The impact of internal and external factors across language domains and features in sequential bilingual acquisition.

Factors which impact bilingual language development can often interact with different language features. The current study teases apart the impact of internal and external factors (chronological age, length of exposure, L2 richness, L2 use at home, maternal education and maternal L2 proficiency) across linguistic domains and features (vocabulary, morphology and syntax). Participants were 40 Arabic-speaking sequential bilinguals acquiring English (5;7-12;2, M = 8;4).

Opportunities, pitfalls and trade-offs in designing protocols for measuring the neural correlates of speech.

Decoding speech and speech-related processes directly from the human brain has intensified in studies over recent years as such a decoder has the potential to positively impact people with limited communication capacity due to disease or injury. Additionally, it can present entirely new forms of human-computer interaction and human-machine communication in general and facilitate better neuroscientific understanding of speech processes. Here, we synthesize the literature on neural speech decoding pertaining to how speech decoding experiments have been conducted, coalescing around a necessity for thoughtful experimental design aimed at specific research goals, and robust procedures for evaluating speech decoding paradigms.

A Bimodal Deep Learning Architecture for EEG-fNIRS Decoding of Overt and Imagined Speech.

Objective: Brain-computer interfaces (BCI) studies are increasingly leveraging different attributes of multiple signal modalities simultaneously. Bimodal data acquisition protocols combining the temporal resolution of electroencephalography (EEG) with the spatial resolution of functional near-infrared spectroscopy (fNIRS) require novel approaches to decoding.

Methods: We present an EEG-fNIRS Hybrid BCI that employs a new bimodal deep neural network architecture consisting of two convolutional sub-networks (subnets) to decode overt and imagined speech.

Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG.

Classification of electroencephalography (EEG) signals corresponding to imagined speech production is important for the development of a direct-speech brain-computer interface (DS-BCI). Deep learning (DL) has been utilized with great success across several domains. However, it remains an open question whether DL methods provide significant advances over traditional machine learning (ML) approaches for classification of imagined speech.

Neurolinguistics Research Advancing Development of a Direct-Speech Brain-Computer Interface.

A direct-speech brain-computer interface (DS-BCI) acquires neural signals corresponding to imagined speech, then processes and decodes these signals to produce a linguistic output in the form of phonemes, words, or sentences. Recent research has shown the potential of neurolinguistics to enhance decoding approaches to imagined speech with the inclusion of semantics and phonology in experimental procedures. As neurolinguistics research findings are beginning to be incorporated within the scope of DS-BCI research, it is our view that a thorough understanding of imagined speech, and its relationship with overt speech, must be considered an integral feature of research in this field.

What language says about the psychology of events.

Despite the variety of verb meanings, linguistic research on their syntax and semantics has shown that they can be categorized into a finite and surprisingly small number of event types. More recently, research in the psycholinguistics of language acquisition and processing has emphasized the relevance of event type. The wider implication of these findings is that the conceptual fluidity of verbal concepts is confined by the fundamental structures of mental grammar, shedding light on this important interface between cognition and syntactic organization.