Abstract
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.
Highlights
In recent times, technological progress has allowed brain-computer interfaces (BCI) to be used more frequently
Technological progress has allowed brain-computer interfaces (BCI) to be used more frequently. Their main purpose is to control devices by means of brain signals. This is of great relevance in the area of rehabilitation because it provides a different method of communication for those who have a motor disability, such as amyotrophic lateral sclerosis, Becker muscular dystrophy, Duchenne muscular dystrophy, Guillain-Barré syndrome, quadriplegia, brain injury, spinal cord injury, and so forth
This study shows that the Stockwell transform is a useful algorithm that allows the detection of P300 evoked potentials induced by visual stimuli
Summary
Technological progress has allowed brain-computer interfaces (BCI) to be used more frequently. Their main purpose is to control devices by means of brain signals. This is of great relevance in the area of rehabilitation because it provides a different method of communication for those who have a motor disability, such as amyotrophic lateral sclerosis, Becker muscular dystrophy, Duchenne muscular dystrophy, Guillain-Barré syndrome, quadriplegia, brain injury, spinal cord injury, and so forth. EEG signals are electrical potentials caused by a set of neurons when a brain process is performed. They are obtained using an electroencephalograph, directly from the scalp. Several types of EEG signals have been classified, such as the sensorimotor rhythm (SMR) [9], slow cortical potential (SCP) [10], event-related potential (ERP) [11], and steady-state visual evoked potential (SSVEP) [12], among others
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