![]() This can be done by many methods ranging from simple averaging of consecutive epochs to transform domain based filtering techniques ranging from basic bandpass filtering to different variants of the wavelet shrinkage method. On the other hand, temporal domain techniques attempt to find similarities within the time domain of a single channel signal that can be used to identify and suppress the noise components in that signal. Such methods range from simple local spatial averaging to sophisticated variants of blind source separation methods such as independent component analysis. In the spatial domain techniques, the data from multiple spatially-distinct channels are utilized to identify the true signal projected onto all channels from the noise that is generally assumed to be independent among such channels. Several research articles addressed the problem of achieving higher quality of EEG signals for BCI applications and otherwise with aim to improve the signal-to-noise ratio (SNR).Two broad categories can be immediately recognized namely, spatial domain techniques and temporal domain techniques. Approaches to improve quality of information from EEG-based systems through noise/artifact removal as well as more sophisticated analysis techniques would therefore allow this low cost, mobile technology to achieve better practical utility. So, there is a clear trade-off between the quality of signals collected on one side and the mobility of the subject and the cost of the system on the other side. The complexity of such systems also range from a simple, relatively inexpensive electrode cap worn by the subject and attached to a relatively small processing unit that provide very noisy signals while allowing subject mobility (as with EEG) to large expensive high field fMRI systems that allow excellent signal-to-noise ratio to be obtained while restricting the slightest subject motion during data acquisition. 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 3 voxel within the subject’s brain (as with fMRI). 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). ![]() Such means can be the only way of communication with patients of such disease conditions as muscular dystrophy (MS) and therefore its development and enhancement have been the focus of many research groups in the past decade. Such information can be used to perform actions controlled by the subject and hence provide an additional means of communication beside normal communication channels present in normal subjects. 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.īrain computer interfacing (BCI) is an important tool that allows direct reading of information from the subject’s brain activity by a computer. Enhanced performance in different experiments as quantitatively assessed using classification block accuracy as well as bit rate estimates was confirmed. 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. ![]() 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. ![]() ![]() MethodsĪ new preprocessing method for denoising P300-based brain-computer interface data that allows better performance with lower number of channels and blocks is presented. 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. 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. The signals acquired in brain-computer interface (BCI) experiments usually involve several complicated sampling, artifact and noise conditions. ![]()
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