22 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
Spike Rate and Spike Timing Contributions to Coding Taste Quality Information in Rat Periphery
There is emerging evidence that individual sensory neurons in the rodent brain rely on temporal features of the discharge pattern to code differences in taste quality information. In contrast, investigations of individual sensory neurons in the periphery have focused on analysis of spike rate and mostly disregarded spike timing as a taste quality coding mechanism. The purpose of this work was to determine the contribution of spike timing to taste quality coding by rat geniculate ganglion neurons using computational methods that have been applied successfully in other systems. We recorded the discharge patterns of narrowly tuned and broadly tuned neurons in the rat geniculate ganglion to representatives of the five basic taste qualities. We used mutual information to determine significant responses and the van Rossum metric to characterize their temporal features. While our findings show that spike timing contributes a significant part of the message, spike rate contributes the largest portion of the message relayed by afferent neurons from rat fungiform taste buds to the brain. Thus, spike rate and spike timing together are more effective than spike rate alone in coding stimulus quality information to a single basic taste in the periphery for both narrowly tuned specialist and broadly tuned generalist neurons
Collaborative Brain-Computer Interface for Human Interest Detection in Complex and Dynamic Settings
Humans can fluidly adapt their interest in complex environments in ways that
machines cannot. Here, we lay the groundwork for a real-world system that
passively monitors and merges neural correlates of visual interest across team
members via Collaborative Brain Computer Interface (cBCI). When group interest
is detected and co-registered in time and space, it can be used to model the
task relevance of items in a dynamic, natural environment. Previous work in
cBCIs focuses on static stimuli, stimulus- or response- locked analyses, and
often within-subject and experiment model training. The contributions of this
work are twofold. First, we test the utility of cBCI on a scenario that more
closely resembles natural conditions, where subjects visually scanned a video
for target items in a virtual environment. Second, we use an
experiment-agnostic deep learning model to account for the real-world use case
where no training set exists that exactly matches the end-users task and
circumstances. With our approach we show improved performance as the number of
subjects in the cBCI ensemble grows, and the potential to reconstruct
ground-truth target occurrence in an otherwise noisy and complex environment.Comment: 6 pages, 6 figure