A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance

Motor imagery–based brain–computer interface (BCI) using electroencephalography (EEG) has demonstrated promising applications by directly decoding users' movement related mental intention. The selection of control signals, e.g., the channel configuration and decoding algorithm, plays a vital role in the online performance and progressing of BCI control. While several offline analyses report the effect of these factors on BCI accuracy for a single session—performance increases asymptotically by increasing the number of channels, saturates, and then decreases—no online study, to the best of our knowledge, has yet been performed to compare for a single session or across training. The purpose of the current study is to assess, in a group of forty-five subjects, the effect of channel number and decoding method on the progression of BCI performance across multiple training sessions and the corresponding neurophysiological changes. The 45 subjects were divided into three groups using Laplacian Filtering (LAP/S) with nine channels, Common Spatial Pattern (CSP/L) with 40 channels and CSP (CSP/S) with nine channels for online decoding. At the first training session, subjects using CSP/L displayed no significant difference compared to CSP/S but a higher average BCI performance over those using LAP/S. Despite the average performance when using the LAP/S method was initially lower, but LAP/S displayed improvement over first three sessions, whereas the other two groups did not. Additionally, analysis of the recorded EEG during BCI control indicates that the LAP/S produces control signals that are more strongly correlated with the target location and a higher R-square value was shown at the fifth session. In the present study, we found that subjects' average online BCI performance using a large EEG montage does not show significantly better performance after the first session than a smaller montage comprised of a common subset of these electrodes. The LAP/S method with a small EEG montage allowed the subjects to improve their skills across sessions, but no improvement was shown for the CSP method.

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

[2]  José del R. Millán,et al.  Brain-Controlled Wheelchairs: A Robotic Architecture , 2013, IEEE Robotics & Automation Magazine.

[3]  G Pfurtscheller,et al.  Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI). , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[4]  Jianjun Meng,et al.  Optimizing spatial spectral patterns jointly with channel configuration for brain-computer interface , 2013, Neurocomputing.

[5]  Bin He,et al.  Sensorimotor Rhythm BCI with Simultaneous High Definition-Transcranial Direct Current Stimulation Alters Task Performance , 2016, Brain Stimulation.

[6]  Bin He,et al.  EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks , 2016, IEEE Transactions on Biomedical Engineering.

[7]  Xingyu Wang,et al.  An adaptive P300-based control system , 2011, Journal of neural engineering.

[8]  Jianjun Meng,et al.  Improved Semisupervised Adaptation for a Small Training Dataset in the Brain–Computer Interface , 2014, IEEE Journal of Biomedical and Health Informatics.

[9]  An H. Do,et al.  The feasibility of a brain-computer interface functional electrical stimulation system for the restoration of overground walking after paraplegia , 2015, Journal of NeuroEngineering and Rehabilitation.

[10]  Xiangyang Zhu,et al.  A novel calibration and task guidance framework for motor imagery BCI via a tendon vibration induced sensation with kinesthesia illusion , 2015, Journal of neural engineering.

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

[12]  K.-R. Muller,et al.  Optimizing Spatial filters for Robust EEG Single-Trial Analysis , 2008, IEEE Signal Processing Magazine.

[13]  Klaus-Robert Müller,et al.  On Optimal Channel Configurations for SMR-based Brain–Computer Interfaces , 2010, Brain Topography.

[14]  Jason Farquhar,et al.  Detection of attempted movement from the EEG during neuromuscular block: proof of principle study in awake volunteers , 2015, Scientific Reports.

[15]  N. Birbaumer,et al.  Predictability of Brain-Computer Communication , 2004 .

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

[17]  José del R. Millán,et al.  Brain-Computer Interfaces , 2020, Handbook of Clinical Neurology.

[18]  G. Pfurtscheller,et al.  The BCI competition III: validating alternative approaches to actual BCI problems , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  Tzyy-Ping Jung,et al.  High-speed spelling with a noninvasive brain–computer interface , 2015, Proceedings of the National Academy of Sciences.

[20]  G. Pfurtscheller,et al.  ‘Thought’ – control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia , 2003, Neuroscience Letters.

[21]  K. Lafleur,et al.  Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface , 2013, Journal of neural engineering.

[22]  Klaus-Robert Müller,et al.  The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects , 2008, IEEE Transactions on Biomedical Engineering.

[23]  Tom Manly,et al.  The P300 as a Marker of Waning Attention and Error Propensity , 2008, Comput. Intell. Neurosci..

[24]  Muhammad Abd-El-Barr,et al.  Long-term Training With a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients. , 2016, Neurosurgery.

[25]  Dennis J McFarland,et al.  The advantages of the surface Laplacian in brain-computer interface research. , 2015, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[26]  Xingyu Wang,et al.  Improved SFFS method for channel selection in motor imagery based BCI , 2016, Neurocomputing.

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

[28]  G. Pfurtscheller,et al.  Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. , 2005, Brain research. Cognitive brain research.

[29]  J R Wolpaw,et al.  Spatial filter selection for EEG-based communication. , 1997, Electroencephalography and clinical neurophysiology.

[30]  Clemens Brunner,et al.  Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.

[31]  C. Neuper,et al.  Sensorimotor rhythm-based brain–computer interface training: the impact on motor cortical responsiveness , 2011, Journal of neural engineering.

[32]  Bin He,et al.  A novel channel selection method for optimal classification in different motor imagery BCI paradigms , 2015, BioMedical Engineering OnLine.

[33]  Cuntai Guan,et al.  Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI , 2011, IEEE Transactions on Biomedical Engineering.

[34]  Cuntai Guan,et al.  Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms , 2011, IEEE Transactions on Biomedical Engineering.

[35]  B. Hjorth An on-line transformation of EEG scalp potentials into orthogonal source derivations. , 1975, Electroencephalography and clinical neurophysiology.

[36]  Bernhard Schölkopf,et al.  Support vector channel selection in BCI , 2004, IEEE Transactions on Biomedical Engineering.

[37]  Xingyu Wang,et al.  Optimized Motor Imagery Paradigm Based on Imagining Chinese Characters Writing Movement , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[38]  K. Müller,et al.  Psychological predictors of SMR-BCI performance , 2012, Biological Psychology.

[39]  Jonathan R Wolpaw,et al.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[40]  Motoaki Kawanabe,et al.  Divergence-Based Framework for Common Spatial Patterns Algorithms , 2014, IEEE Reviews in Biomedical Engineering.

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

[42]  Bin He,et al.  EEG Control of a Virtual Helicopter in 3-Dimensional Space Using Intelligent Control Strategies , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[43]  L. Cohen,et al.  Brain–machine interface in chronic stroke rehabilitation: A controlled study , 2013, Annals of neurology.

[44]  Bin He,et al.  Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks , 2016, Scientific Reports.

[45]  Xingyu Wang,et al.  P300 Chinese input system based on Bayesian LDA , 2010, Biomedizinische Technik. Biomedical engineering.

[46]  Vera Kaiser,et al.  Cortical effects of user training in a motor imagery based brain–computer interface measured by fNIRS and EEG , 2014, NeuroImage.

[47]  M. Molinari,et al.  Brain–computer interface boosts motor imagery practice during stroke recovery , 2015, Annals of neurology.

[48]  Wei Sun,et al.  A Semisupervised Support Vector Machines Algorithm for BCI Systems , 2007, Comput. Intell. Neurosci..

[49]  Julien Doyon,et al.  Functional neuroanatomical networks associated with expertise in motor imagery , 2008, NeuroImage.

[50]  Maarten De Vos,et al.  Real-time EEG feedback during simultaneous EEG–fMRI identifies the cortical signature of motor imagery , 2015, NeuroImage.

[51]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[52]  Bin He,et al.  The impact of mind-body awareness training on the early learning of a brain-computer interface. , 2014, Technology.

[53]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[54]  J. Lawson Design and Analysis of Experiments with R , 2014 .

[55]  N. Birbaumer,et al.  The Influence of Psychological State and Motivation on Brain–Computer Interface Performance in Patients with Amyotrophic Lateral Sclerosis – a Longitudinal Study , 2010, Front. Neuropharma..

[56]  Minkyu Ahn,et al.  Journal of Neuroscience Methods , 2015 .

[57]  N. Birbaumer,et al.  The thought-translation device (TTD): neurobehavioral mechanisms and clinical outcome , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.