that could span from the medical field to the entertainment industry. In this context, Artificial Intelligence (AI) approaches become of fundamental importance especially when wanting to provide a correct and coherent feedback to BCI users. Moreover, publicly available datasets in the field of MI EEG-based BCIs have been widely exploited to test new techniques from the AI domain. In this work, AI approaches applied to datasets collected in different years and with different devices but with coherent experimental paradigms are investigated with the aim of providing a concise yet sufficiently comprehensive survey on the evolution and influence of AI techniques on MI EEG-based BCI data.

[1]  David Zambrana Vinaroz,et al.  Validation of Continuous Monitoring System for Epileptic Users in Outpatient Settings , 2022, Sensors.

[2]  Victoria Peterson,et al.  A motor imagery vs. rest dataset with low-cost consumer grade EEG hardware , 2022, Data in brief.

[3]  F. Ebrahimi,et al.  A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals , 2022, Comput. Biol. Medicine.

[4]  B. K. Rout,et al.  A review on Virtual Reality and Augmented Reality use-cases of Brain Computer Interface based applications for smart cities , 2021, Microprocess. Microsystems.

[5]  John Raiti,et al.  Demonstration of low-cost EEG system providing on-demand communication for locked-in patients , 2021, 2021 IEEE Global Humanitarian Technology Conference (GHTC).

[6]  Serhii Lupenko,et al.  Comprehensive justification for the choice of software development tools and hardware components of a multi-channel neurointerface system , 2021, 2021 IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT).

[7]  Hans W. Guesgen,et al.  A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface , 2021, Sensors.

[8]  A. Eskandarian,et al.  EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification , 2021, Journal of neural engineering.

[9]  N.S. Malan,et al.  Motor Imagery EEG Spectral-Spatial Feature Optimization Using Dual-Tree Complex Wavelet and Neighbourhood Component Analysis , 2021 .

[10]  Hesam Varsehi,et al.  An EEG channel selection method for motor imagery based brain-computer interface and neurofeedback using Granger causality , 2020, Neural Networks.

[11]  Zehong Cao.  A review of artificial intelligence for EEG‐based brain−computer interfaces and applications , 2020 .

[12]  L. Benini,et al.  An Accurate EEGNet-based Motor-Imagery Brain–Computer Interface for Low-Power Edge Computing , 2020, 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[13]  Jie Wang,et al.  Motor imagery EEG classification based on ensemble support vector learning , 2020, Comput. Methods Programs Biomed..

[14]  Victoria Peterson,et al.  A feasibility study of a complete low-cost consumer-grade brain-computer interface system , 2020, Heliyon.

[15]  Leonardo Cunha de Miranda,et al.  Brain–Computer Interface Games Based on Consumer-Grade EEG Devices: A Systematic Literature Review , 2019, Int. J. Hum. Comput. Interact..

[16]  Zong Qun,et al.  A novel hybrid deep learning scheme for four-class motor imagery classification , 2019, Journal of neural engineering.

[17]  Shiru Sharma,et al.  Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals , 2019, Comput. Biol. Medicine.

[18]  Ikhtiyor Majidov,et al.  Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods , 2019, Sensors.

[19]  Dezhong Yao,et al.  Separated channel convolutional neural network to realize the training free motor imagery BCI systems , 2019, Biomed. Signal Process. Control..

[20]  Yongtian He,et al.  Deep learning for electroencephalogram (EEG) classification tasks: a review , 2019, Journal of neural engineering.

[21]  Mario Ignacio Chacon Murguia,et al.  Classification of multiple motor imagery using deep convolutional neural networks and spatial filters , 2019, Appl. Soft Comput..

[22]  Shuai Wang,et al.  EEG Classification of Motor Imagery Using a Novel Deep Learning Framework , 2019, Sensors.

[23]  Sadasivan Puthusserypady,et al.  An end-to-end deep learning approach to MI-EEG signal classification for BCIs , 2018, Expert Syst. Appl..

[24]  Clemens Brunner,et al.  Frequency Specific Cortical Dynamics During Motor Imagery Are Influenced by Prior Physical Activity , 2018, Front. Psychol..

[25]  Behzad Mozaffari Tazehkand,et al.  A New Self-Regulated Neuro-Fuzzy Framework for Classification of EEG Signals in Motor Imagery BCI , 2018, IEEE Transactions on Fuzzy Systems.

[26]  Lina Yao,et al.  Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals , 2017, 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[27]  Lina Yao,et al.  Intent Recognition in Smart Living Through Deep Recurrent Neural Networks , 2017, ICONIP.

[28]  Girijesh Prasad,et al.  Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface , 2015, Soft Computing.

[29]  Javier Gomez-Pilar,et al.  Adaptive semi-supervised classification to reduce intersession non-stationarity in multiclass motor imagery-based brain-computer interfaces , 2015, Neurocomputing.

[30]  J. Huggins,et al.  Brain-computer interface: current and emerging rehabilitation applications. , 2015, Archives of physical medicine and rehabilitation.

[31]  Swati Vaid,et al.  EEG Signal Analysis for BCI Interface: A Review , 2015, 2015 Fifth International Conference on Advanced Computing & Communication Technologies.

[32]  Javier Gomez-Pilar,et al.  Adaptive Stacked Generalization for Multiclass Motor Imagery-Based Brain Computer Interfaces , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[33]  Cuntai Guan,et al.  Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b , 2012, Front. Neurosci..

[34]  Ad Aertsen,et al.  Review of the BCI Competition IV , 2012, Front. Neurosci..

[35]  黄亚明.  PhysioBank , 2009 .

[36]  Cuntai Guan,et al.  Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[37]  G. Pfurtscheller,et al.  Brain–Computer Communication: Motivation, Aim, and Impact of Exploring a Virtual Apartment , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[38]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

[39]  Steven Lemm,et al.  BCI competition 2003-data set III: probabilistic modeling of sensorimotor /spl mu/ rhythms for classification of imaginary hand movements , 2004, IEEE Transactions on Biomedical Engineering.

[40]  Klaus-Robert Müller,et al.  The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials , 2004, IEEE Transactions on Biomedical Engineering.

[41]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[42]  G. Pfurtscheller,et al.  Graz brain-computer interface II: towards communication between humans and computers based on online classification of three different EEG patterns , 1996, Medical and Biological Engineering and Computing.

[43]  2021 IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT) , 2021 .

[44]  Francisco J. Pelayo,et al.  Trends in EEG-BCI for daily-life: Requirements for artifact removal , 2017, Biomed. Signal Process. Control..

[45]  Danilo P. Mandic,et al.  Augmented Complex Common Spatial Patterns for Classification of Noncircular EEG From Motor Imagery Tasks , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[46]  Jason Sleight,et al.  Classification of Executed and Imagined Motor Movement EEG Signals , 2009 .

[47]  C. Torrence,et al.  A Practical Guide to Wavelet Analysis. , 1998 .