Comparison of Pre-Processing and Classification Techniques for Single-Trial and Multi-Trial P300-Based Brain Computer Interfaces

Abstract

Abstract:The P300 component of Event Related Brain Potentials (ERP) is commonly used in Brain Computer Interfaces (BCI) to translate the intentions of an individual into commands for external devices. The P300 response, however, resides in a signal environment of high background noise. Consequently, the main problem in developing a P300-based BCI lies in identifying the P300 response in the presence of this noise. Traditionally, attenuating the background activity of P300 data is done by averaging multiple trials of recorded signals. This method, though effective, suffers two drawbacks. First, collecting multiple trials of data is time consuming and delays the BCI response. Second, latency distortions may appear in the averaged result due to variable time-locking of the P300 in the individual trials. Problem statement: The use of single-trial P300 data overcomes both these shortcomings. However, single-trial data must be properly denoised to allow for reliable BCI operation. Single-trial P300-based BCIs have been implemented using a variety of signal processing techniques and classification methodologies. However, comparing the accuracies of these systems to other multi-trial systems is likely to include the comparison of more than just the trial format (single-trial/multi-trial) as the data quality and recording circumstances are likely to be dissimilar. Approach: This issue was directly addressed by comparing the performance comparison of three different preprocessing agents and three classification methodologies on the same data set over both the single-trial and multi-trial settings. The P300 data set of BCI Competition II was used to facilitate this comparison. Results: The LDA classifier exhibited the best performance in classifying unseen P300 spatiotemporal features in both the single-trial (74.19%) and multi-trial format (100%). It is also very efficient in terms of computational and memory requirements. Conclusion: This study can serve as a general guide for practitioners developing single-trial and multi-trial P300-based BCI systems, particularly for selecting appropriate pre-processing agents and classification methodologies for inclusion. The possibilities for future study include the investigation of double-trial and triple-trial P300 system based on the LDA classifier. The time savings of such approaches will still be significant. It is very likely that such systems would benefit from accuracies higher than the one obtained in this study for single-trial LDA (74.19%).

Binary DE-Based Channel Selection and Weighted Ensemble of SVM Classification for Novel Brain–Computer Interface Using Devanagari Script-Based P300 Speller Paradigm

“…Different types of signal processing methods (Cecotti, 2015;Jung et al, 2001;Xu et al, 2004), dimension reduction methods (Andrews, Palaniappan, Teoh, & chu Kiong, 2008;Li, Sankar, Arbel, & Donchin, 2009;Ming et al, 2010;Piccione, et al, 2006;Syan & Harnarinesingh, 2010) and error correction codes (Bießmann, 2007;Hill, Farquhar, Martens, Bießmann, & Schölkopf, 2009) have also been developed to increase the ITR.…”

“…Various review and comparison articles have been published to cover different aspects of P300 spellers and compare the performance of different classifier strategies on P300 based BCI (Akcakaya, et al, 2014;Fazel-Rezai, et al, 2012;Hwang, Kim, Choi, & Im, 2013;Lotte, et al, 2007;Mirghasemi, Fazel-Rezai, & Shamsollahi, 2006;Syan & Harnarinesingh, 2010). Different types of signal processing methods (Cecotti, 2015;Jung et al, 2001;Xu et al, 2004), dimension reduction methods (Andrews, Palaniappan, Teoh, & chu Kiong, 2008;Li, Sankar, Arbel, & Donchin, 2009;Ming et al, 2010;Piccione, et al, 2006;Syan & Harnarinesingh, 2010) and error correction codes (Bießmann, 2007;Hill, Farquhar, Martens, Bießmann, & Schölkopf, 2009) have also been developed to increase the ITR.…”

Suitability of Data Intensive Application in a Bandwidth Constrained Global Grid Network

“…Although great advances have been made in the field of Grid computing, QoS remains a major issue as Grid systems cannot be scaled proportionately as expected by the user. A computational grid (Syan and Harnarinesingh, 2010) works in a highly dynamic environment with the resources including bandwidth and processor time availability changing continuously and thus not guaranteeing QoS. Grid applications in a global network also needs to compete for shared resources which again leads to degradation of QOS (Wang et al, 2009).…”

Improved Application of Paraconsistent Artificial Neural Networks in Diagnosis of Alzheimer's Disease

“…Stimuli are transmitted from the processing element to another one via synapses or interconnections, which can be excitatory or inhibitory. Neural networks have an advantage over conventional programming because they lie in their ability to solve problems that do not have an algorithmic solution or where the available solution is too complex to be found (Syan and Harnarinesingh, 2010). Thus, neural networks are well suited to tackle problems that people are good at solving, such as prediction and pattern recognition.…”

“…Thus, neural networks are well suited to tackle problems that people are good at solving, such as prediction and pattern recognition. Moreover, ANNs have been applied within the medical domain for clinical diagnosis, imaging analysis and interpretation, signal analysis and interpretation (Karait et al, 2009;Syan and Harnarinesingh, 2010), and drug development. Therefore, ANN constitutes an interesting tool for EEG qualitative analysis.…”