Singular value decomposition based feature classification for single trial brain-computer interface design

The performance of any brain-computer interface (BCI) highly depends on being artifact free. In this study, we propose a mathematical modelling approach to design an efficient non-invasive BCI based on P300 component found in single trial visual evoked potential (VEP) signals. Since the brain processes multiple functions simultaneously the extracted VEP results are in a complex pattern. Further, the characteristics of the P300 component are difficult to be determined a priori especially when the signals are analysed on single trial basis. However, the data used by BCI systems have high dimensionality due to the recording from multiple electrode locations and this high dimensionality could be exploited for reducing the effects from artifacts, using specific pre-processing techniques. In this research study, we propose a mathematical framework for noise reduction and a two-step classification using dynamic methods that results in an enhanced BCI design. The application of singular value decomposition (SVD) to the discrete single trial VEP data facilitates reduction of noise and operational data dimension. Frequency specific filtering further reduces noise and a computationally simple distance based measure with novel method of using two thresholds was utilised for classification. The experimental results give a very low false accept rate (FAR) and false reject rate (FRR) and a near negligible equal error rate (EER) of 2.91%. The high accuracy obtained validates our proposed single trial based approach.