Tagged: P300

Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface

Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as outperform traditional within-subject calibration techniques when limited data is available. This method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.

Single-trial discrimination for integrating simultaneous EEG and fMRI: Identifying cortical areas contributing to trial-to-trial variability in the auditory oddball task

The auditory oddball task is a well-studied stimulus paradigm used to investigate the neural correlates of simple target detection. It elicits several classic event-related potentials (ERPs), the most prominent being the P300 which is seen as a neural correlate of subjects’ detection of rare (target) stimuli. Though trial-averaging is typically used to identify and characterize such ERPs, their latency and amplitude can vary on a trial-to-trial basis reflecting variability in the underlying neural information processing. Here we simultaneously recorded EEG and fMRI during an auditory oddball task and identified cortical areas correlated with the trial-to-trial variability of task-discriminating EEG components. Unique to our approach is a linear multivariate method for identifying task-discriminating components within specific stimulus- or response-locked time windows. We find fMRI activations indicative of distinct processes that contribute to the single-trial variability during target detection. These regions are different from those found using standard, including trial-averaged, regressors. Of particular note is the strong activation of the lateral occipital complex (LOC). The LOC was not seen when using traditional event-related regressors. Though LOC is typically associated with visual/spatial attention, its activation in an auditory oddball task, where attention can wax and wane from trial to trial, indicates that it may be part of a more general attention network involved in allocating resources for target detection and decision making. Our results show that trial-to-trial variability in EEG components, acquired simultaneously with fMRI, can yield task-relevant BOLD activations that are otherwise unobservable using traditional fMRI analysis.