21,720 research outputs found
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification
Automatic classification of epileptic seizure types in electroencephalograms
(EEGs) data can enable more precise diagnosis and efficient management of the
disease. This task is challenging due to factors such as low signal-to-noise
ratios, signal artefacts, high variance in seizure semiology among epileptic
patients, and limited availability of clinical data. To overcome these
challenges, in this paper, we present SeizureNet, a deep learning framework
which learns multi-spectral feature embeddings using an ensemble architecture
for cross-patient seizure type classification. We used the recently released
TUH EEG Seizure Corpus (V1.4.0 and V1.5.2) to evaluate the performance of
SeizureNet. Experiments show that SeizureNet can reach a weighted F1 score of
up to 0.94 for seizure-wise cross validation and 0.59 for patient-wise cross
validation for scalp EEG based multi-class seizure type classification. We also
show that the high-level feature embeddings learnt by SeizureNet considerably
improve the accuracy of smaller networks through knowledge distillation for
applications with low-memory constraints
Quantitative Analysis of Saliency Models
Previous saliency detection research required the reader to evaluate
performance qualitatively, based on renderings of saliency maps on a few
shapes. This qualitative approach meant it was unclear which saliency models
were better, or how well they compared to human perception. This paper provides
a quantitative evaluation framework that addresses this issue. In the first
quantitative analysis of 3D computational saliency models, we evaluate four
computational saliency models and two baseline models against ground-truth
saliency collected in previous work.Comment: 10 page
Unsupervised decoding of long-term, naturalistic human neural recordings with automated video and audio annotations
Fully automated decoding of human activities and intentions from direct
neural recordings is a tantalizing challenge in brain-computer interfacing.
Most ongoing efforts have focused on training decoders on specific, stereotyped
tasks in laboratory settings. Implementing brain-computer interfaces (BCIs) in
natural settings requires adaptive strategies and scalable algorithms that
require minimal supervision. Here we propose an unsupervised approach to
decoding neural states from human brain recordings acquired in a naturalistic
context. We demonstrate our approach on continuous long-term
electrocorticographic (ECoG) data recorded over many days from the brain
surface of subjects in a hospital room, with simultaneous audio and video
recordings. We first discovered clusters in high-dimensional ECoG recordings
and then annotated coherent clusters using speech and movement labels extracted
automatically from audio and video recordings. To our knowledge, this
represents the first time techniques from computer vision and speech processing
have been used for natural ECoG decoding. Our results show that our
unsupervised approach can discover distinct behaviors from ECoG data, including
moving, speaking and resting. We verify the accuracy of our approach by
comparing to manual annotations. Projecting the discovered cluster centers back
onto the brain, this technique opens the door to automated functional brain
mapping in natural settings
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