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Purpose Brain–computer interface (BCI) techniques may provide computer access for individuals with severe physical impairments. However, the relatively hidden nature of BCI control obscures how BCI systems work behind the scenes, making it difficult to understand “how” electroencephalography (EEG) records the BCI-related brain signals, “what” brain signals are recorded by EEG, and “why” these signals are targeted for BCI control. Furthermore, in the field of speech-language-hearing, signals targeted for BCI application have been of primary interest to clinicians and researchers in the area of augmentative and alternative communication (AAC). However, signals utilized for BCI control reflect sensory, cognitive, and motor processes, which are of interest to a range of related disciplines, including speech science. Method This tutorial was developed by a multidisciplinary team emphasizing primary and secondary BCI-AAC–related signals of interest to speech-language-hearing. Results An overview of BCI-AAC–related signals are provided discussing (a) “how” BCI signals are recorded via EEG; (b) “what” signals are targeted for noninvasive BCI control, including the P300, sensorimotor rhythms, steady-state evoked potentials, contingent negative variation, and the N400; and (c) “why” these signals are targeted. During tutorial creation, attention was given to help support EEG and BCI understanding for those without an engineering background. Conclusion Tutorials highlighting how BCI-AAC signals are elicited and recorded can help increase interest and familiarity with EEG and BCI techniques and provide a framework for understanding key principles behind BCI-AAC design and implementation.

Purpose: The purpose of this scoping review was to identify and synthesize research on interventions in which noninvasive brain stimulation (NIBS) was used to improve linguistic abilities in individuals with aphasia. NIBS comprising transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) are emerging technologies with potential to improve the underlying neurobiology of language in brains with stroke-induced lesions.Methods: The results of a systematic search of electronic literature databases were reviewed in CADIMA software by two authors yielding 57 studies published between 2015 and 2022. Selected articles were reviewed for study characteristics, participant characteristics, intervention details, and outcome measures.Results: NIBS is largely used for non-fluent aphasia during the chronic phase of recovery for improving naming and comprehension using picture naming and auditory comprehension of words, commands, and small paragraphs. Standardized test materials are used to measure treatment efficiency, with neuroimaging gradually emerging as an added measure to assess the neurobiological changes arising as a result of treatment induced linguistic recovery. Conclusion:The findings from this scoping review describe the design and delivery of NIBS treatment from subacute to chronic stages of recovery in aphasia. Positive results from heterogenous studies show the potential of NIBS in improving linguistic outcomes for people with aphasia. Large scale clinical trials and systematic reviews should further substantiate our findings of NIBS efficiency for specific language skills (e.g., naming accuracy, sentence production, discourse comprehension).

Purpose This study investigated whether changes in brain activity preceding spoken words can be used as a neural marker of speech intention. Specifically, changes in the contingent negative variation (CNV) were examined prior to speech production in three different study designs to determine a method that maximizes signal detection in a speaking task. Method Electroencephalography data were collected in three different protocols to elicit the CNV in a spoken word task that varied the timing and type of linguistic information. The first protocol provided participants with the word to be spoken before the instruction of whether or not to speak, the second provided both the word and the instruction to speak, and the third provided the instruction to speak before the word. Participants ( N = 18) were split into three groups (one for each protocol) and were instructed to either speak (Go) or refrain from speaking (NoGo) each word according to task instructions. The CNV was measured by analyzing the difference in slope between Go and NoGo trials. Results Statistically significant effects of hemispheric laterality on the CNV slope confirm the third protocol where the participants know they will speak in advance of the word, as the paradigm that reliably elicits a CNV response related to speech intention. Conclusions The maximal CNV response when the instruction is known before the word indicates the neural processing measured in this protocol may reflect a generalized speech intention process in which the speech-language systems become prepared to speak and then execute production once the word information is provided. Further analysis of the optimal protocol identified in this study requires additional experimental investigation to confirm its role in eliciting an objective marker of speech intention. Supplemental Material https://doi.org/10.23641/asha.14111468

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