نوع مقاله : مقاله کامل پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی پزشکی، گروه بیوالکتریک، دانشکده مهندسی برق و کامپیوتر، دانشگاه تربیت مدرس

2 دانشیار، گروه بیوالکتریک، دانشکده مهندسی برق و کامپیوتر، دانشگاه تربیت مدرس

10.22041/ijbme.2012.13112

چکیده

سیستم‌های BCIکاربرفرما در مقایسه با سیستمهای BCIسنکرون، ارتباط طبیعی‌تر کاربر را با فضای خارج امکان‌پذیر می‌کنند. آشکارسازی بازه‌های وقوع حرکت در سیگنال پیوسته EEGمسأله‌ای کلیدی در طراحی سیستم‌های BCI  کاربرفرما مبتنی بر حرکت است. در این مقاله با استفاده از ویژگی بعد فرکتالی در باندفرکانسی 6 تا 36 هرتز و طراحی طبقه‌بند مبتنی بر نمایش تنک سیگنال، پدیده نورولوژیک همزمانی وابسته به رخداد (ERS)- که بلافاصله پس از وقوع حرکت پا در سیگنال EEGاتفاق می‌افتد- با دقت قابل قبولی از سیگنال پس‌زمینه تشخیص داده شد. روش پیشنهادی این مقاله، بر سیگنال EEGتک کانال ثبت شده از 7 کاربر حین انجام حرکت پا اعمال شد و متوسط=90%   TPRavr  و  FPRavr=5%برای همه افراد بدست آمد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Foot movement onset detection in self-paced BCIs using sparse representation based classifier

نویسندگان [English]

  • Rahele Mohammadi 1
  • Ali Mahloojifar 2

1 PhD student of Biomedical Engineering, Electrical and Computer Eng. College, Tarbiat Modares University

2 Associate Professor, Biomedical Engineering department, Electrical and Computer Eng. College, Tarbiat Modares University

چکیده [English]

Self-paced BCI systems are more natural for real-life applications since these systems allow the user to control the system when desired. Detection of event periods in continuous EEG signal is one of the most important challenges in designing self-paced BCIs. In this paper, the Event related synchronization (ERS) is extracted from idle EEG signal using fractal dimensions in frequency range from 6 to 36 Hz and sparse representation based classifier. Our proposed method applied on EEG signal recorded during executing foot movement in 7 subjects. The average true positive rate and false positive rate equal to 90% and 5% were achieved.

کلیدواژه‌ها [English]

  • Self-paced Brain Computer Interface
  • Electroencephalogram signal
  • Sparse signal representation
[1] Wolpaw J.R., Birbaumer N., McFarland D.J., Pfurtscheller G., Vaughan T.M., Brain computer interfaces for communication and control; Clin. Neurophysiol., 2002; 113: 767–791.
[2] Muller-Putz J.R., Scherer R., Pfurtscheller G., Game-like training to learn single switch operated neuroprosthetic control;  Int. Conf. Adv. Comput. Entertainment Technol. Workshop. BrainPlay’07: playing with your brain (brain–computer interfaces and games), 2007: 49–51.
[3] Thorpe J., Oorchot P., Somayaji A., Pass-thoughts: authenticating with our minds; Proc new Secur paradigms workshop, 2005.
[4] Scherer R., Schlogl A., Lee F., Bischof H., Jansa J., Pfurtscheller G., The self-paced Graz brain–computer interface: methods and applications; J. Comput. Intell. Neurosci., 2007; 79825.
[5] Townsend G., Graimann B., Pfurtscheller G., Continuous EEG Classification During Motor Imagery—Simulation of an Asynchronous BCI; IEEE Trans. Neural, Rehab., 2006; 12: 258-265.
 [6] Mason S.G., Birch G.E., A brain-controlled switch for asynchronous control applications; IEEE Trans. Biomed. Eng., 2000; 47: 1297–1307.
[7] Fatourechi M., Ward R.K., Birch G.E., A self-paced brain–computer interface system with a low false positive rate; J. Neural Eng., 2008; 5: 9-23.
[8] Bashashati A., Mason S., Ward R.K., Birch G.E.,   An improved asynchronous brain interface: making use of the temporal history of the LF-ASD feature vectors; J. Neural. Eng., 2006; 3: 87-94.
[9] Pfurtscheller G., Lopes da Silva F.H., Event-related EEG/MEG synchronization and desynchronization: basic principles; Clin Neurophysiol, 1999; 110: 1842-57.
[10] Leeb R., Friedman D., Müller-Putz G.R., Scherer R., Slater M., Pfurtscheller G., Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: A case study with a Tetraplegic Computational Intelligence and Neuroscience special issue: Brain-Computer Interfaces; Towards Practical Implementations and Potential Applications, 2007: 1-8.
[11] Hasan B.A.S, Gan J.Q., Unsupervised movement onset detection from EEG recorded during self-paced real hand movement; Med. Biol. Eng. Comput., 2010; 48: 245-53.
[12] Wright J., Yang A.Y.,  Ganesh A., Sastry S.S., Ma Y., Robust face recognition via sparse representation; IEEE Trans. Pattern Anal. Mach. Intell., 2009; 31: 210–27.
[13] Chen S., Donoho D., Saunders M., Atomic decomposition by basis pursuit; SIAM Rev., 2001; 43: 129–59.
[14] Gemmeke J.F., Virtanen T., Hurmalainen A., Exemplar-based sparse representations for noise robustautomatic speech recognition; IEEE Trans. Audio Speech Lang. Process., 2011; 19: 2067–80.
[15] Li Y., Guan C., Qin J., Enhancing feature extraction with sparse component analysis for brain–computer interface; Proc. 27th Annual Int. Conf. of the Engineering in Medicine and Biology Society (IEEE-EMBS 2005),  2005: 5335–5338.
[16] Arvaneh M., Guan C., Ang K.K., Quek H.C., Spatially sparsed common spatial pattern to improve BCI performance; Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 2011), 2011: 2412–2415.
[17] Yu H., Lu H., Ouyang T., Liu H., Lu B.L., Vigilance detection based on sparse representation of EEG; Proc. 32nd  Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC 2010),  2010: 2439–2442.
 [18] Solis-Escalante T., Muller-Putz G.R., Pfurtscheller G., Overt foot movement detection in one single Laplacian EEG derivation; J. Neurosci. Methods, 2008; 175: 148-153.
[19] Mohammadi R., Mahlooji A., Coyle D., A Combination of Pre- and Postprocessing Techniques to Enhance Self-Paced BCIs; Advances in humn-computer interaction, 2012;  Article ID 185320.
[20] Schlogl A., Brunner C., Scherer R., Glatz A., BioSig: an open-source software library for BCI research; In Towards brain–computer interfacing, 2007; 20: MIT Press. p. 347–58.
[21] Graimann B., Huggins J.E., Levine S.P.,  Pfurtscheller G., Visualization of significant ERD/ERS patterns in multichannel EEG and ECoG data; Clin. Neurophysiol., 2002; 113(1): 43–47.
[22] Sadeghian E.B., Moradi M.H., fractal dimension for detection of ERD/ERS patterns in asynchronous brain computer interface; The 2th Int. Conf. Bioinfo. Biomed. Eng., May 16-18, 2008.
[23] Shin Y., Lee S., Lee J., Lee H.N., Sparse representation-based classification scheme for motor imagery-based brain-computer interface systems; J Neural Eng., 2012;  no.9;056002.
[24] Donoho D., Stodden V., Tsaig Y., SparseLab: http://sparselab.stanford.edu/.
[25] Townsend G., Graimann B., Pfurtscheller G., Continuous EEG Classification During Motor Imagery—Simulation of an Asynchronous BCI; IEEE Trans. Neural, Rehab., 2006; 12: 258-265.