205 research outputs found
Potential Pitfalls in High-Tech Copyright Litigation, 25 J. Marshall J. Computer & Info. L. 513 (2008)
Alleging software and data-base infringement is probably the most common offensive strategy currently seen in high-tech copyright litigation. In the context of a hypothetical factual setting, this article explores three potential pitfalls attendant to such a strategy, and suggests ways to minimize those risks
Stimulus-Informed Generalized Canonical Correlation Analysis of Stimulus-Following Brain Responses
In brain-computer interface or neuroscience applications, generalized
canonical correlation analysis (GCCA) is often used to extract correlated
signal components in the neural activity of different subjects attending to the
same stimulus. This allows quantifying the so-called inter-subject correlation
or boosting the signal-to-noise ratio of the stimulus-following brain responses
with respect to other (non-)neural activity. GCCA is, however,
stimulus-unaware: it does not take the stimulus information into account and
does therefore not cope well with lower amounts of data or smaller groups of
subjects. We propose a novel stimulus-informed GCCA algorithm based on the
MAXVAR-GCCA framework. We show the superiority of the proposed
stimulus-informed GCCA method based on the inter-subject correlation between
electroencephalography responses of a group of subjects listening to the same
speech stimulus, especially for lower amounts of data or smaller groups of
subjects
Learning Subject-Invariant Representations from Speech-Evoked EEG Using Variational Autoencoders
The electroencephalogram (EEG) is a powerful method to understand how the
brain processes speech. Linear models have recently been replaced for this
purpose with deep neural networks and yield promising results. In related EEG
classification fields, it is shown that explicitly modeling subject-invariant
features improves generalization of models across subjects and benefits
classification accuracy. In this work, we adapt factorized hierarchical
variational autoencoders to exploit parallel EEG recordings of the same
stimuli. We model EEG into two disentangled latent spaces. Subject accuracy
reaches 98.96% and 1.60% on respectively the subject and content latent space,
whereas binary content classification experiments reach an accuracy of 51.51%
and 62.91% on respectively the subject and content latent space
Town of Lisbon, Maine Annual Financial Report For The Fiscal Year Ended June 30, 2009
status: publishe
Relating the fundamental frequency of speech with EEG using a dilated convolutional network
To investigate how speech is processed in the brain, we can model the
relation between features of a natural speech signal and the corresponding
recorded electroencephalogram (EEG). Usually, linear models are used in
regression tasks. Either EEG is predicted, or speech is reconstructed, and the
correlation between predicted and actual signal is used to measure the brain's
decoding ability. However, given the nonlinear nature of the brain, the
modeling ability of linear models is limited. Recent studies introduced
nonlinear models to relate the speech envelope to EEG. We set out to include
other features of speech that are not coded in the envelope, notably the
fundamental frequency of the voice (f0). F0 is a higher-frequency feature
primarily coded at the brainstem to midbrain level. We present a
dilated-convolutional model to provide evidence of neural tracking of the f0.
We show that a combination of f0 and the speech envelope improves the
performance of a state-of-the-art envelope-based model. This suggests the
dilated-convolutional model can extract non-redundant information from both f0
and the envelope. We also show the ability of the dilated-convolutional model
to generalize to subjects not included during training. This latter finding
will accelerate f0-based hearing diagnosis.Comment: Accepted for Interspeech 202
Detecting post-stroke aphasia using EEG-based neural envelope tracking of natural speech
[Objective]. After a stroke, one-third of patients suffer from aphasia, a
language disorder that impairs communication ability. The standard behavioral
tests used to diagnose aphasia are time-consuming and have low ecological
validity. Neural tracking of the speech envelope is a promising tool for
investigating brain responses to natural speech. The speech envelope is crucial
for speech understanding, encompassing cues for processing linguistic units. In
this study, we aimed to test the potential of the neural envelope tracking
technique for detecting language impairments in individuals with aphasia (IWA).
[Approach]. We recorded EEG from 27 IWA in the chronic phase after stroke and
22 controls while they listened to a story. We quantified neural envelope
tracking in a broadband frequency range as well as in the delta, theta, alpha,
beta, and gamma frequency bands using mutual information analysis. Besides
group differences in neural tracking measures, we also tested its suitability
for detecting aphasia using a Support Vector Machine (SVM) classifier. We
further investigated the required recording length for the SVM to detect
aphasia and to obtain reliable outcomes. [Results]. IWA displayed decreased
neural envelope tracking compared to controls in the broad, delta, theta, and
gamma band. Neural tracking in these frequency bands effectively captured
aphasia at the individual level (SVM accuracy 84%, AUC 88%). High-accuracy and
reliable detection could be obtained with 5-7 minutes of recording time.
[Significance]. Our study shows that neural tracking of speech is an effective
biomarker for aphasia. We demonstrated its potential as a diagnostic tool with
high reliability, individual-level detection of aphasia, and time-efficient
assessment. This work represents a significant step towards more automatic,
objective, and ecologically valid assessments of language impairments in
aphasia
- …