Recent Trends and Open Challenges in EEG Based Brain-Computer Interface Systems

Recent advances in computer hardware and signal processing have made possible the use of electroencephalogram (EEG) for communication between human brain and computers and this technology is known as brain-computer interface (BCI). Locked-in patients have now a way to communicate with the outside world using BCI technology. Nowadays, BCIs are getting popularity among the researchers to control devices using brainwaves especially in providing good assistance to disabled people. Impressive development and integration of both hardware and software in BCI have been carried out in the last two decades. However, some open challenges and limitations have also been exposed in the previous researches. In this paper, we have tried to mention some critical issues of EEG based BCI system including EEG modalities, EEG acquisition, signal processing algorithm and performance evaluation. These issues need to be solved to develop error-free BCI system. In addition, possible solutions and future directions have also been discussed.

[1]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[2]  Sabira Khatun,et al.  The Classification of EEG Signal Using Different Machine Learning Techniques for BCI Application , 2018, RiTA.

[3]  Reza Abiri,et al.  A comprehensive review of EEG-based brain–computer interface paradigms , 2019, Journal of neural engineering.

[4]  Ayman Atia,et al.  Brain computer interfacing: Applications and challenges , 2015 .

[5]  Anatole Lécuyer,et al.  Author manuscript, published in "IEEE Transactions on Computational Intelligence and AI in games (2013)" Two Brains, One Game: Design and Evaluation of a Multi-User BCI Video Game Based on Motor Imagery , 2022 .

[6]  Mousa K. Wali,et al.  Classification of driver drowsiness level using wireless EEG , 2013 .

[7]  Mihaly Benda,et al.  Brain–Computer Interface Spellers: A Review , 2018, Brain sciences.

[8]  N. Birbaumer.  Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. , 2006, Psychophysiology.

[9]  Rihab Bousseta,et al.  EEG Based Brain Computer Interface for Controlling a Robot Arm Movement Through Thought , 2018 .

[10]  J. Polich,et al.  On P300 measurement stability: habituation, intra-trial block variation, and ultradian rhythms , 1999, Biological Psychology.

[11]  Bao-Liang Lu,et al.  Emotional state classification from EEG data using machine learning approach , 2014, Neurocomputing.

[12]  Irshad Ahmad Ansari,et al.  BCI: an optimised speller using SSVEP , 2016 .

[13]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[14]  张宇,et al.  脑控: 基于脑——机接口的人机融合控制 , 2014 .

[15]  G. Mengozzi,et al.  Assessment of Diagnostic and Prognostic Role of Copeptin in the Clinical Setting of Sepsis , 2016, BioMed research international.

[16]  G. Pfurtscheller,et al.  Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces? , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[17]  Emmanuel Maby,et al.  BCI Could Make Old Two-Player Games Even More Fun: A Proof of Concept with "Connect Four" , 2012, Adv. Hum. Comput. Interact..

[18]  Á Fernández-Rodríguez,et al.  Review of real brain-controlled wheelchairs , 2016, Journal of neural engineering.

[19]  Wanli Ma,et al.  Age and gender classification using EEG paralinguistic features , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[20]  Feyzullah Temurtas,et al.  Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN , 2016, Australasian Physical & Engineering Sciences in Medicine.

[21]  Zhihong Liu,et al.  Study of the Algorithm for the Classification of Brain Waves , 2017, CSPS.

[22]  Montri Phothisonothai,et al.  EEG signal classification method based on fractal features and neural network , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  J. Cohen,et al.  P300, stimulus intensity, modality, and probability. , 1996, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[24]  Stefan Haufe,et al.  Optimizing event-related potential based brain-computer interfaces: a systematic evaluation of dynamic stopping methods. , 2013, Journal of neural engineering.

[25]  Jaime Gómez Gil,et al.  Brain Computer Interfaces, a Review , 2012, Sensors.

[26]  Eda Akman Aydin,et al.  P300-Based Asynchronous Brain Computer Interface for Environmental Control System , 2018, IEEE Journal of Biomedical and Health Informatics.

[27]  Areej Al-Wabil,et al.  Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review , 2017 .

[28]  Guillaume Gibert,et al.  xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface , 2009, IEEE Transactions on Biomedical Engineering.

[29]  Yu Zhang,et al.  Brain Control: Human-computer Integration Control Based on Brain-computer Interface Approach , 2013 .

[30]  Pasin Israsena,et al.  Emotion classification using minimal EEG channels and frequency bands , 2013, The 2013 10th International Joint Conference on Computer Science and Software Engineering (JCSSE).

[31]  Chi Zhang,et al.  An EEG-Based Person Authentication System with Open-Set Capability Combining Eye Blinking Signals , 2018, Sensors.

[32]  Nurullah Akkaya,et al.  Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks , 2016, BioMed research international.

[33]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.

[34]  M. J. E. Salami,et al.  EEG signal classification for real-time brain-computer interface applications: A review , 2011, 2011 4th International Conference on Mechatronics (ICOM).

[35]  Leonardo Cunha de Miranda,et al.  A Survey of Interactive Systems based on Brain- Computer Interfaces , 2013 .

[36]  Iñaki Iturrate,et al.  A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation , 2009, IEEE Transactions on Robotics.

[37]  Byron M. Yu,et al.  Brain–computer interfaces for dissecting cognitive processes underlying sensorimotor control , 2016, Current Opinion in Neurobiology.

[38]  Mário Sarcinelli Filho,et al.  Proposal of a SSVEP-BCI to Command a Robotic Wheelchair , 2013 .

[39]  Athanasios V. Vasilakos,et al.  Brain computer interface: control signals review , 2017, Neurocomputing.

[40]  Rui Zhang,et al.  A BCI-Based Environmental Control System for Patients With Severe Spinal Cord Injuries , 2017, IEEE Transactions on Biomedical Engineering.

[41]  Puteh Saad,et al.  A survey of analysis and classification of EEG signals for brain-computer interfaces , 2015, 2015 2nd International Conference on Biomedical Engineering (ICoBE).

[42]  Yijun Wang,et al.  A high-speed BCI based on code modulation VEP , 2011, Journal of neural engineering.

[43]  Xingyu Wang,et al.  Optimized stimulus presentation patterns for an event-related potential EEG-based brain–computer interface , 2011, Medical & Biological Engineering & Computing.

[44]  Yuanqing Li,et al.  Target Selection With Hybrid Feature for BCI-Based 2-D Cursor Control , 2012, IEEE Transactions on Biomedical Engineering.

[45]  Fazly Salleh Abas,et al.  Superpixel Classification with Color and Texture Features for Automated Wound Area Segmentation , 2018, 2018 IEEE Student Conference on Research and Development (SCOReD).