Abstract
The P300 paradigm is one of the most promising techniques for its robustness and reliability in Brain-Computer Interface (BCI) applications, but it is not exempt from shortcomings. The present work studied single-trial classification effectiveness in distinguishing between target and non-target responses considering two conditions of visual stimulation and the variation of the number of symbols presented to the user in a single-option visual frame. In addition, we also investigated the relationship between the classification results of target and non-target events when training and testing the machine-learning model with datasets containing different stimulation conditions and different number of symbols. To this end, we designed a P300 experimental protocol considering, as conditions of stimulation: the color highlighting or the superimposing of a cartoon face and from four to nine options. These experiments were carried out with 19 healthy subjects in 3 sessions. The results showed that the Event-Related Potentials (ERP) responses and the classification accuracy are stronger with cartoon faces as stimulus type and similar irrespective of the amount of options. In addition, the classification performance is reduced when using datasets with different type of stimulus, but it is similar when using datasets with different the number of symbols. These results have a special connotation for the design of systems, in which it is intended to elicit higher levels of evoked potentials and, at the same time, optimize training time.
Highlights
Brain-Computer Interfaces (BCI) were first proposed almost fifty years ago as an alternative output pathway to allow people communication and control of external devices without performing muscular activity [1]
This subsection presents the results of the data analysis procedure which aimed, first, to study the effect of the stimulation conditions and of the number of symbols on the classification accuracy and on the P300 responses, and second, to investigate the effect on the classification accuracy of training and testing the machine-learning model with datasets containing different stimulation conditions and different number of symbols
Some aspects of the visual stimuli in P300-based BCIs may affect the characteristics of the Event-Related Potentials (ERP); they play a critical role in the BCI performance
Summary
Brain-Computer Interfaces (BCI) were first proposed almost fifty years ago as an alternative output pathway to allow people communication and control of external devices without performing muscular activity [1]. Since this technology has evolved considerably and nowadays the main applications are found in clinical environments. This technology has evolved considerably and nowadays the main applications are found in clinical environments Areas, such as neuro-rehabilitation for patients with neurodegenerative diseases [2,3,4,5,6], as well as assistive technologies for people with motor impairments [7,8,9,10], have had the widest presence. Scientific efforts to successfully incorporate BCIs into daily life activities by end users are mainly focused towards improvements in performance [13], either by reduction of calibration time [14] or development of novel stimuli presentation strategies [15], among other aspects
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