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

Cooney, Ciaran; Folli, Raffaella; Coyle, Damien

doi:10.1016/j.neubiorev.2022.104783

Cited by 7 publications

(9 citation statements)

References 99 publications

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“…The extent to which internal and vocalized speech generalize is still debated (Cooney, Folli, and Coyle 2018;Perrone-Bertolotti et al 2014;Alderson-Day and Fernyhough 2015), and depends on the investigated brain area (Pei, Leuthardt, et al 2011b;Soroush et al 2022). In this work, we found on average stronger representation for vocalized (74%) than internal speech (Figure 4A, 55%).…”

Section: Shared Neural Representations Between Internal and Vocalized...supporting

confidence: 52%

“…However, this same shared representation could have occurred if visual processes were activated in the internal speech phase. For instance, the participant could have performed mental visualization of the written word instead of generating an internal monologue, as the subjective perception of internal speech may vary between individuals (Alderson-Day and Fernyhough 2015; Cooney, Folli, and Coyle 2022)…”

Section: Discussionmentioning

confidence: 99%

“…The relationship between inner speech and vocalized speech is still debated. The general consensus posits similarities between internal and vocalized speech processes (Pei, Leuthardt, et al 2011a), but the degree of overlap is not well understood (Cooney, Folli, and Coyle 2022; 2022; Martin et al 2018; Perrone-Bertolotti et al 2014; Alderson-Day and Fernyhough 2015). Characterizing similarities between vocalized and internal speech could provide evidence that results found with vocalized speech could translate to internal speech.…”

Section: Introductionmentioning

confidence: 99%

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Online internal speech decoding from single neurons in a human participant

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Bjånes

Pejsa

et al. 2022

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Speech brain-machine interfaces (BMI's) translate brain signals into words or audio outputs, enabling communication for people having lost their speech abilities due to diseases or injury. While important advances in vocalized, attempted, and mimed speech decoding have been achieved, results for internal speech decoding are sparse, and have yet to achieve high functionality. Notably, it is still unclear from which brain areas internal speech can be decoded. In this work, a tetraplegic participant with implanted microelectrode arrays located in the supramarginal gyrus (SMG) and primary somatosensory cortex (S1) performed internal and vocalized speech of six words and two pseudowords. We found robust internal speech decoding from SMG single neuron activity, achieving up to 91% classification accuracy during an online task (chance level 12.5%). Evidence of shared neural representations between internal speech, word reading, and vocalized speech processes were found. SMG represented words in different languages (English/ Spanish) as well as pseudowords, providing evidence for phonetic encoding. Furthermore, our decoder achieved high classification with multiple internal speech strategies (auditory imagination/ visual imagination). Activity in S1 was modulated by vocalized but not internal speech, suggesting no articulator movements of the vocal tract occurred during internal speech production. This works represents the first proof-of-concept for a high-performance internal speech BMI.

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Section: Shared Neural Representations Between Internal and Vocalized...supporting

confidence: 52%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Online internal speech decoding from single neurons in a human participant

Wandelt

Bjånes

Pejsa

et al. 2022

Preprint

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“…In patients with aphasia, both subjective and objective assessments indicate that imagined speech is better preserved than spoken language [11][12][13] , even in presence of severe overt (i.e. articulated and audible) speech deficits for reviews 9,30,31 ). However, nearly all studies address imagined speech decoding from an engineering perspective, their main goal being the optimization of current classifiers to boost decoding accuracy (see 32 for a review of classification methods.…”

Section: Introductionmentioning

confidence: 99%

“…Capitalizing on its far greater accessibility, several studies have employed surface EEG for decoding offline (i.e. open-loop) a wide variety of speech units imagery, most often in binary classification paradigm 9,29 (see for reviews 9,30,31 ). However, nearly all studies address imagined speech decoding from an engineering perspective, their main goal being the optimization of current classifiers to boost decoding accuracy (see 32 for a review of classification methods.…”

Section: Introductionmentioning

confidence: 99%

Learning to operate an imagined speech Brain-Computer Interface involves the spatial and frequency tuning of neural activity

Bhadra,

Giraud,

Marchesotti

2023

Preprint

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Brain-Computer Interfaces (BCI) will revolutionize the way people with impaired speech production can communicate. While recent studies confirm the possibility of decoding imagined speech based on pre-recorded intracranial neurophysiological signals, current efforts focus on collecting vast amounts of data to train classifiers, rather than exploring how the individual’s brain adapts to improve BCI control, an important aspect given the known problem of “BCI illiteracy”, the inability of some individuals to operate a BCI. This issue can be investigated by providing real-time feedback to allow users to identify the best control strategy. In this study, we trained 15 healthy participants to operate a simple binary BCI system based on electroencephalography (EEG) signals through syllable imagery for five consecutive days. We explored whether BCI-control improves with training and characterized the underlying neural dynamics, both in terms of EEG power changes and of the neural features contributing to real-time classification. Despite considerable interindividual variability in performance and learning, a significant improvement in BCI control was observed from day 1 to 5. Performance improvement was reproducibly associated with a global EEG power increase in frontal theta and a focal increase in temporal low-gamma, showing that learning to operate an imagined-speech BCI involves global and local dynamical changes involving low- and high-frequency neural features, respectively. These findings indicate that both machine and human learning must be considered to reach optimal controllability of imagined-speech BCI, and that non-invasive BCI-learning can help predict the individual benefit from an invasive speech BCI and guide both the electrode implantation and decoding strategies.

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