44 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
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
Relating EEG to continuous speech using deep neural networks: a review
Objective. When a person listens to continuous speech, a corresponding
response is elicited in the brain and can be recorded using
electroencephalography (EEG). Linear models are presently used to relate the
EEG recording to the corresponding speech signal. The ability of linear models
to find a mapping between these two signals is used as a measure of neural
tracking of speech. Such models are limited as they assume linearity in the
EEG-speech relationship, which omits the nonlinear dynamics of the brain. As an
alternative, deep learning models have recently been used to relate EEG to
continuous speech, especially in auditory attention decoding (AAD) and
single-speech-source paradigms. Approach. This paper reviews and comments on
deep-learning-based studies that relate EEG to continuous speech in AAD and
single-speech-source paradigms. We point out recurrent methodological pitfalls
and the need for a standard benchmark of model analysis. Main results. We
gathered 29 studies. The main methodological issues we found are biased
cross-validations, data leakage leading to over-fitted models, or
disproportionate data size compared to the model's complexity. In addition, we
address requirements for a standard benchmark model analysis, such as public
datasets, common evaluation metrics, and good practices for the match-mismatch
task. Significance. We are the first to present a review paper summarizing the
main deep-learning-based studies that relate EEG to speech while addressing
methodological pitfalls and important considerations for this newly expanding
field. Our study is particularly relevant given the growing application of deep
learning in EEG-speech decoding
Objectief en automatisch meten van spraakverstaan
Hearing impairment is considered the most prevalent impairment in the world. Methods to diagnose hearing loss are used for screening, diagnostics and evaluation during rehabilitation with auditory prostheses. We can distinguish between behavioural and objective diagnostic methods. With behavioural methods, active participation of the patient is required, which leads to confounded results, or makes it entirely impossible to test some patients. With behavioural methods, the response is the result of activation of the entire auditory system, so it is hard to distinguish between disorders at different levels. With an objective method, these problems are not present, and by manipulating the auditory stimulus and response detection methods, different parts of the auditory system can be precisely evaluated. Current objective measurements only allow investigation of the early parts of the auditory system as they do not use real speech stimuli. This is currently the missing link between behavioural and objective audiometry. In this project, we developed an objective and automatic measure of speech intelligibility using speech signals. The basic principle is to record the electroencephalogram (EEG) in response to the speech signal and define a metric to compare between the EEG and the temporal envelope of the speech signal. This metric is indicative of the acuity of transmission of essential features of speech to the auditory cortex, and therefore of speech intelligibility.status: publishe
Semantic features in Dutch: a replication study of Brodbeck et al. and Broderick et al.
status: publishe
Effect of Task and Attention on Neural Tracking of Speech
EEG-based measures of neural tracking of natural running speech are becoming increasingly popular to investigate neural processing of speech and have applications in audiology. When the stimulus is a single speaker, it is usually assumed that the listener actively attends to and understands the stimulus. However, as the level of attention of the listener is inherently variable, we investigated how this affected neural envelope tracking. Using a movie as a distractor, we varied the level of attention while we estimated neural envelope tracking. We varied the intelligibility level by adding stationary noise. We found a significant difference in neural envelope tracking between the condition with maximal attention and the movie condition. This difference was most pronounced in the right-frontal region of the brain. The degree of neural envelope tracking was highly correlated with the stimulus signal-to-noise ratio, even in the movie condition. This could be due to residual neural resources to passively attend to the stimulus. When envelope tracking is used to measure speech understanding objectively, this means that the procedure can be made more enjoyable and feasible by letting participants watch a movie during stimulus presentation.status: publishe
The Effect of Stimulus Choice on an EEG-Based Objective Measure of Speech Intelligibility.
OBJECTIVES: Recently, an objective measure of speech intelligibility (SI), based on brain responses derived from the electroencephalogram (EEG), has been developed using isolated Matrix sentences as a stimulus. We investigated whether this objective measure of SI can also be used with natural speech as a stimulus, as this would be beneficial for clinical applications. DESIGN: We recorded the EEG in 19 normal-hearing participants while they listened to two types of stimuli: Matrix sentences and a natural story. Each stimulus was presented at different levels of SI by adding speech weighted noise. SI was assessed in two ways for both stimuli: (1) behaviorally and (2) objectively by reconstructing the speech envelope from the EEG using a linear decoder and correlating it with the acoustic envelope. We also calculated temporal response functions (TRFs) to investigate the temporal characteristics of the brain responses in the EEG channels covering different brain areas. RESULTS: For both stimulus types, the correlation between the speech envelope and the reconstructed envelope increased with increasing SI. In addition, correlations were higher for the natural story than for the Matrix sentences. Similar to the linear decoder analysis, TRF amplitudes increased with increasing SI for both stimuli. Remarkable is that although SI remained unchanged under the no-noise and +2.5 dB SNR conditions, neural speech processing was affected by the addition of this small amount of noise: TRF amplitudes across the entire scalp decreased between 0 and 150 ms, while amplitudes between 150 and 200 ms increased in the presence of noise. TRF latency changes in function of SI appeared to be stimulus specific: the latency of the prominent negative peak in the early responses (50 to 300 ms) increased with increasing SI for the Matrix sentences, but remained unchanged for the natural story. CONCLUSIONS: These results show (1) the feasibility of natural speech as a stimulus for the objective measure of SI; (2) that neural tracking of speech is enhanced using a natural story compared to Matrix sentences; and (3) that noise and the stimulus type can change the temporal characteristics of the brain responses. These results might reflect the integration of incoming acoustic features and top-down information, suggesting that the choice of the stimulus has to be considered based on the intended purpose of the measurement.status: publishe