Key result
Stockwell transform and SVM achieve up to ~92% accuracy detecting P300 potentials via wireless EEG.
Why the study?
Detection of P300 evoked potentials using low-cost EEG devices with limited sensors requires effective signal processing techniques for accurate identification.
Answers are grounded in this paper and cite their sources.
The Stockwell transform combined with machine learning algorithms can effectively identify P300 evoked potentials using a low-cost, two-sensor EEG device.
The evoked potential is a neuronal activity that originates when a stimulus is presented. To achieve its detection, various techniques of brain signal processing can be used. One of the most studied evoked potentials is the P300 brain wave, which usually appears between 300 and 500 ms after the stimulus. Currently, the detection of P300 evoked potentials is of great importance due to its unique properties that allow the development of applications such as spellers, lie detectors, and diagnosis of psychiatric disorders. The present study was developed to demonstrate the usefulness of the Stockwell transform in the process of identifying P300 evoked potentials using a low-cost electroencephalography (EEG) device with only two brain sensors. The acquisition of signals was carried out using the Emotiv EPOC® device—a wireless EEG headset. In the feature extraction, the Stockwell transform was used to obtain time-frequency information. The algorithms of linear discriminant analysis and a support vector machine were used in the classification process. The experiments were carried out with 10 participants; men with an average age of 25.3 years in good health. In general, a good performance (75–92%) was obtained in identifying P300 evoked potentials.
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Pérez-Vidal et al. (2018) studied Healthy (n=10). Stockwell transform and Support Vector Machine (SVM) classification was evaluated on Classification accuracy of P300 evoked potentials. The Stockwell transform combined with a support vector machine classifier achieved 75% to 92% accuracy in detecting P300 evoked potentials using a low-cost wireless EEG device with only two sensors.
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