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

Mohammed J Alhaddad,Meena M Makary,Mahmoud I Kamel,Yasser M Kadah,Hani Hargas

doi:10.1186/1475-925x-13-36

Abstract

BackgroundThe 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.MethodsA 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.ResultsThe 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.ConclusionThe 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.

Highlights

Brain computer interfacing (BCI) is an important tool that allows direct reading of information from the subject’s brain activity by a computer
The methodological approach that will be followed in this work is to adopt spectral subtraction based signal denoising, which is an effective speech signal denoising method that was previously applied to functional magnetic resonance imaging (fMRI) signal denoising [20]
The classification results of using the new denoising method are shown in Figures 7 and 8 where each figure consists of the results with bandpass denoising, results with spectral subtraction denoising, and results with wavelet shrinkage denoising

Summary

Introduction

Brain computer interfacing (BCI) is an important tool that allows direct reading of information from the subject’s brain activity by a computer Such information can be used to perform actions controlled by the subject and provide an additional means of communication beside normal communication channels present in normal subjects. The brain activity at different locations can be measured using different methods that include electroencephalography (EEG), magnetoencephalography (MEG), and some functional imaging modalities such as functional magnetic resonance imaging (fMRI) These techniques offer brain activity signal time courses that come from a particular location in the brain with the resolution of such spatial localization ranging from a few signals for the whole brain (as with EEG) to signal for each 1 mm voxel within the subject’s brain (as with fMRI). 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

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Conclusion