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Development of a fast and efficient algorithm for P300 event related potential detection
Franz, Elliott
Franz, Elliott
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Date
2014
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Electrical Engineering
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https://doi.org/10.34944/dspace/3215
Abstract
Electroencephalography (EEG) is an over one hundred year old technique which refers to the recording of electrical potentials in the brain. The most commonly used type of EEG is surface EEG, where electrical potentials from the brain are recorded from electrodes on the surface of the scalp. Recently, a number of consumer grade wireless EEG headsets have been developed. These headsets allow users to access recorded electrical potentials from the scalp. One application of this technology has been 'brain training,' in which users play video games designed to promote activity in certain regions of the brain in order to improve concentration and problem solving ability. Another application of the wireless headsets is for use in Brain-Computer Interfaces (BCI). The goal of BCI research is to provide an individual (e.g., an individual with paralysis or another impairment which limits functionality) with 'mind-control' of a computer. EEG recorded from the scalp has been an area of interest in the BCI community for some time. One type of BCI which can use surface EEG is the P300 Event Related Potential (ERP) BCI. This BCI employs a 6x6 matrix of alphanumeric characters to spell words based solely on input from the brain. The P300 ERP signal is elicited from the user when rows and columns of the matrix containing the target character are flashed. The ERPs which are elicited to spell words in this type of setup are notoriously difficult to detect due to the large amount of noise in the signal. The goal of this research is to optimize the detection of P300 potentials using the EPOC from Emotiv Systems, a consumer grade wireless EEG headset. This work compares the Emotiv EPOC directly with a high grade EEG collection system (the Neurodata 12 Acquisition System from Grass Technologies) by recording signals from spelling sessions in parallel. This research also presents a novel algorithm for optimizing P300 spelling speed to improve the throughput of a P300 based BCI speller. Increasing speed is an important concern for any future mobile application of the BCI technology (e.g., a tablet), because battery life and processor capabilities are limited. Increasing speed is synonymous with decreasing computational complexity, decreasing processor load, and increasing battery life.
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