Tagged: Data mining

The Bilinear Brain: Towards Subject‐Invariant Analysis

A major challenge in single-trial electroencephalography (EEG) analysis and Brain Computer Interfacing (BCI) is the so called, inter-subject/inter-session variability: (i.e large variability in measurements obtained during different recording sessions). This variability restricts the number of samples available for single-trial analysis to a limited number that can be obtained during a single session. Here we propose a novel method that distinguishes between subject-invariant features and subject-specific features, based on a bilinear formulation. The method allows for one to combine multiple recording of EEG to estimate the subject-invariant parameters, hence addressing the issue of inter-subject variability, while reducing the complexity of estimation for the subject-specific parameters. The method is demonstrated on 34 datasets from two different experimental paradigms: Perception categorization task and Rapid Serial Visual Presentation (RSVP) task. We show significant improvements in classification performance over state-of-the-art methods. Further, our method extracts neurological components never before reported on the RSVP thus demonstrating the ability of our method to extract novel neural signatures from the data.

Classifying single-trial ERPs from visual and frontal cortex during free viewing

Event-related potentials (ERPs) recorded at the scalp are indicators of brain activity associated with event-related information processing; hence they may be suitable for the assessment of changes in cognitive processing load. While the measurement of ERPs in a laboratory setting and classifying those ERPs is trivial, such a task presents major challenges in a “real world” setting where the EEG signals are recorded when subjects freely move their eyes and the sensory inputs are continuously, as opposed to discretely presented. Here we demonstrate that with the aid of second-order blind identification (SOBI), a blind source separation (BSS) algorithm: (1) we can extract ERPs from such challenging data sets; (2) we were able to obtain meaningful single-trial ERPs in addition to averaged ERPs; and (3) we were able to estimate the spatial origins of these ERPs. Finally, using back-propagation neural networks as classifiers, we show that these single-trial ERPs from specific brain regions can be used to determine moment-to-moment changes in cognitive processing load during a complex “real world” task.