Abstract
Here we present a novel approach to detect P300 wave in single trial Visual Event Related Potential (VERP) signals using improved principal component analysis to enable a faster brain-computer interface (BCI) design. In the process, the principal components (PCs) are selected using novel methods, namely spectral power ratio (SPR) and sandwich spectral power ratio (SSPR). We set out to assess the improved performances of our proposed methods, SPR and SSPR over standard PC selection methods like Kaiser and residual power for speller BCI design. Concluding, the P300 parameters extracted through our proposed SPR and SSPR methods showed improved detection of target characters in the speller BCI.
Highlights
Paralysed people can utilise Brain-Computer Interface (BCI) designs to communicate with their external environments as it does not require any conventional muscle control
The selection of Principal Components (PCs) is an important issue in Principal Component Analysis (PCA) and in this study, we address this issue by proposing novel methods based on the spectral content of the principal components (PCs), namely the Spectral Power Ratio method (SPR) and Sandwich Spectral Power Ratio (SSPR)
Sandwich spectral power ratio: The sandwich spectral power ratio (SSPR) method is similar to SPR method except that we introduced an additional low pass filter in between two levels of PCASPR applications
Summary
Paralysed people can utilise Brain-Computer Interface (BCI) designs to communicate with their external environments as it does not require any conventional muscle control. The advantage of using the VERP based BCI is that it is easier for the users to adopt and does not require any significant prior training. There are many improvements taking place in the constituent modules of this BCI such as in the stimulus paradigm, recording (hardware) protocols, signal processing algorithms and output formats for a wide variety of applications with several objectives like enhanced accuracy, quicker response and simpler usability. It is hoped that these improvements in BCI will move the usability of the systems from laboratory to real world and impart confidence to locked-in patients to communicate in simple words
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