12 research outputs found
Offline EEG-based driver drowsiness estimation using enhanced batch-mode active learning (EBMAL) for regression
© 2016 IEEE. There are many important regression problems in real-world brain-computer interface (BCI) applications, e.g., driver drowsiness estimation from EEG signals. This paper considers offline analysis: given a pool of unlabeled EEG epochs recorded during driving, how do we optimally select a small number of them to label so that an accurate regression model can be built from them to label the rest? Active learning is a promising solution to this problem, but interestingly, to our best knowledge, it has not been used for regression problems in BCI so far. This paper proposes a novel enhanced batch-mode active learning (EBMAL) approach for regression, which improves upon a baseline active learning algorithm by increasing the reliability, representativeness and diversity of the selected samples to achieve better regression performance. We validate its effectiveness using driver drowsiness estimation from EEG signals. However, EBMAL is a general approach that can also be applied to many other offline regression problems beyond BCI
EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features
Riemannian geometry has been successfully used in many brain-computer
interface (BCI) classification problems and demonstrated superior performance.
In this paper, for the first time, it is applied to BCI regression problems, an
important category of BCI applications. More specifically, we propose a new
feature extraction approach for Electroencephalogram (EEG) based BCI regression
problems: a spatial filter is first used to increase the signal quality of the
EEG trials and also to reduce the dimensionality of the covariance matrices,
and then Riemannian tangent space features are extracted. We validate the
performance of the proposed approach in reaction time estimation from EEG
signals measured in a large-scale sustained-attention psychomotor vigilance
task, and show that compared with the traditional powerband features, the
tangent space features can reduce the root mean square estimation error by
4.30-8.30%, and increase the estimation correlation coefficient by 6.59-11.13%.Comment: arXiv admin note: text overlap with arXiv:1702.0291
Decreasing the human coding burden in randomized trials with text-based outcomes via model-assisted impact analysis
For randomized trials that use text as an outcome, traditional approaches for
assessing treatment impact require that each document first be manually coded
for constructs of interest by trained human raters. This process, the current
standard, is both time-consuming and limiting: even the largest human coding
efforts are typically constrained to measure only a small set of dimensions
across a subsample of available texts. In this work, we present an inferential
framework that can be used to increase the power of an impact assessment, given
a fixed human-coding budget, by taking advantage of any ``untapped"
observations -- those documents not manually scored due to time or resource
constraints -- as a supplementary resource. Our approach, a methodological
combination of causal inference, survey sampling methods, and machine learning,
has four steps: (1) select and code a sample of documents; (2) build a machine
learning model to predict the human-coded outcomes from a set of automatically
extracted text features; (3) generate machine-predicted scores for all
documents and use these scores to estimate treatment impacts; and (4) adjust
the final impact estimates using the residual differences between human-coded
and machine-predicted outcomes. As an extension to this approach, we also
develop a strategy for identifying an optimal subset of documents to code in
Step 1 in order to further enhance precision. Through an extensive simulation
study based on data from a recent field trial in education, we show that our
proposed approach can be used to reduce the scope of a human-coding effort
while maintaining nominal power to detect a significant treatment impact