NPXLab Suite
1 DICII Dept., Tor Vergata UniversL.
Aston University, Birmingham, U Introduction: The NPXLab Suite physiological signals acquired durin also benefit from this framework as classification, statistics and metrics some of their features are here briefl Material, Methods and Results: T BCI2000, OpenVibe, BF++ [1] to na hardly interact, especially in real-tim mix BCI software modules from di software, breaking existing impleme off-line analysis tools, thus stand mechanisms that would permit to sh NPXLab goes in this direction, as it from different acquisition devices friendly user interface. It implemen without breaking the backward com different classifiers for BCI (SWLD domain and spatial filters, including advanced analyses on protocols like version was also adopted by several computation and statistical validati systems manufacturer (EBNeuro, F through a commercial plug-in mech functional model described in [2] an
Fig. 1 – ERP module view in which an av relative to target (orange) and non-target (b protocol of a patient are shown. Dark statistical significance (p<0.05) after sampl False Discovery Rate statistical correction for Discussion: The NPXLab Suite is processing of physiological signals Analysis, time domain-filtering, etc classification, etc...). In the BCI re process, review, remove artifacts and It can also be used to compare the devices (e.g. EEG, fMRI, ERP, NIR Born in 2002, this project is continu following years. It is available for do [1] Brunner C, et al. (2012). BCI Software P Bridging the Gap from Research to Real-Wor [2] Quitadamo LR, Marciani MG, Cardarilli G model: a UML implementation. Neuroinform [3] Quitadamo LR, Abbafati M, Cardarilli G different P300 based brain–computer interfac
e 2016: tools for BCI signal a
Bianchi1*, LR Quitadamo2, G. Saggio3
sity of Rome, Italy;2School of Life and Health Science UK; 3 Electronic Engineering Dept., Tor Vergata Univ
* E-mail: luigi.bianchi@uniroma2.it
is a collection of easy to use free software too ng either clinical or experimental protocols. Brain-Com
several facilities are provided, such as ICA, CSP, ER s computation. The main guidelines that inspired the ly outlined.
There are several frameworks aimed at handling BC ame few, but because they do not share a common fun me: today it is really difficult to have widely accepted
fferent frameworks as this would require to rewrite r entations. However, a different approach could be to tr dardizing just the “static” functional model and no hare information across modules in real-time.
t allows to analyze different bio-signals (e.g. EEG, ER and vendors (it supports more than 15 different file nts a native file format (NPX, based on XML) which mpatibility so that it can be extended in a painless wa DA, SVM, BLDA, Neural Networks, SRLDA, RLDA
g ICA (Fig. 1), Laplacian and CSP (Fig. 2), and can e P300, N400, Steady State EP and all kinds of ERPs
laboratories in the EU Decoder Project for performing ion of the analysis results from NIRS, fMRI and E Florence, Italy) has also integrated the NPXLab S hanism. Completely written in C++ programming lang nd performs faster than many similar and expensive co
veraged ICA component blue) stimuli in a P300 pink bubbles indicate le by sample t-test and r multiple comparisons.
Fig. 2 – A partial screenshot from the All the supported file formats could conversion to native NPX format. Th performed with the File Converter softw a features rich and easy to use collection of tool s. It allows to quickly perform classical analyses (e.
c...) as well as more advanced ones (e.g. ICA, Com esearch field it has been successfully used in sever d classify signals from various systems and to comput performances of BCI systems from different protoc RS, etc.) in a simple way as it is based on the model des uously improved, updated and extended, and it will b ownloading at http://www.brainterface.com.
Platforms. In: Allison, B.Z., Dunne, S., Leeb, R., Millan, J., and Nij rld Applications. BERLIN: Springer-Verlag
GC, Bianchi L. (2008). Describing different Brain Computer Interfa matics. vol. 6:2, pp. 81-96.
GC, Mattia D, Cincotti F, Babiloni F, BianchiL L. (2012). Evalua ces by means of the efficiency metric. J Neurosci Methods, vol. 203
analysis
es, Aston Brain Centre, versity of Rome, Italy
ls aimed at analyzing mputer Interfaces could RP and spectral analysis, eir implementation and
CIs protocols, such as nctional model they can d standards that allow to relevant portions of the ry to share at least some ot also the “dynamic”
RP, MEG, NIRS, etc...), e formats) with a very can be easily extended ay. It also comes with 7 A, FLDA), several time n perform classical and s. A previously released g ERP analysis, metrics ERPs. An Italian EEG Suite within its system guage it implements the
mmercial products.
common spatial pattern tool.
take advantage of it after his operation can be easily ware facility.
ls for the analysis and .g. Averaging, Spectral mmon Spatial Patterns, ral laboratories to pre- te various metrics.
cols [3] and acquisition scribed in [2].
be also supported in the
jholt. Toward Practical BCIs:
ace Systems through a unique ation of the performances of , p. 361-368.
DOI: 10.3217/978-3-85125-467-9-152 Proceedings of the 6th International Brain-Computer Interface Meeting, organized by the BCI Society
Published by Verlag der TU Graz, Graz University of Technology, sponsored by g.tec medical engineering GmbH 152
