A Brain-Computer Interface Test-Bench Based on EEG Signals for Research and Student Training Paweá Raif, Ph.D.
Aleksandra Káos-Witkowska, Ph.D.
Dept. of Biosensors and Biomedical Signals Processing, Silesian University of Technology, Gliwice, Poland
[email protected]
Department of Electrical Engineering and Automation University of Bielsko-Biaáa, Bielsko-Biaáa, Poland
[email protected]
Mufti Mahmud, Ph.D.§
Renata Suchanek, Ph.D.
NeuroChip Laboratory, University of Padova, Padova, Italy
[email protected]
Department of Medical Genetics, Medical University of Silesia, Sosnowiec, Poland
[email protected]
Amir Hussain, Ph.D. Division of Computing Science & Maths, University of Stirling, Scotland, UK
[email protected] §
Second Affiliation: Institute of Information Technology, Jahangirnagar University, Savar, 1342 – Dhaka, Bangladesh
Abstract—The paper describes a test-bench model for braincomputer interface research based on EEG signals. The testbench is going to be used for students training and education. The goal is to prepare modern Brain–Computer Interface development environment in order to create interest about this topic among the students.
This paper will concentrate only on non-invasive brain computer interface based on EEG signals analysis with free and open source software. Our aim is to propose simple test-bench for brain-computer interface dedicated for both research and education purposes.
Keywords—Brain-computer interface, BCI in research and education, Electroencephalography, EEG signals.
C. BCI components Brain Computer Interface experimental setups usually contain following components (Fig.1) [10]:
I.
INTRODUCTION
A. The Brain-Computer Interface Brain–Computer Interface (BCI) also called brain-machine interface or neural interface system is a kind of system that acquires and analyzes neural signals with the goal of creating a communication channel directly between the human brain and the computer or the robotic device [1]. Based on signal acquisition techniques employed for establishing the interface, the BCI systems can be broadly classified into three main types: noninvasive (using Electroencephalography, EEG signals) [1], partially-invasive (using Electrocorticography, ECoG signals) and invasive (using spikes and/or field potentials acquired by Cortical Multi-Electrode Array) [2]. B. Applications of BCI There are many different applications for the BCI. The main application fields are: medical (e.g., disease diagnosis and assistance to disabled people [3]), entertainment [4, 5] and multimedia (e.g., virtual reality and video games [6]), robotics [7-9] and military applications [2].
c 978-1-4673-5883-5/13/$31.00 2013 IEEE
Figure 1. BCI components
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electroencephalograph for the acquisition of the signals,
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biosignals amplifier
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software platform for real time signals analysis
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stimulation unit, usually visual stimulation matrix (for example: matrix contains letters of the alphabet as well as images associated to them, it permits word-writing and the elaboration of messages with the images.) EEG SIGNAL AND ITS ACQUISITION
II.
The BCI system measures the specific features of brain activity and translates them into device control signal [11, 12]. The system uses electroencephalogram (EEG) signals recorded during specific mental activity as input and provides control option by its input. The common structure of non-invasive Brain Computer Interface could be described as follows: The signals are obtained from the brain using non-invasive methods: for example by placing electrodes or other sensors on or near the persons scalp. Those electrodes or sensors detect the tiny, microvolt or femtotesla level electrical or magnetically activity at the surface of the scalp. These activities usually have a frequency ranging from little above the DC voltage up to 100 Hz with different frequency bands, namely - delta (δ) with a frequency range of 0-3Hz, theta (θ) ranges from 4-7 Hz, alpha (α) varies from 8-12 Hz, beta (β) is from 12-30 Hz, and gamma (γ) has a band of 34-100 Hz. These different signals have their own clinical implications in disease diagnosis [12]. TABLE I. Company. g.tec
TYPES OF EEG CAPS AND THEIR FEATURES Model
Product name g.EEGcap
No. of Electrodes 65
Electrode materials
Features
Au or Ag/AgCl electrodes
- easy access to skin for perfect skin preparation, - active and passive electrode can be used
g.tec
g.GAMM A-cap
74
Ag/AgCl sintered ring electrodes
-fast preparation and cleaning procedure which speeds up experiment, -single electrodes can be replaced easily, - fast and easy montageactive and passive electrode can be used
easycap
EC40
36
Ag/AgCl sintered ring electrodes
connect and detach many electrodes quick and error free to any EEG amplifier
Mind Media BV
NeXus EEG cap
21
the electrodes are already built into the cap
Once the signals are cleaned they will be processed by and classified to find out which kind of mental task the subject is performing, and then, they will be used by an appropriate algorithm for the development of certain application. As can be seen, the caps contain different number of standard electrode positions. From 21 for NeXus EEG cap type, where the electrodes are built into the cap, till 74 for g.GAMMAcap type. For EEG measurement with the use g.GAMMAcap type and EC40 cap type, Ag/AgCl electrodes are recommended by the manufacturers, g.tec and easycap respectively. This type of electrodes for DC derivations with EEG frequencies below 0.1 Hz performs better than gold electrodes. However, for g.EEGcap type Au electrode could also be used and the g.tec company fully recommend it. Both g.EEGcap and g.GAMMAcap type could be configured with active and passive electrodes. Passive electrodes consist only of the disk material and are connected with the electrode cable and a 1.5 mm medical connector to the biosignal amplifier. Active electrodes have a pre-amplifier with gain 1-10 inside the electrode which makes the electrode less sensitive to environmental noise such as power line interference and cable movements. Active electrodes have system connectors to supply the electronic components with power [10]. For g.EEGcap, g.GAMMAcap, and EC40 single electrode system is applied. In this kind of system, when an electrodes breaks down, it could be removed and replaced. However, the big disadvantage is that all electrodes must be connected separately each time. It is important to mention, that for g.GAMMAcap to shorten the time of experiment, it is possible to remove cap together with the electrodes for cleaning. In NeXus EEG cap type the electrodes are built into the cap and the system with integrated electrodes is applied. The main disadvantage is the inflexibility of the montage, and the whole cap must be removed if any electrode breaks down. For proposed test-bench model, the g.GAMMAcap will be the most suitable, because of: single electrodes application, larger number of standard electrode positions. III.
BCI HARDWARE
The g.USBamp USB amplifier (Fig.2.) allows recording multimodal biosignals.
Table I presents different types of EEG caps models with the short description of the most important features. The detection of such faint brain waves is difficult because of the noisy electrical and magnetic environment. This creates “artifacts” which are electrical signal arising, for example, from muscle activity or the eye blink of the subject, may produce an electrical and magnetic wave which is stronger than the subject’s brain waves [13, 14]. After signals collection, the signal is amplified and sampled. However the signal still contains the noise. That is why the signal pre-processing or signal cleaning is required.
Figure 2. g.USBamp – 24 bit biosignal acquisition device [15].
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The device allows investigating several body signals: brainactivity, heart-activity, muscle-activity, eye movement, respiration, galvanic skin response and other [15]. The g.USBamp can be combined with the g.EEGcap, g.GAMMAcap and a range of active and passive electrodes. The g.USBamp comes with 16 simultaneous sampled biosignal channels. It is USB 2.0 enabled, it can be simply connected to the USB socket of the PC and then used for data recording. IV.
BCI SOFTWARE PLATFORM
There are several BCI software platforms both commercial and publicly available (free and open source). Typical BCI system consists of three parts: data acquisition, signal processing and feedback or stimulus presentation [16]. Software developed for BCI research is influenced by several demands and requirements from the BCI research field, which include flexibility (due to rapidly advancing research, the software should be flexible), ease of use (as the software will be used by non-computer experts they should be user friendly), efficiency (careful selection of computing algorithms for less time laps), performance (ability to perform well in practical BCI environment), and robustness (not only perform well in test-bench, but also in practical situations). According to our assumptions in proposed test-bench we are going to use only free and open source solutions [17]. Keeping in mind these requirements many software have been developed in both commercial and open-source platforms. An overview of open source BCI software has been presented in [15]. The authors distinguished seven major platforms for BCI research (BCI2000, OpenViBE, TOBI, BCILAB, BCI++, xBCI, and BF++) and one platform for feedback and stimulus presentation (Pyff). Another young, yet popular platform is the MATLAB (http://www.mathworks.com/) based open-source software for BCI research and have been reviewed in [17] where two most prominent MATLAB based open-source tools for real-time data streaming in BCI research have been discussed (FieldTrip, and DataSuite). Considering our purpose and the pros-and-cons of each of these above mentioned software tools, the most interesting one is the OpenViBE platform for our test-bench. The following subsections contain brief descriptions of some of the most prominent and popular open-source tools used in BCI research. A. OpenViBE OpenViBE (Open Platform for Virtual Brain Environments) [18] is free, open source modular software platform for BCI research developed in the National Institute for Research in Computer Science and Control, INRIA. It is dedicated to designing, testing and using brain-computer interfaces. The OpenViBE is a software for real time processing of brain signals (real time neurosciences). This platform consists of a set of software modules devoted to: acquisition, pre-processing, processing and visualization of cerebral data. It is designed for different types of users: BCI researchers, clinicians, game and virtual reality developers. The software can comply with different data acquisition machines,
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and different types of signals, such as EEG or MEG. OpenViBE can be used by programmers, video game developers, signal-processing or robotics researchers, physicians, etc.). It can run on both Windows and Linux operating systems. B. Pyff The Pyff is a feedback framework for BCI Systems written in Python programming language [19], it is part of the Berlin Brain Computer Interface project (http://bbci.de/). This software is dedicated to rapid development of BCI feedback applications in Python (as easy as possible). The Pyff framework consists of four parts: (1) the feedback controller, (2) graphical user interface, (3) set of feedback paradigm and stimuli, and (4) collection of base classes [15]. Pyff is free and open source software (GNU General Public License). It can run on Windows, Linux and MacOS operating systems. C. PBbrain PBrain is a part of the NeuroImaging in Python suite NIPY [19]. It is a collection of applications for the analysis of EEG and medical image data. It consists of two applications (Fig.3.): (a) eegview – for an eeg signals visualization and analysis, and (b) loc3d - 3D image analysis application for localizing, identifying and labeling objects in image data. D. FieldTrip The FieldTrip toolbox [20] is an open-source software implemented in MATLAB to analyze EEG/MEG signals with a realtime data buffering scheme. This buffer allows the acquisition client to stream EEG data and at the same time any data present in the buffer can be retrieved and processed by other applications. Mainly written in C/C++, the buffer lets multiple clients to simultaneously read/write data. The C/C++ codes are compiled into MATLAB ‘mex’ files. This allows processing of small segments of incoming stream of EEG data under MATLAB while new acquired data is buffered. E. DataSuite DataSuite [21] is developed at the Swartz Center for Computational Neuroscience (SCCN), University of California – San Diego. It is designed as a distributed data acquisition, synchronization, online processing, and stimulus delivery system. It mainly has two components: DataRiver and MatRiver. The DataRiver is mainly a real-time data management and synchronization engine capable of synchronizing data from different clients and maintain realtime analysis. On the other hand, the MatRiver is a MATLAB toolbox for real-time data processing, buffering and visualization with emphasis on EEG analysis. It internally communicates with DataRiver and performs EEG preprocessing and classification. The common tasks performed by MatRiver include channel selection, re-referencing, frequency filtering, linear spatial filtering, noisy channel detection and compensation, and may be used for event-based classification or continuous visualization of derived EEG features such as
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alpha band power which at a later stage may be applied to the interfacing device.
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Testing and design control modules of neuroprosthetic devices. As a rapid prototyping system for student projects the Blender 3D modeling software will be used [25]. Blender is a versatile open source graphical program that allows modeling 3D objects, rendering them, preparing animations, movies and video games. Blender is equipped with physics engine (Blender Game Engine, BGE) and Python API (application programming interface) . BGE allows modeling of realistic behavior of virtual 3D objects and Python API allows to interface Blender with Python programming language.
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Developing and testing selected machine learning methods as well as probabilistic in application to signal processing in brain-computer interfaces [26]. VI.
CONCLUSION
The proposed test bench will be used for both BCI research and educational purposes. It is relatively simple and compounded of free, open source BCI software. It will allow performing research in applications of brain-computer interfaces in computer games, virtual reality systems, controlling robotic devices and in medicine (neural prosthetics). It could also be useful for creating and testing new solutions and ideas in the field of brain-computer interfaces. REFERENCES [1]
Figure 3. EEG Viewer PBrain [18].
V.
FUTURE APPLICATIONS AND EXPERIMENTS
The BCI systems based on EEG signals may be studied from two points of view: both engineering and neuroscience [23]. Therefore, the proposed test-bed will be used as development environment for application as well as research projects regarding non-invasive brain-machine interfaces. Regarding engineering projects it will be used for: •
Designing and testing software to generate visual stimuli on LCD or CRT screens. This will allow to test the interfaces from different points of view regarding complexity of BCI system (low, medium, high) [24].
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Research on stability and performance of BCI as a communication system. These are two major issues in designing brain-computer interfaces. BCI system is stable if it has low error rate, and its performance depends on speed of providing commands [23].
Regarding research projects, proposed test-bed will be used for:
Vialatte FB, Maurice M, Dauwels J, Cichocki A., Steady-state visually evoked potentials: focus on essential paradigms and future perspectives., Prog Neurobiol. 2010 Apr;90(4):418-38. doi: 10.1016/j.pneurobio.2009.11.005. Epub 2009 Dec 4. [2] B.J. Baars, N.M. Gage, Cognition, Brain, and Consciousness: Introduction to Cognitive Neuroscience, San Diego, USA: Academic Press, 2010. [3] I.S. Kotchetkov, B.Y. Hwang, G. Appelboom, C.P. Kellner, E.S. Connolly, “Brain-computer interfaces: military, neurosurgical, and ethical perspective,” Neurosurg. Focus, Vol. 28, No. 5, 2010, pp. e25. [4] C Neuper, G.R Müller, A Kübler, N Birbaumer, G Pfurtscheller, “Clinical application of an EEG-based brain–computer interface: a case study in a patient with severe motor impairment,” Clinical Neurophysiology, Vol. 114, No. 3, 2003, pp. 399-409. [5] Emotiv Systems, http://www.emotiv.com/ [6] NeuroSky, http://www.neurosky.com/ [7] A. Nijholt, “BCI for Games: A ‘State of the Art’ Survey,” Entertainment Computing - ICEC 2008, Lecture Notes in Computer Science, Vol. 5309, 2009, pp. 225-228, doi: 10.1007/978-3-540-89222-9_29. [8] M. Mahmud, A. Bertoldo, S. Vassanelli, “EEG Based Brain-Machine Interfacing: Navigation of Mobile Robotic Device,” in Mobile Robots Control Architectures, Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training, J. BĊdkowski, Ed. Rijeka, Croatia: InTech, 2011, pp. 129 – 144, ISBN: 978-953-307-842-7. [9] M. Mahmud, D. Hawellek, A. Bertoldo, “EEG based brain-machine interface for navigation of robotic device”, in Proc. 3rd IEEE/RASEMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob2010), Tokyo, Japan, 2010, pp. 168–172. [10] M. Mahmud, D. Hawellek, A. Valjamae, “A brain-machine interface based on EEG: extracted alpha waved applied to mobile robot,” in Proc 2009 ECSIS Symposium on Advanced Technologies for Enhanced Quality of Life (AT-EQUAL 2009), Iasi, Romania, 2009 pp. 28–31. [11] Reza Fazel-Rezai, Recent Advances in Brain-Computer Interface Systems, InTech: Rijeka, Croatia, ISBN: 978-953-307-175-6. 2011.
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[12] M. Senthilmurugan, M. Latha, N. Malmurugan “Classification in EEGBased Brain Computer Interfaces Using Inverse Model,” International Journal of Computer Theory and Engineering, Vol. 3, No.2, 2011, pp 274-276. [13] G. Pfurtscheller, C. Neuper, C. Guger, W. Harkam, H. Ramoser, A. Schl gl, B. Obermaier and M. Pregenzer “Current Trends in Graz Brain-Computer Interface (BCI) Research”, IEEE Transations on Rehabilitation Engineering, Vol. 8, No.2, 2000, pp. 216-219. [14] H. Poor, An Introduction to Signal Detection and Estimation. New York: Springer-Verlag, 1985. [15] E. Niedermeyer, F. H. Lopes da Silva, Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Philadelphia, USA: Lippincott Williams & Wilkins, 2005. [16] http://www.cortechsolutions.com/Applications/Small-animal-EEG/gUSBamp.aspx [17] Brunner C., et al.., “BCI Software Platforms,” in Towards Practical Brain-Computer Interfaces: Bridging the Gap from Research to RealWorld Applications, B.Z. Allison et al., Eds., Berlin Heidelberg: Springer, 2013, pp.303-331 , eISBN: 978-3-642-29746-5. [18] A. Delorme, C. Kothe, A. Vankov, N. Bigdely-Shamlo, R. Oostenveld, T.O. Zander, S. Makeig, “MATLAB-Based Tools for BCI Research,” in Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction, D.S. Tan & A. Nijholt, Eds. London: Springer, 2010, pp. 241-260, eISBN: 978-1-84996-272-8.
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[19] OpenViBE: An Open-Source Software Platform to Design, Test and Use Brain-Computer Interfaces in Real and Virtual Environments. INRIA: http://openvibe.inria.fr/ [20] Neuroimaging in Python, http://nipy.sourceforge.net/pbrain/ [21] R. Oostenveld, P. Fries, E. Maris, J.M. Schoffelen, “FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data,” Computational Intelligence and Neuroscience, Vol. 2011, Article ID:156869, 9 pages, 2011. doi:10.1155/2011/156869 [22] www.sccn.ucsd.edu/wiki/DataSuit [23] Vialatte F.-B., Maurice M., Dauwels J., Cichocki A., Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives, http://dx.doi.org/10.1016/j.bbr.2011.03.031 [24] Wu, Z., Lai, W., Xia, Y., Wu, D., Yao, D., Stimulator selection in SSVEP-based BCI. Med. Eng. Phys. 30, 1079–1088., 2008 [25] www.blender.org [26] Heung-Il Suk, Seong-Whan Lee, "A Novel Bayesian Framework for Discriminative Feature Extraction in Brain-Computer Interfaces," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 2, pp. 286-299, Feb. 2013, doi:10.1109/TPAMI.2012.69
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