Tagged: Signal analysis

Electrooculogram based system for computer control using a multiple feature classification model

This paper discusses the creation of a system for computer-aided communication through automated analysis and processing of electrooculogram signals. In situations of disease or trauma, there may be an inability to communicate with others through standard means such as speech or typing. Eye movement tends to be one of the last remaining active muscle capabilities for people with neurodegenerative disorders, such as amyotrophic lateral sclerosis (ALS) also known as Lou Gehrig’s disease. Thus, there is a need for eye movement based systems to enable communication. To meet this need, the Telepathix system was designed to accept eye movement commands denoted by looking to the left, looking to the right, and looking straight ahead to navigate a virtual keyboard. Using a ternary virtual keyboard layout and a multiple feature classification model, a typing speed of 6 letters per minute was achieved

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.

Do We See Before We Look?

We investigated neural correlates of target detection in the electroencephalogram (EEG) during a free viewing search task and analyzed signals locked to saccadic events. Subjects performed a search task for multiple random scenes while we simultaneously recorded 64 channels of EEG and tracked subjects eye position. For each subject we identified target saccades (TS) and distractor saccades (DS). We sampled the sets of TS and DS saccades such that they were equalized/matched for saccade direction and duration, ensuring that no information in the saccade properties themselves was discriminating for their type. We aligned EEG to the saccade onset and used logistic regression (LR), in the space of the 64 electrodes, to identify activity discriminating a TS from a DS on a single-trial basis. We found significant discriminating activity in the EEG both before and after the saccade. We also saw substantial reduction in discriminating activity when the saccade was executed. We conclude that we can identify neural signatures of detection both before and after the saccade, indicating that subjects anticipate the target before the last saccade, which serves to foveate and confirm the target identity.

A system for single-trial analysis of simultaneously acquired EEG and fMRI

In this paper we describe a system for simultaneously acquiring EEG and fMRI and evaluate it in terms of discriminating, single-trial, task-related neural components in the EEG. Using an auditory oddball stimulus paradigm, we acquire EEG data both inside and outside a 1.5T MR scanner and compare both power spectra and single-trial discrimination performance for both conditions. We find that EEG activity acquired inside the MR scanner during echo planer image acquisition is of high enough quality to enable single-trial discrimination performance that is 95 % of that acquired outside the scanner. We conclude that EEG acquired simultaneously with fMRI is of high enough fidelity to permit single-trial analysis.