Biology subjects

Comstock, L. B.

Publications and source records attributed to Comstock, L. B..

3 recordsLinked to original sources

bioRxiv2022

An open-access EEG dataset for speech decoding: Exploring the role of articulation and coarticulation

Electroencephalography (EEG) holds promise for brain-computer interface (BCI) devices as a non-invasive measure of neural activity. With increased attention to EEG-based BCI systems, publicly available datasets that can represent the complex tasks required for naturalistic speech decoding are necessary to establish a common standard of performance within the BCI community. Effective solutions must overcome various kinds of noise in the EEG signal and remain reliable across sessions and subjects without overfitting to a specific dataset or task. We present two validated datasets (N=8 and N=16) for classification at the phoneme and word level and by the articulatory properties of phonemes. EEG signals were recorded from 64 channels while subjects listened to and repeated six consonants and five vowels. Individual phonemes were combined in different phonetic environments to produce coarticulated variation in forty consonant-vowel pairs, twenty real words, and twenty pseudowords. Phoneme pairs and words were presented during a control condition and during transcranial magnetic stimulation targeted to inhibit or augment the EEG signal associated with specific articulatory processes.

neuroscience↗

bioRxiv2021

A meta-analysis of task-based differences in bilingual L1 and L2 language networks

The functional organization of first (L1) and second (L2) language processing in bilinguals remains a topic of great interest to the neurolinguistics community. Functional magnetic resonance imaging (fMRI) studies report meaningful differences in the location and extent of hemodynamic changes between tasks performed in the L1 and L2, yet there is no consensus on whether these networks can be considered truly distinct. In part, this may be due to the multiplicity of task designs implemented in such studies, which complicates the interpretation of their findings. This paper compares the results of previous bilingual meta-analyses to a new ALE meta-analysis that categorizes neuroimaging studies by task design. Factors such as the age of L2 acquisition (AoA) and the L2 language proficiency level of participants are also considered. The findings support previous accounts of the effect of participant characteristics on linguistic processing, while at the same time revealing dissociable differences in fMRI activation for L1 and L2 networks within and across tasks that appear independent of these external factors.

neuroscience↗

bioRxiv2021

Exploratory methods for high-performance EEG speech decoding

State-of-the-art technologies in neural speech decoding utilize data collected from microwires or microarrays implanted directly into the cerebral cortex. Yet as a tool accessible only to individuals with implanted electrodes, speech decoding from devices of this nature is severely limited in its implementation, and cannot be considered a viable solution for widespread application. Speech decoding from non-invasive EEG signals can achieve relatively high accuracy (70-80%), but only from very small classification tasks, with more complex tasks typically yielding a limited (20-50%) classification accuracy. We propose a novel combination of technologies in which transcranial magnetic stimulation (TMS) is first applied to augment the neural signals of interest, producing a greater signal-to-noise ratio in the EEG data. Next, delay differential analysis (DDA) - a cutting-edge computational method based on nonlinear dynamics - is implemented to capture the widest range of information available in the neural signal, by incorporating both linear and nonlinear dynamics.

neuroscience↗