Abstract
Event-related potentials (ERPs) are important neurophysiological markers widely used in scientific, medical and engineering contexts. Proper ERP detection contributes to widening the scope of use and, in general, improving functionality. The morphology and latency of ERPs are variable among subject sessions, which complicates their detection. Although variability is an intrinsic feature of neuronal activity, it can be addressed with novel views on ERP detection techniques. In this paper, we propose an agile method for characterizing and thus detecting variable ERPs, which keeps track of their temporal and spatial information through the continuous measurement of the area under the curve in ERP components. We illustrate the usefulness of the proposed ERP characterization for electrode selection in brain-computer interfaces (BCIs) and compare the results with other standard methods. We assess ERP classification for BCI use with Bayesian linear discriminant analysis (BLDA) and cross-validation. We also evaluate performance with both the information transfer rate and BCI utility. The results of our validation tests show that this characterization helps to take advantage of the information on the evolution of positive and negative ERP components and, therefore, to efficiently select electrodes for optimized ERP detection. The proposed method improves the classification accuracy and bitrate of all sets of electrodes analyzed. Furthermore, the method is robust between different day sessions. Our work contributes to the efficient detection of ERPs, manages inter- and intrasubject variability, decreases the computational cost of classic detection methods and contributes to promoting low-cost personalized brain-computer interfaces.
Highlights
A brain-computer interface (BCI) is a technology that allows people to communicate with the external environment without relying on the usual peripheral pathways
We show that the implementation of these methods improves the performance of P300-based BCIs with Bayesian linear discriminant analysis (BLDA)
RESULTS we show the results of the accuracy achieved with the maximum AUC (maxAUC) method to characterize event-related potentials (ERPs) and compare the associated performance with the accuracy of electrodes of a widely used standard configuration
Summary
A brain-computer interface (BCI) is a technology that allows people to communicate with the external environment without relying on the usual peripheral pathways. In recent years, this technology has been widely studied, and modern research has provided different methods to implement it, through event-related potentials (ERPs). Such variable brain responses reflect intrinsic neurophysiological characteristics of the brain and are closely associated with cognitive function varying across individuals, ages, pathological conditions or genetic factors [2]–[5]. Variability is an intrinsic property of brain function that we must understand and manage
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