108 research outputs found
Analyzing P300 Distractors for Target Reconstruction
P300-based brain-computer interfaces (BCIs) are often trained per-user and
per-application space. Training such models requires ground truth knowledge of
target and non-target stimulus categories during model training, which imparts
bias into the model. Additionally, not all non-targets are created equal; some
may contain visual features that resemble targets or may otherwise be visually
salient. Current research has indicated that non-target distractors may elicit
attenuated P300 responses based on the perceptual similarity of these
distractors to the target category. To minimize this bias, and enable a more
nuanced analysis, we use a generalized BCI approach that is fit to neither user
nor task. We do not seek to improve the overall accuracy of the BCI with our
generalized approach; we instead demonstrate the utility of our approach for
identifying target-related image features. When combined with other intelligent
agents, such as computer vision systems, the performance of the generalized
model equals that of the user-specific models, without any user specific data.Comment: 4 pages, 3 figure
Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time
This paper investigates how to utilize different forms of human interaction
to safely train autonomous systems in real-time by learning from both human
demonstrations and interventions. We implement two components of the
Cycle-of-Learning for Autonomous Systems, which is our framework for combining
multiple modalities of human interaction. The current effort employs human
demonstrations to teach a desired behavior via imitation learning, then
leverages intervention data to correct for undesired behaviors produced by the
imitation learner to teach novel tasks to an autonomous agent safely, after
only minutes of training. We demonstrate this method in an autonomous perching
task using a quadrotor with continuous roll, pitch, yaw, and throttle commands
and imagery captured from a downward-facing camera in a high-fidelity simulated
environment. Our method improves task completion performance for the same
amount of human interaction when compared to learning from demonstrations
alone, while also requiring on average 32% less data to achieve that
performance. This provides evidence that combining multiple modes of human
interaction can increase both the training speed and overall performance of
policies for autonomous systems.Comment: 9 pages, 6 figure
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
Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)
© 1993-2012 IEEE. One big challenge that hinders the transition of brain-computer interfaces (BCIs) from laboratory settings to real-life applications is the availability of high-performance and robust learning algorithms that can effectively handle individual differences, i.e., algorithms that can be applied to a new subject with zero or very little subject-specific calibration data. Transfer learning and domain adaptation have been extensively used for this purpose. However, most previous works focused on classification problems. This paper considers an important regression problem in BCI, namely, online driver drowsiness estimation from EEG signals. By integrating fuzzy sets with domain adaptation, we propose a novel online weighted adaptation regularization for regression (OwARR) algorithm to reduce the amount of subject-specific calibration data, and also a source domain selection (SDS) approach to save about half of the computational cost of OwARR. Using a simulated driving dataset with 15 subjects, we show that OwARR and OwARR-SDS can achieve significantly smaller estimation errors than several other approaches. We also provide comprehensive analyses on the robustness of OwARR and OwARR-SDS
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
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