Single-trial P300 Classification using PCA with LDA, QDA and Neural Networks

Various neurological diseases can disrupt the neuromuscular channels through which the brain communicates with the external world. In certain cases like hemorrhage in the anterior brain stem or degenerative neuromuscular diseases like amyotrophic lateral scleriosis (ALS), the patients suffer from a total motor paralysis [5]. This results in a condition known aslocked-in syndrome, wherein the patient is awake and fully aware but cannot communicate with the outside world due to complete paralysis. For such "locked-in" patients, there is a need for an assistive technology that needs no muscular activity whatsoever. A brain-computer interface (BCI) is a device that uses brain signals to provide a direct, non-muscular communication channel between brain and the outside world [32, 31, 29]. The idea underlying BCIs is to measure electric, magnetic, or other physical manifestations of the brain activity and to translate these into commands for a computer or other devices [21, 15]. For patients with locked-in syndrome,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 [9]. In this study we present comparison of some classification methods to classify an EEG signal based on the presence of P300 component.