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An event-related potential (ERP) is a measure of brain response to a specific sensory, cognitive, or motor event. One common ERP technique used in cognition research is the oddball paradigm where the brain’s response to common and uncommon stimuli is compared. The neurologic response to the oddball paradigm produces a P300 ERP which is one of the major visual/auditory sensory ERP components. The purpose of this study to classify ERP responses to common and uncommon tones by extracting the P300 feature from ERP epochs and identify the accurate shape of the P300 wave. For recording ERP data, and OpenBCI system is used. P300 features are extracted using EEGlab which is a mathematical tool of MATLAB. Finally, various types of machine learning models are used for identifying the accurate shape of a P300 wave and then classifying common and uncommon auditory tones. For stimuli classification, all of the algorithms evaluated performed efficiently and built a consistent model with 93.75% to 99.1% evaluation accuracy. Also, for P300 shape detection, NN model showed the best performance with 94.95% accuracy. These findings have the potential to add useful machine learning-based methods to the clinical application of ERPs.

Classification of Common and Uncommon Tones by P300 Feature Extraction and Identification of Accurate P300 Wave by Machine Learning Algorithms