The Brain Differentially Prepares Inner and Overt Speech Production: Electrophysiological and Vascular Evidence

EEG based depression detection by machine learning: Does inner or overt speech condition provide better biomarkers when using emotion words as experimental cues?

BACKGROUND

Objective diagnostic approaches need to be tested to enhance the efficacy of depression detection. Non-invasive EEG-based identification represents a promising area.

AIMS

The present EEG study addresses two central questions: 1) whether inner or overt speech condition result in higher diagnositc accuracy of depression detection; and 2) does the affective nature of the presented emotion words count in such diagnostic approach.

METHODS

A matched case-control sample consisting of 10 depressed subjects and 10 healthy controls was assessed. An EEG headcap containing 64 electrodes measured neural responses to experimental cues presented in the form of 15 different words that belonged to three emotional categories: neutral, positive, and negative. 120 experimental cues was presented for every participant, each containing an "inner speech" and an "overt speech" segment. An EEGNet neural network was utilized.

RESULTS

The highest diagnostic accuracy of the EEGNet model was observed in the case of the overt speech condition (i.e. 69.5%), while a an overall subject-wise accuracy of 80% was achieved by the model. Only a negligible difference in diagnostic accuracy could be found between aggregated emotion word categories, with the highest accuracy (i.e. 70.2%) associated with the presentation of positive emotion words. Model decision was primarily influenced by electrodes representing the regions of the left parietal, the left temporal lobe and the middle frontal areas.

CONCLUSIONS

While the generalizability of our results is limited by the small sample size and potentially uncontrolled confounders, depression was associated with sensitive and presumably network-like aspects of these brain areas, potentially implying a higher level of emotion regulation that increases primarily in open communication.

Interdisciplinary approaches to understanding the inner speech, with emphasis on the role of incorporating clinical data

Neuroscience has largely conceptualized inner speech, sometimes called covert speech, as being a part of the language system, namely, a precursor to overt speech and/or speech without the motor component (impoverished motor speech). Yet interdisciplinary work has strongly suggested that inner speech is multidimensional and situated within the language system as well as in more domain general systems. By leveraging evidence from philosophy, linguistics, neuroscience and cognitive science, we argue that neuroscience can gain a more comprehensive understanding of inner speech processes. We will summarize the existing knowledge on the traditional approach to understanding the neuroscience of inner speech, which is squarely through the language system, before discussing interdisciplinary approaches to understanding the cognitive, linguistic and neural substrates/mechanisms that may be involved in inner speech. Given our own interests in inner speech after brain injury, we finish by discussing the theoretical and clinical benefits of researching inner speech in aphasia through an interdisciplinary lens.

Revealing the spatiotemporal brain dynamics of covert speech compared with overt speech: A simultaneous EEG-fMRI study

Covert speech (CS) refers to speaking internally to oneself without producing any sound or movement. CS is involved in multiple cognitive functions and disorders. Reconstructing CS content by brain-computer interface (BCI) is also an emerging technique. However, it is still controversial whether CS is a truncated neural process of overt speech (OS) or involves independent patterns. Here, we performed a word-speaking experiment with simultaneous EEG-fMRI. It involved 32 participants, who generated words both overtly and covertly. By integrating spatial constraints from fMRI into EEG source localization, we precisely estimated the spatiotemporal dynamics of neural activity. During CS, EEG source activity was localized in three regions: the left precentral gyrus, the left supplementary motor area, and the left putamen. Although OS involved more brain regions with stronger activations, CS was characterized by an earlier event-locked activation in the left putamen (peak at 262 ms versus 1170 ms). The left putamen was also identified as the only hub node within the functional connectivity (FC) networks of both OS and CS, while showing weaker FC strength towards speech-related regions in the dominant hemisphere during CS. Path analysis revealed significant multivariate associations, indicating an indirect association between the earlier activation in the left putamen and CS, which was mediated by reduced FC towards speech-related regions. These findings revealed the specific spatiotemporal dynamics of CS, offering insights into CS mechanisms that are potentially relevant for future treatment of self-regulation deficits, speech disorders, and development of BCI speech applications.

Overt Word Reading and Visual Object Naming in Adults with Dyslexia: Electroencephalography Study in Transparent Orthography

The study aimed to investigate overt reading and naming processes in adult people with dyslexia (PDs) in shallow (transparent) language orthography. The results of adult PDs are compared with adult healthy controls HCs. Comparisons are made in three phases: pre-lexical (150-260 ms), lexical (280-700 ms), and post-lexical stage of processing (750-1000 ms) time window. Twelve PDs and HCs performed overt reading and naming tasks under EEG recording. The word reading and naming task consisted of sparse neighborhoods with closed phonemic onset (words/objects sharing the same onset). For the analysis of the mean ERP amplitude for pre-lexical, lexical, and post-lexical time window, a mixed design ANOVA was performed with the right (F4, FC2, FC6, C4, T8, CP2, CP6, P4) and left (F3, FC5, FC1, T7, C3, CP5, CP1, P7, P3) electrode sites, within-subject factors and group (PD vs. HC) as between-subject factor. Behavioral response latency results revealed significantly prolonged reading latency between HCs and PDs, while no difference was detected in naming response latency. ERP differences were found between PDs and HCs in the right hemisphere's pre-lexical time window (160-200 ms) for word reading aloud. For visual object naming aloud, ERP differences were found between PDs and HCs in the right hemisphere's post-lexical time window (900-1000 ms). The present study demonstrated different distributions of the electric field at the scalp in specific time windows between two groups in the right hemisphere in both word reading and visual object naming aloud, suggesting alternative processing strategies in adult PDs. These results indirectly support the view that adult PDs in shallow language orthography probably rely on the grapho-phonological route during overt word reading and have difficulties with phoneme and word retrieval during overt visual object naming in adulthood.

Self-talk: research challenges and opportunities

In this review, we discuss major measurement and methodological challenges to studying self-talk. We review the assessment of self-talk frequency, studying self-talk in its natural context, personal pronoun usage within self-talk, experiential sampling methods, and the experimental manipulation of self-talk. We highlight new possible research opportunities and discuss recent advances such as brain imaging studies of self-talk, the use of self-talk by robots, and measurement of self-talk in aphasic patients.

Imagined Speech Classification Using EEG and Deep Learning

In this paper, we propose an imagined speech-based brain wave pattern recognition using deep learning. Multiple features were extracted concurrently from eight-channel electroencephalography (EEG) signals. To obtain classifiable EEG data with fewer sensors, we placed the EEG sensors on carefully selected spots on the scalp. To decrease the dimensions and complexity of the EEG dataset and to avoid overfitting during the deep learning algorithm, we utilized the wavelet scattering transformation. A low-cost 8-channel EEG headset was used with MATLAB 2023a to acquire the EEG data. The long-short term memory recurrent neural network (LSTM-RNN) was used to decode the identified EEG signals into four audio commands: up, down, left, and right. Wavelet scattering transformation was applied to extract the most stable features by passing the EEG dataset through a series of filtration processes. Filtration was implemented for each individual command in the EEG datasets. The proposed imagined speech-based brain wave pattern recognition approach achieved a 92.50% overall classification accuracy. This accuracy is promising for designing a trustworthy imagined speech-based brain-computer interface (BCI) future real-time systems. For better evaluation of the classification performance, other metrics were considered, and we obtained 92.74%, 92.50%, and 92.62% for precision, recall, and F1-score, respectively.

The emotional component of inner speech: A pilot exploratory fMRI study

Inner speech is one of the most important human cognitive processes. Nevertheless, until now, many aspects of inner speech, particularly the emotional characteristics of inner speech, remain poorly understood. The main objectives of our study are to identify the neural substrate for the emotional (prosodic) dimension of inner speech and brain structures that control the suppression of expression in inner speech. To achieve these goals, a pilot exploratory fMRI study was carried out on 33 people. The subjects listened to pre-recorded phrases or individual words pronounced with different emotional connotations, after which they were internally spoken with the same emotion or with suppression of expression (neutral). The results show that there is an emotional component in inner speech, which is encoded by similar structures as in spoken speech. The unique role of the caudate nuclei in the suppression of expression in the inner speech was also shown.

Can We Predict Dissociative Experiences Based on Inner Speech in Nonclinical Population by Mediating Role of Sleep Disturbance?

ABSTRACT

Dissociative experiences include various experiences and behaviors that can cause people to feel disturbed and disconnected from reality. Individuals with dissociative experiences may exhibit various symptoms, particularly in their inner speech. The present study examined how we can predict dissociative experiences based on inner speech in nonclinical populations by mediating the role of sleep disturbance. In this cross-sectional study, data were collected from university students aged 18 to 40 years ( N = 400). They were asked to complete online self-report questionnaires: Varieties of the Inner Speech Questionnaire, Dissociative Experiences Scale, and Pittsburgh Sleep Quality Index. Results showed that there was a relationship between dissociative experiences and sleep disturbance ( r = 0.29, p < 0.001), dialogic inner speech ( r = 0.39, p < 0.001), condensed inner speech ( r = 0.31, p < 0.001), other people's inner speech ( r = 0.46, p < 0.001), evaluative/motivational inner speech ( r = 0.28, p < 0.001), and total inner speech score ( r = 0.48, p < 0.001). Thus, the current study showed a significant relationship among inner speech, dissociative experiences, and sleep disturbances. Inner speech was found to predict dissociative experiences by mediating sleep disturbances in the nonclinical population. Individuals with strong dissociative experiences had high scores for inner speech and sleep disturbance. The present study highlights a new area of research and its relationship to inner speech and dissociation. Future studies could further explore this new area to validate the findings reported here and support the authors' theoretical interpretation.

Investigation of functional near-infrared spectroscopy signal quality and development of the hemodynamic phase correlation signal

Significance: There is a longstanding recommendation within the field of fNIRS to use oxygenated (HbO 2 ) and deoxygenated (HHb) hemoglobin when analyzing and interpreting results. Despite this, many fNIRS studies do focus onHbO 2 only. Previous work has shown thatHbO 2 on its own is susceptible to systemic interference and results may mostly reflect that rather than functional activation. Studies using bothHbO 2 and HHb to draw their conclusions do so with varying methods and can lead to discrepancies between studies. The combination ofHbO 2 and HHb has been recommended as a method to utilize both signals in analysis. Aim: We present the development of the hemodynamic phase correlation (HPC) signal to combineHbO 2 and HHb as recommended to utilize both signals in the analysis. We use synthetic and experimental data to evaluate how the HPC and current signals used for fNIRS analysis compare. Approach: About 18 synthetic datasets were formed using resting-state fNIRS data acquired from 16 channels over the frontal lobe. To simulate fNIRS data for a block-design task, we superimposed a synthetic task-related hemodynamic response to the resting state data. This data was used to develop an HPC-general linear model (GLM) framework. Experiments were conducted to investigate the performance of each signal at different SNR and to investigate the effect of false positives on the data. Performance was based on each signal's mean T -value across channels. Experimental data recorded from 128 participants across 134 channels during a finger-tapping task were used to investigate the performance of multiple signals [HbO 2 , HHb, HbT, HbD, correlation-based signal improvement (CBSI), and HPC] on real data. Signal performance was evaluated on its ability to localize activation to a specific region of interest. Results: Results from varying the SNR show that the HPC signal has the highest performance for high SNRs. The CBSI performed the best for medium-low SNR. The next analysis evaluated how false positives affect the signals. The analyses evaluating the effect of false positives showed that the HPC and CBSI signals reflect the effect of false positives onHbO 2 and HHb. The analysis of real experimental data revealed that the HPC and HHb signals provide localization to the primary motor cortex with the highest accuracy. Conclusions: We developed a new hemodynamic signal (HPC) with the potential to overcome the current limitations of usingHbO 2 and HHb separately. Our results suggest that the HPC signal provides comparable accuracy to HHb to localize functional activation while at the same time being more robust against false positives.

Viewing neurovascular coupling through the lens of combined EEG-fNIRS: A systematic review of current methods

Neurovascular coupling is a key physiological mechanism that occurs in the healthy human brain, and understanding this process has implications for understanding the aging and neuropsychiatric populations. Combined electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) has emerged as a promising, noninvasive tool for probing neurovascular interactions in humans. However, the utility of this approach critically depends on the methodological quality used for multimodal integration. Despite a growing number of combined EEG-fNIRS applications reported in recent years, the methodological rigor of past studies remains unclear, limiting the accurate interpretation of reported findings and hindering the translational application of this multimodal approach. To fill this knowledge gap, we critically evaluated various methodological aspects of previous combined EEG-fNIRS studies performed in healthy individuals. A literature search was conducted using PubMed and PsycINFO on June 28, 2021. Studies involving concurrent EEG and fNIRS measurements in awake and healthy individuals were selected. After screening and eligibility assessment, 96 studies were included in the methodological evaluation. Specifically, we critically reviewed various aspects of participant sampling, experimental design, signal acquisition, data preprocessing, outcome selection, data analysis, and results presentation reported in these studies. Altogether, we identified several notable strengths and limitations of the existing EEG-fNIRS literature. In light of these limitations and the features of combined EEG-fNIRS, recommendations are made to improve and standardize research practices to facilitate the use of combined EEG-fNIRS when studying healthy neurovascular coupling processes and alterations in neurovascular coupling among various populations.

Decoding Covert Speech From EEG-A Comprehensive Review

Over the past decade, many researchers have come up with different implementations of systems for decoding covert or imagined speech from EEG (electroencephalogram). They differ from each other in several aspects, from data acquisition to machine learning algorithms, due to which, a comparison between different implementations is often difficult. This review article puts together all the relevant works published in the last decade on decoding imagined speech from EEG into a single framework. Every important aspect of designing such a system, such as selection of words to be imagined, number of electrodes to be recorded, temporal and spatial filtering, feature extraction and classifier are reviewed. This helps a researcher to compare the relative merits and demerits of the different approaches and choose the one that is most optimal. Speech being the most natural form of communication which human beings acquire even without formal education, imagined speech is an ideal choice of prompt for evoking brain activity patterns for a BCI (brain-computer interface) system, although the research on developing real-time (online) speech imagery based BCI systems is still in its infancy. Covert speech based BCI can help people with disabilities to improve their quality of life. It can also be used for covert communication in environments that do not support vocal communication. This paper also discusses some future directions, which will aid the deployment of speech imagery based BCI for practical applications, rather than only for laboratory experiments.

Inner versus Overt Speech Production: Does This Make a Difference in the Developing Brain?

Studies in adults showed differential neural processing between overt and inner speech. So far, it is unclear whether inner and overt speech are processed differentially in children. The present study examines the pre-activation of the speech network in order to disentangle domain-general executive control from linguistic control of inner and overt speech production in 6- to 7-year-olds by simultaneously applying electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Children underwent a picture-naming task in which the pure preparation of a subsequent speech production and the actual execution of speech can be differentiated. The preparation phase does not represent speech per se but it resembles the setting up of the language production network. Only the fNIRS revealed a larger activation for overt, compared to inner, speech over bilateral prefrontal to parietal regions during the preparation phase. Findings suggest that the children's brain can prepare the subsequent speech production. The preparation for overt and inner speech requires different domain-general executive control. In contrast to adults, the children´s brain did not show differences between inner and overt speech when a concrete linguistic content occurs and a concrete execution is required. This might indicate that domain-specific executive control processes are still under development.