Spectral subtraction denoising preprocessing block to improve P300-based brain-computer interfacing

BioMedical Engineering OnLine

Mohammed J Alhaddad¹  Mahmoud I Kamel¹  Meena M Makary²  Hani Hargas¹  Yasser M Kadah² 

[1] Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

[2] Biomedical Engineering Department, Cairo University, Giza 12613, Egypt

关键词: Signal denoising;    Wavelet shrinkage;    Spectral subtraction;    Brain-computer interface;   

Others  :  795009
DOI  :  10.1186/1475-925X-13-36

 received in 2013-09-10, accepted in 2014-03-28,  发布年份 2014

【 摘 要 】

Background

The signals acquired in brain-computer interface (BCI) experiments usually involve several complicated sampling, artifact and noise conditions. This mandated the use of several strategies as preprocessing to allow the extraction of meaningful components of the measured signals to be passed along to further processing steps. In spite of the success present preprocessing methods have to improve the reliability of BCI, there is still room for further improvement to boost the performance even more.

Methods

A new preprocessing method for denoising P300-based brain-computer interface data that allows better performance with lower number of channels and blocks is presented. The new denoising technique is based on a modified version of the spectral subtraction denoising and works on each temporal signal channel independently thus offering seamless integration with existing preprocessing and allowing low channel counts to be used.

Results

The new method is verified using experimental data and compared to the classification results of the same data without denoising and with denoising using present wavelet shrinkage based technique. Enhanced performance in different experiments as quantitatively assessed using classification block accuracy as well as bit rate estimates was confirmed.

Conclusion

The new preprocessing method based on spectral subtraction denoising offer superior performance to existing methods and has potential for practical utility as a new standard preprocessing block in BCI signal processing.

【 授权许可】

   
2014 Alhaddad et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Ramirez RR, Kopell BH, Butson CR, Hiner BC, Baillet S: Spectral signal space projection algorithm for frequency domain MEG and EEG denoising, whitening, and source imaging. NeuroImage 2011, 56:78-92.
• [2]de Cheveigné A, Simon JZ: Denoising based on spatial filtering. J Neurosci Methods 2008, 171:331-339.
• [3]Piresa G: Statistical spatial filtering for a P300-based BCI: tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis. J Neurosci Methods 2011, 195:270-281.
• [4]Vorobyov S, Cichocki A: Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis. Biol Cybern 2002, 86:293-303.
• [5]Akhtar MT, Mitsuhashi W, James CJ: Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data. Signal Process 2012, 92:401-416.
• [6]Geetha G, Geethalakshmi SN: Artifact removal from EEG using spatially constrained independent component analysis and wavelet denoising with Otsu’s thresholding technique. Procedia Eng 2012, 30:1064-1071.
• [7]Hammon PS, de Sa VR: Assessment of Preprocessing on Classifiers Used in the P300 Speller Paradigm. In Proceedings of the 28th IEEE EMBS Annual International Conference of IEEE ISBN: 1-4244-0032-5, Aug 30-Sept 3 2006. New York City, USA: IEEE Press; 2006:1319-1322.
• [8]Hammon PS, de Sa VR: Preprocessing and meta-classification for brain-computer interfaces. IEEE Trans Biomed Eng 2007, 54:518-525.
• [9]Romo Vázquez R, Vélez-Pérez R, Ranta R, Louis Dorr V, Maquin D, Maillard K: Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling. Biomed Signal Process Control 2012, 7:389-400.
• [10]Ahmadi M, Quian Quiroga R: Automatic denoising of single-trial evoked potentials. NeuroImage 2013, 66:672-680.
• [11]Donoho DL: Denoising by soft-thresholding. IEEE Trans Inf Theory 1995, 42:613-627.
• [12]Quian Quiroga R, Garcia H: Single-trial event-related potentials with wavelet denoising. Clin Neurophysiol 2003, 114:376-390.
• [13]Effern A, Lehnertz K, Grunwald T, Fernandez G, David P, Elger CE: Time adaptive denoising of single trial event-related potentials in the wavelet domain. Psychophysiology 2000, 37:859-865.
• [14]Gao J, Sultan H, Hu J, Tung WW: Denoising nonlinear time series by adaptive filtering and wavelet shrinkage: a comparison. IEEE Signal Process Lett 2010, 17:237-240.
• [15]Saavedra C, Bougrain L: Wavelet denoising for P300 single-trial detection. In Proceedings of the 5th French conference on computational neuroscience (Neurocomp’10). lyon, France: NeuroComp; 2010:227-231.
• [16]Hammad S, Corazzol M, Kamavuako EN, Jensen W: Wavelet denoising and ANN/SVM decoding of a self-paced forelimb movement based on multi-unit intra-cortical signals in rats. In Proceedings of 2012 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES). Langkawi, Malaysia: IEEE Press; 2012:990-994.
• [17]Sammaiah A, Narsimha B, Suresh E, Reddy M: On the performance of wavelet transform improving Eye blink detections for BCI. In Proceedings of 2011 International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT). Tamil Nadu, India: IEEE Press; 2011:800-804.
• [18]Estrada E, Nazeran H, Sierra G, Ebrahimi F, Setarehdan SK: Wavelet-based EEG denoising for automatic sleep stage classification. In Proceedings of 21st International Conference on Electrical Communications and Computers (CONIELECOMP). Puebla, Mexico: IEEE Press; 2011:295-298.
• [19]Tu Y, Huang G, Hung YS, Hu L, Hu Y, Zhang Z: Single-trial Detection of Visual Evoked Potentials by Common Spatial Patterns and Wavelet Filtering for Brain-computer Interface. In Proceedings of 35th Annual International Conference of the IEEE EMBS, ISBN: 978-1-4577-0216-7, 3 - 7 July 2013. Osaka, Japan: IEEE Press; 2013:2882-2885.
• [20]Kadah Y: Adaptive denoising of event-related functional magnetic resonance imaging data using spectrum subtraction. IEEE Trans Biomed Eng 2004, 51:1944-1953.
• [21]Hoffmann U, Vesin JM, Ebrahimi T, Diserens K: An efficient P300-based brain–computer interface for disabled subjects. J Neurosci Methods 2008, 167:115-125.
• [22]Thompson DE, Blain-Moraes S, Huggins JE: Performance assessment in brain-computer interface-based augmentative and alternative communication. BioMed Eng OnLine 2013, 12:43. BioMed Central Full Text