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Single-trial P300 classification using PCA with LDA and neural networks




Sharma, Nand, author
Anderson, Charles, advisor
Kirby, Michael, advisor
Peterson, Chris, committee member

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A brain-computer interface (BCI) is a device that uses brain signals to provide a non-muscular communication channel for motor-impaired patients. It is especially targeted at patients with 'locked-in' syndrome, a condition where the patient is awake and fully aware but cannot communicate with the outside world due to complete paralysis. The P300 event-related potential (ERP), evoked in scalp-recorded electroencephalography (EEG) by external stimuli, has proven to be a reliable response for controlling a BCI. The P300 component of an event related potential is thus widely used in brain-computer interfaces to translate the subjects' intent by mere thoughts into commands to control artificial devices. The main challenge in the classification of P300 trials in electroencephalographic (EEG) data is the low signal-to-noise ratio (SNR) of the P300 response. To overcome the low SNR of individual trials, it is common practice to average together many consecutive trials, which effectively diminishes the random noise. Unfortunately, when more repeated trials are required for applications such as the P300 speller, the communication rate is greatly reduced. This has resulted in a need for better methods to improve single-trial classification accuracy of P300 response. In this work, we use Principal Component Analysis (PCA) as a preprocessing method and use Linear Discriminant Analysis (LDA)and neural networks for classification. The results show that a combination of PCA with these methods provided as high as 13% accuracy gain while using only 3 to 4 principal components. So, PCA feature selection not only increased the classification accuracy but also reduced the execution time of the algorithms by the resulting dimensionality reduction. It was also observed that when treating each data sample from each EEG channel as a separate data sample, PCA successfully separates out the variance across channels.


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