Department Publications

The P300 wave is an Event Related Potential that is elicited in the brain when a subject is presented with a familiar stimulus. The Guilty Knowledge Test is used to determine if certain information is stored in the brain by detecting the P300 wave. In this paper, an improved Guilty Knowledge Test has been conducted on 14 subjects. Although a variety of feature extraction and classification algorithms for detecting the P300 have been used thus far, this paper introduces the use of deep learning techniques for the Guilty Knowledge Test. Two deep learning techniques; Deep Belief Networks and Stacked Autoencoders, have been compared. The input to these classifiers was the filtered EEG signal without any feature extraction. A mean accuracy of 86.9% was achieved for the Deep Belief Network and 86.01% for the Stacked Autoencoder on our dataset, outperforming the conventional Support Vector Machine.

The P300 Speller is a Brain Computer Interface that enables communication using the EEG signal. The P300 wave is an Event Related Potential that occurs as a response to a familiar stimulus. This system can be used to aid persons who are unable to communicate via conventional methods. In this paper, the P300 Speller has been modified to allow communication in three languages: English, Sinhala and Tamil. Classification of the P300 waveform was done using an ensemble of Support Vector Machines. A mean letter classification accuracy of 83.76% and a mean P300 classification accuracy of 90.68% was achieved on our dataset.