A P300-based brain computer interface for smart home interaction through an ANFIS ensemble

Adaptive neuro fuzzy Inference systems (ANFIS) has been applied in brain computer interfaces (BcI) in different ways such as mapping of P300 or fusing information from EEG channels and it has reached high classification accuracy. This work proposes a combination of ANFIS classifiers by voting for a single-trial detection of a P300 wave in a BCI, using four channels; five healthy subjects and three post-stroke patients have participated in this study, each participant performs 4 BCI sessions, crossvalidation is applied to evaluate the classifier performance. The results of average accuracy were greater than 75% for all subjects, similar results were gotten for healthy subjects and post-stroke patients, but the better classifiers for each subject have achieved accuracies greater than 80%.

[1]  Ali Motie Nasrabadi,et al.  Fusion of classic P300 detection methods' inferences in a framework of fuzzy labels , 2008, Artif. Intell. Medicine.

[2]  Mel Slater,et al.  Virtual Smart Home Controlled by Thoughts , 2009, 2009 18th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises.

[3]  S Frenzel,et al.  Two communication lines in a 3 × 3 matrix speller. , 2011, Journal of neural engineering.

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

[5]  Guang-Zhong Yang,et al.  A Self-Adaptive Online Brain–Machine Interface of a Humanoid Robot Through a General Type-2 Fuzzy Inference System , 2018, IEEE Transactions on Fuzzy Systems.

[6]  Eric W Sellers,et al.  Manipulating attention via mindfulness induction improves P300-based brain–computer interface performance , 2011, Journal of neural engineering.

[7]  田中 武昌,et al.  Adaptive Neuro-Fuzzy Inference System (ANFIS)の超音波診断支援への試用(一般講演) , 2000 .

[8]  Touradj Ebrahimi,et al.  Recent advances in brain-computer interfaces , 2007, 2007 IEEE 9th Workshop on Multimedia Signal Processing.

[9]  Gunther Krausz,et al.  ow many people are able to control a P 300-based brain – computer nterface ( BCI ) ? , 2009 .

[10]  Christian H. Flores Vega,et al.  Cognitive task discrimination using approximate entropy (ApEn) on EEG signals , 2013, 2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC).

[11]  B. Blankertz,et al.  (C)overt attention and visual speller design in an ERP-based brain-computer interface , 2010, Behavioral and Brain Functions.

[12]  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.

[13]  Jonathan R Wolpaw,et al.  A brain-computer interface for long-term independent home use , 2010, Amyotrophic lateral sclerosis : official publication of the World Federation of Neurology Research Group on Motor Neuron Diseases.

[14]  J. Wolpaw,et al.  Does the ‘P300’ speller depend on eye gaze? , 2010, Journal of neural engineering.

[15]  Nader Pouratian,et al.  Integrating Language Information With a Hidden Markov Model to Improve Communication Rate in the P300 Speller , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[17]  F. Cincotti,et al.  Eye-gaze independent EEG-based brain–computer interfaces for communication , 2012, Journal of neural engineering.

[18]  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.

[19]  Liviu Goras,et al.  On compressed sensing for EEG signals - validation with P300 speller paradigm , 2016, 2016 International Conference on Communications (COMM).

[20]  F. Babiloni,et al.  A covert attention P300-based brain–computer interface: Geospell , 2012, Ergonomics.

[21]  Amit Konar,et al.  Interval type-2 fuzzy logic based multiclass ANFIS algorithm for real-time EEG based movement control of a robot arm , 2015, Robotics Auton. Syst..

[22]  R. K. Chaurasiya,et al.  A modified approach to ensemble of SVM for P300 based brain computer interface , 2016, 2016 International Conference on Advances in Human Machine Interaction (HMI).

[23]  M S Treder,et al.  Gaze-independent brain–computer interfaces based on covert attention and feature attention , 2011, Journal of neural engineering.

[24]  Vicente Alarcon-Aquino,et al.  Anfis-Based P300 Rhythm Detection Using Wavelet Feature Extraction on Blind Source Separated Eeg Signals , 2011 .

[25]  Chang-Hwan Im,et al.  EEG-Based Brain-Computer Interfaces: A Thorough Literature Survey , 2013, Int. J. Hum. Comput. Interact..

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

[27]  I. Scott MacKenzie,et al.  Effects of feedback and dwell time on eye typing speed and accuracy , 2006, Universal Access in the Information Society.

[28]  J. R. Wolpaw,et al.  ' s personal copy A novel P 300-based brain – computer interface stimulus presentation paradigm : Moving beyond rows and columns q , 2010 .

[29]  Yuanqing Li,et al.  Toward improved P300 speller performance in outdoor environment using polarizer , 2016, 2016 12th World Congress on Intelligent Control and Automation (WCICA).