No 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.

No abstract

No abstract

Working Memory (WM) is a limited capacity system for storing and processing information, which varies from subject to subject. Several works show the ability to predict the performance of WM with machine learning (ML) methods, and although good prediction results are obtained in these works, ignoring the intersubject variability and the temporal and spatial characterization in a WM task to improve the prediction in each subject. In this paper, we take advantage of the spectral properties of WM to characterize the individual differences in visual WM capacity and predict the subject’s performance. Feature selection was implemented through the selection of electrodes making use of methods to treat unbalanced classes. The results show a correlation between the accuracy achieved with an Regularized Linear Discriminant Analysis (RLDA) classifier using the power spectrum of the EEG signal and the accuracy achieved by each subject in the behavioral experiment response of a WM task with retro-cue. The proposed methodology allows identifying spatial and temporal characteristics in the WM performance in each subject. Our methodology shows that it is possible to predict the WM performance in each subject. Finally, our results showed that by knowing the spatiotemporal characteristics that predict WM performance, it is possible to customize a WM task and optimize the use of electrodes for agile processing adapted to a specific subject. Thus, we pave the way for implementing neurofeedback through a Brain-Computer Interface.

No abstract

No abstract

No abstract