8 accepted papers
The current standard for training brain-computer interface (BCI) machine learning models is user-specific. There is a high interest in developing generic models that are trained on data from other users to minimize BCI calibration time; however, this is limited by noisy, non-stationary brain signals
Brain-computer interfaces (BCIs) can provide a means of communication for individuals with severe neuromuscular diseases, the target end-users. While personalized BCI machine learning models are the current standard, models trained on data from other users could reduce BCI calibration time. We use a…
Mixture-of-Experts (MoE) models have become popular in machine learning, boosting performance by partitioning tasks across multiple experts. However, the need for several experts often results in high computational costs, limiting their application on resource-constrained devices with stringent real…
We propose and show the efficacy of a new method to address generic inverse problems. Inverse modeling is the task whereby one seeks to determine the hidden parameters of a natural system that produce a given set of observed measurements. Recent work has shown impressive results using deep learning,…
Speech intelligibility in cochlear implant (CI) users degrades considerably in listening environments with reverberation and noise. Previous research in automatic speech recognition (ASR) has shown that phoneme-based speech enhancement algorithms improve ASR system performance in reverberant environ…
Cochlear implant (CI) users experience substantial difficulties in understanding reverberant speech. A previous study proposed a strategy that leverages automatic speech recognition (ASR) to recognize reverberant speech and speech synthesis to translate the recognized text into anechoic speech. Howe…
Latent Dirichlet allocation (LDA) is a statistical model that is often used to discover topics or themes in a large collection of documents. In the LDA model, topics are modeled as discrete distributions over a finite vocabulary of words. The LDA is also a popular choice to model other datasets span…
The P300-based brain-computer interface (BCI) speller relies on eliciting and detecting specific brain responses to target stimulus events, termed event-related potentials (ERPs). In a visual speller, ERPs are elicited when the user's desired character, i.e. the “target,” is flashed on a computer sc…