Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces

Human-Machine Interfaces (HMI) allow users to interact with different devices such as computers or home elements. A key part in HMI is the design of simple non-invasive interfaces to capture the signals associated with the user’s intentions. In this work, we have designed two different approaches based on Electroencephalography (EEG) and Electrooculography (EOG). For both cases, signal acquisition is performed using only one electrode, which makes placement more comfortable compared to multi-channel systems. We have also developed a Graphical User Interface (GUI) that presents objects to the user using two paradigms—one-by-one objects or rows-columns of objects. Both interfaces and paradigms have been compared for several users considering interactions with home elements.

[1]  Yuanqing Li,et al.  A hybrid BCI web browser based on EEG and EOG signals , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[2]  Adriana Dapena,et al.  A Prototype of EEG System for IoT , 2020, Int. J. Neural Syst..

[3]  Guangli Li,et al.  Novel passive ceramic based semi-dry electrodes for recording electroencephalography signals from the hairy scalp , 2016 .

[4]  R. Fazel-Rezai,et al.  Analysis of P300 Classifiers in Brain Computer Interface Speller , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  E. W. Sellers,et al.  Toward enhanced P300 speller performance , 2008, Journal of Neuroscience Methods.

[6]  Leif Sörnmo.  Chapter 4 – Evoked Potentials , 2005 .

[7]  Ferdinando Grossi,et al.  Home smart home: brain-computer interface control for real smart home environments , 2010 .

[8]  Geraldine B. Boylan,et al.  Analysis of a Low-Cost EEG Monitoring System and Dry Electrodes toward Clinical Use in the Neonatal ICU , 2019, Sensors.

[9]  J. Wolpaw,et al.  A novel P300-based brain–computer interface stimulus presentation paradigm: Moving beyond rows and columns , 2010, Clinical Neurophysiology.

[10]  E Donchin,et al.  The mental prosthesis: assessing the speed of a P300-based brain-computer interface. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[11]  Manuel Mazo,et al.  Wheelchair Guidance Strategies Using EOG , 2002, J. Intell. Robotic Syst..

[12]  I. Reinvang.  Cognitive Event-Related Potentials in Neuropsychological Assessment , 1999, Neuropsychology Review.

[13]  Kwang Suk Park,et al.  A Novel Wearable Forehead EOG Measurement System for Human Computer Interfaces , 2017, Sensors.

[14]  Reza Fazel-Rezai,et al.  A region-based P300 speller for brain-computer interface , 2009, Canadian Journal of Electrical and Computer Engineering.

[15]  Guangli Li,et al.  Review of semi-dry electrodes for EEG recording , 2020, Journal of neural engineering.

[16]  Xiaowei Yang,et al.  A human-machine interface based on single channel EOG and patchable sensor , 2016, Biomed. Signal Process. Control..

[17]  Minho Hwang,et al.  Online Home Appliance Control Using EEG-Based Brain–Computer Interfaces , 2019, Electronics.

[18]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[19]  S. Coyle,et al.  Brain–computer interfaces: a review , 2003 .

[20]  Anders M. Fjell,et al.  P300 and Neuropsychological Tests as Measures of Aging: Scalp Topography and Cognitive Changes , 2004, Brain Topography.

[21]  Touradj Ebrahimi,et al.  An efficient P300-based brain–computer interface for disabled subjects , 2008, Journal of Neuroscience Methods.

[22]  Gerhard Tröster,et al.  Eye Movement Analysis for Activity Recognition Using Electrooculography , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Febo Cincotti,et al.  Assistive device with conventional, alternative, and brain-computer interface inputs to enhance interaction with the environment for people with amyotrophic lateral sclerosis: a feasibility and usability study. , 2015, Archives of physical medicine and rehabilitation.

[24]  Dongchu Chen,et al.  Towards emerging EEG applications: a novel printable flexible Ag/AgCl dry electrode array for robust recording of EEG signals at forehead sites , 2020, Journal of neural engineering.

[25]  Peter D. Lawrence,et al.  A non-contact device for tracking gaze in a human computer interface , 2005, Comput. Vis. Image Underst..

[26]  Brendan Z. Allison,et al.  P300 brain computer interface: current challenges and emerging trends , 2012, Front. Neuroeng..

[27]  Peter M. Corcoran,et al.  Statistical models of appearance for eye tracking and eye-blink detection and measurement , 2008, IEEE Transactions on Consumer Electronics.

[28]  J. Polich.  Updating P300: An integrative theory of P3a and P3b , 2007, Clinical Neurophysiology.

[29]  E. Donchin,et al.  A P300-based brain–computer interface: Initial tests by ALS patients , 2006, Clinical Neurophysiology.

[30]  M. Thulasidas,et al.  Robust classification of EEG signal for brain-computer interface , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[31]  Fusheng Yang,et al.  BCI competition 2003-data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications , 2004, IEEE Transactions on Biomedical Engineering.

[32]  H. Adeli,et al.  Brain-computer interface technologies: from signal to action , 2013, Reviews in the neurosciences.

[33]  Ferdinando Grossi,et al.  Light on! Real world evaluation of a P300-based brain–computer interface (BCI) for environment control in a smart home , 2012, Ergonomics.

[34]  Kenneth B. Campbell,et al.  Intelligence and the effects of perceptual processing demands, task difficulty and processing speed on P300, reaction time and movement time , 1998 .

[35]  J. Polich,et al.  Meta-analysis of P300 normative aging studies. , 1996, Psychophysiology.

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

[37]  Reza Fazel-Rezai,et al.  A Review of Hybrid Brain-Computer Interface Systems , 2013, Adv. Hum. Comput. Interact..

[38]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[39]  Yuanqing Li,et al.  An asynchronous wheelchair control by hybrid EEG–EOG brain–computer interface , 2014, Cognitive Neurodynamics.

[40]  Chun-Liang Hsu,et al.  EOG-based Human-Computer Interface system development , 2010, Expert Syst. Appl..

[41]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[42]  K. A. Colwell,et al.  Channel selection methods for the P300 Speller , 2014, Journal of Neuroscience Methods.

[43]  Chris Van Hoof,et al.  Soft, Comfortable Polymer Dry Electrodes for High Quality ECG and EEG Recording , 2014, Sensors.

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

[45]  Behzad Nazari,et al.  A single channel-single trial P300 detection algorithm , 2013, 2013 21st Iranian Conference on Electrical Engineering (ICEE).

[46]  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.

[47]  Tzyy-Ping Jung,et al.  Dry-Contact and Noncontact Biopotential Electrodes: Methodological Review , 2010, IEEE Reviews in Biomedical Engineering.

[48]  Paul Blenkhorn,et al.  Blink detection for real-time eye tracking , 2002, J. Netw. Comput. Appl..

[49]  Dean J Krusienski,et al.  A comparison of classification techniques for the P300 Speller , 2006, Journal of neural engineering.

[50]  Chang Liu,et al.  Single channel single trial P300 detection using extreme learning machine: Compared with BPNN and SVM , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[51]  M. Bradley,et al.  Event-Related Potentials (ERPs) , 2012 .

[52]  Chang-Hwan Im,et al.  Detection of eye blink artifacts from single prefrontal channel electroencephalogram , 2016, Comput. Methods Programs Biomed..

[53]  Dan Zhang,et al.  Novel semi-dry electrodes for brain–computer interface applications , 2016, Journal of neural engineering.

[54]  Daniel Sánchez Morillo,et al.  Dry EEG Electrodes , 2014, Sensors.

[55]  Rebeca Corralejo,et al.  A P300-based brain–computer interface aimed at operating electronic devices at home for severely disabled people , 2014, Medical & Biological Engineering & Computing.

[56]  J. Kleinman,et al.  Speaking, thinking, and blinking , 1981, Psychiatry Research.

[57]  T W Picton,et al.  The P300 Wave of the Human Event‐Related Potential , 1992, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[58]  Brendan Z. Allison,et al.  The Hybrid BCI , 2010, Frontiers in Neuroscience.

[59]  Seungjin Choi,et al.  A hybrid EOG-P300 BCI with dual monitors , 2014, 2014 International Winter Workshop on Brain-Computer Interface (BCI).

[60]  Chabane Djeraba,et al.  Drowsy driver detection system using eye blink patterns , 2010, 2010 International Conference on Machine and Web Intelligence.

[61]  A. Krolak,et al.  Vision-based eye blink monitoring system for human-computer interfacing , 2008, 2008 Conference on Human System Interactions.

[62]  Yuanqing Li,et al.  A Single-Channel EOG-Based Speller , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[63]  Refet Firat Yazicioglu,et al.  A low-power, wireless, 8-channel EEG monitoring headset , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[64]  E. Donchin,et al.  The contingent negative variation and the late positive wave of the average evoked potential. , 1970, Electroencephalography and clinical neurophysiology.

[65]  F Babiloni,et al.  P300-based brain–computer interface for environmental control: an asynchronous approach , 2011, Journal of neural engineering.

[66]  Yuanqing Li,et al.  A Hybrid Asynchronous Brain-Computer Interface Combining SSVEP and EOG Signals , 2020, IEEE Transactions on Biomedical Engineering.

[67]  Chin-Teng Lin,et al.  Controlling a Human–Computer Interface System With a Novel Classification Method that Uses Electrooculography Signals , 2013, IEEE Transactions on Biomedical Engineering.