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Neuroimaging is revolutionizing our ability to investigate the brain's structural and functional properties, enabling us to visualize brain activity during diverse mental processes and actions. One of the most widely used neuroimaging techniques is electroencephalography (EEG), which records electrical activity from the brain using electrodes positioned on the scalp. EEG signals capture both spatial (brain region) and temporal (time-based) data. While a high temporal resolution is achievable with EEG, spatial resolution is comparatively limited. Consequently, capturing both spatial and temporal information from EEG data to recognize mental activities remains challenging. In this paper, we represent spatial and temporal information obtained from EEG signals by transforming EEG data into sequential topographic brain maps. We then apply hybrid deep learning models to capture the spatiotemporal features of the EEG topographic images and classify imagined English words. The hybrid framework utilizes a sequential combination of three-dimensional convolutional neural networks (3DCNNs) and recurrent neural networks (RNNs). The experimental results reveal the effectiveness of the proposed approach, achieving an average accuracy of 77.8% in identifying imagined English speech. [ABSTRACT FROM AUTHOR]

Individuals facing verbal communication impairments resulting from brain disorders like paralysis or autism encounter significant challenges when unable to articulate speech. This research proposes the design and development of a wearable system capable of decoding imagined speech using electroencephalogram (EEG) signals obtained during the mental process of speech generation. The system's main objective is to offer an alternative communication method for individuals who can hear and think but face challenges in articulating their thoughts verbally. The design suggested includes userfriendliness, wearability, and comfort for seamless integration into daily life. A minimal number of electrodes are strategically placed on the scalp to minimize invasiveness. Achieving precise localization of the cortical areas responsible for generating the EEG patterns during imagined speech is vital for accurate decoding. Literature studies are utilized to determine the cortical positions associated with speech processing. Due to the inherent limitations in EEG spatial resolution, meticulous experiments are conducted to map the scalp positions onto their corresponding cortical counterparts. Specifically, we focus on identifying the scalp location over the superior temporal gyrus (T3) using the internationally recognized 10-20 electrode placement system by employing a circular periphery movement with a 2 cm distance increment. Our research involves nine subjects spanning various age groups, with the youngest being 23 and the oldest 65. Each participant undergoes ten iterations, during which they imagine six Marathi syllables. Our work contributes to the development of wearable assistive technology, enabling mute individuals to communicate effectively by translating their imagined speech into actionable commands. This innovation ultimately enhances their social participation and overall well-being. [ABSTRACT FROM AUTHOR]

Speech involves the synchronization of the brain and the oral articulators. Inner speech, also known as imagined speech or covert speech, refers to thinking in the form of sound without intentional movement of the lips, tongue, or hands. Decoding human thoughts is a powerful technique that can help individuals who have lost the ability to speak. This paper introduces a high-performance brain wave decoder based on inner speech, using a novel feature extraction method. The approach combined Support Vector Machine (SVM) and multi-wavelet feature extraction techniques to decode two EEG-based inner speech datasets (Data 1 and Data 2) into internally spoken words. The proposed approach achieved an overall classification accuracy of 68.20%, precision of 68.22%, recall of 68.20%, and F1-score of 68.21% for Data 1, and accuracy of 97.5%, precision of 97.73%, recall of 97.50%, and F1-score of 97.61% for Data 2. Additionally, the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) demonstrated the validity of the proposed approach for classifying inner speech commands by achieving a macro-average of 78.76% and 99.32% for Data 1 and Data 2, respectively. The EEG-based inner speech classification method proposed in this research has the potential to improve communication for patients with speech disorders, mutism, cognitive development issues, executive function problems, and mental disorder.

Neuroimaging is revolutionizing our ability to investigate the brain’s structural and functional properties, enabling us to visualize brain activity during diverse mental processes and actions. One of the most widely used neuroimaging techniques is electroencephalography (EEG), which records electrical activity from the brain using electrodes positioned on the scalp. EEG signals capture both spatial (brain region) and temporal (time-based) data. While a high temporal resolution is achievable with EEG, spatial resolution is comparatively limited. Consequently, capturing both spatial and temporal information from EEG data to recognize mental activities remains challenging. In this paper, we represent spatial and temporal information obtained from EEG signals by transforming EEG data into sequential topographic brain maps. We then apply hybrid deep learning models to capture the spatiotemporal features of the EEG topographic images and classify imagined English words. The hybrid framework utilizes a sequential combination of three-dimensional convolutional neural networks (3DCNNs) and recurrent neural networks (RNNs). The experimental results reveal the effectiveness of the proposed approach, achieving an average accuracy of 77.8% in identifying imagined English speech.

IntroductionParalyzed and physically impaired patients face communication difficulties, even when they are mentally coherent and aware. Electroencephalographic (EEG) brain–computer interfaces (BCIs) offer a potential communication method for these people without invasive surgery or physical device controls.MethodsAlthough virtual keyboard protocols are well documented in EEG BCI paradigms, these implementations are visually taxing and fatiguing. All English words combine 44 unique phonemes, each corresponding to a unique EEG pattern. In this study, a complete phoneme-based imagined speech EEG BCI was developed and tested on 16 subjects.ResultsUsing open-source hardware and software, machine learning models, such as k-nearest neighbor (KNN), reliably achieved a mean accuracy of 97 ± 0.001%, a mean F1 of 0.55 ± 0.01, and a mean AUC-ROC of 0.68 ± 0.002 in a modified one-versus-rest configuration, resulting in an information transfer rate of 304.15 bits per minute. In line with prior literature, the distinguishing feature between phonemes was the gamma power on channels F3 and F7.DiscussionHowever, adjustments to feature selection, trial window length, and classifier algorithms may improve performance. In summary, these are iterative changes to a viable method directly deployable in current, commercially available systems and software. The development of an intuitive phoneme-based EEG BCI with open-source hardware and software demonstrates the potential ease with which the technology could be deployed in real-world applications.

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. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated that it is possible to decode and synthesize various aspects of acoustic speech directly from intracranial measurements of electrophysiological brain activity. In order to continue progressing toward the development of a practical speech neuroprosthesis for the individuals with speech impairments, better understanding and modeling of imagined speech processes are required. The present study uses intracranial brain recordings from participants that performed a speaking task with trials consisting of overt, mouthed, and imagined speech modes, representing various degrees of decreasing behavioral output. Speech activity detection models are constructed using spatial, spectral, and temporal brain activity features, and the features and model performances are characterized and compared across the three degrees of behavioral output. The results indicate the existence of a hierarchy in which the relevant channels for the lower behavioral output modes form nested subsets of the relevant channels from the higher behavioral output modes. This provides important insights for the elusive goal of developing more effective imagined speech decoding models with respect to the better-established overt speech decoding counterparts.

The electroencephalogram (EEG) based brain-computer interface (BCI) system employing imagined speech serves as a mechanism for decoding EEG signals to facilitate control over external devices or communication with the external world at the moment the user desires. To effectively deploy such BCIs, it is imperative to accurately discern various brain states from continuous EEG signals when users initiate word imagination. This study involved the acquisition of EEG signals from 15 subjects engaged in four states: resting, listening, imagined speech, and actual speech, each involving a predefined set of 10 words. The EEG signals underwent preprocessing, segmentation, spatio-temporal and spectral analysis of each state, and functional connectivity analysis using the phase locking value (PLV) method. Subsequently, five features were extracted from the frequency and time-frequency domains. Classification tasks were performed using four machine learning algorithms in both pair-wise and multiclass scenarios, considering subject-dependent and subject-independent data. In the subject-dependent scenario, the random forest (RF) classifier achieved a maximum accuracy of 94.60 % for pairwise classification, while the artificial neural network (ANN) classifier achieved a maximum accuracy of 66.92 % for multiclass classification. In the subject-independent scenario, the random forest (RF) classifier achieved maximum accuracies of 81.02 % for pairwise classification and 55.58 % for multiclass classification. Moreover, EEG signals were classified based on frequency bands and brain lobes, revealing that the theta (θ) and delta (δ) bands, as well as the frontal and temporal lobes, are sufficient for distinguishing between brain states. The findings promise to develop a system capable of automatically segmenting imagined speech segments from continuous EEG signals. • Explored four brain states: rest, listening, imagined speech, and actual speech. • Identified key differences in brain activity across different brain states using spatio-temporal, spectral, and functional analyses. • Achieved reasonable accuracy for classifying brain states with both subject-dependent and subject-independent models. • Identified the significance of θ and δ bands and the role of the frontal and temporal lobes in distinguishing brain states. [ABSTRACT FROM AUTHOR]

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.

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. Features from each subnet are fused before further feature extraction and classification. Nineteen participants performed overt and imagined speech in a novel cue-based paradigm enabling investigation of stimulus and linguistic effects on decoding. Results: Using the hybrid approach, classification accuracies (46.31% and 34.29% for overt and imagined speech, respectively (chance: 25%)) indicated a significant improvement on EEG used independently for imagined speech (p = 0.020) while tending towards significance for overt speech (p = 0.098). In comparison with fNIRS, significant improvements for both speech-types were achieved with bimodal decoding (p<0.001). There was a mean difference of ∼12.02% between overt and imagined speech with accuracies as high as 87.18% and 53%. Deeper subnets enhanced performance while stimulus effected overt and imagined speech in significantly different ways. Conclusion: The bimodal approach was a significant improvement on unimodal results for several tasks. Results indicate the potential of multi-modal deep learning for enhancing neural signal decoding. Significance: This novel architecture can be used to enhance speech decoding from bimodal neural signals. [ABSTRACT FROM AUTHOR]

Currently, the most used method to measure brain activity under a non-invasive procedure is the electroencephalogram (EEG). This is because of its high temporal resolution, ease of use, and safety. These signals can be used under a Brain Computer Interface (BCI) framework, which can be implemented to provide a new communication channel to people that are unable to speak due to motor disabilities or other neurological diseases. Nevertheless, EEG-based BCI systems have presented challenges to be implemented in real life situations for imagined speech recognition due to the difficulty to interpret EEG signals because of their low signal-to-noise ratio (SNR). As consequence, in order to help the researcher make a wise decision when approaching this problem, we offer a review article that sums the main findings of the most relevant studies on this subject since 2009. This review focuses mainly on the pre-processing, feature extraction, and classification techniques used by several authors, as well as the target vocabulary. Furthermore, we propose ideas that may be useful for future work in order to achieve a practical application of EEG-based BCI systems toward imagined speech decoding. [ABSTRACT FROM AUTHOR]

We present a transfer learning-based approach for decoding imagined speech from electroencephalogram (EEG). Features are extracted simultaneously from multiple EEG channels, rather than separately from individual channels. This helps in capturing the interrelationships between the cortical regions. To alleviate the problem of lack of enough data for training deep networks, sliding window-based data augmentation is performed. Mean phase coherence and magnitude-squared coherence, two popular measures used in EEG connectivity analysis, are used as features. These features are compactly arranged, exploiting their symmetry, to obtain a three dimensional “image-like” representation. The three dimensions of this matrix correspond to the alpha, beta and gamma EEG frequency bands. A deep network with ResNet50 as the base model is used for classifying the imagined prompts. The proposed method is tested on the publicly available ASU dataset of imagined speech EEG, comprising four different types of prompts. The accuracy of decoding the imagined prompt varies from a minimum of 79.7% for vowels to a maximum of 95.5% for short-long words across the various subjects. The accuracies obtained are better than the state-of-the-art methods, and the technique is good in decoding prompts of different complexities.

Currently, the most used method to measure brain activity under a non-invasive procedure is the electroencephalogram (EEG). This is because of its high temporal resolution, ease of use, and safety. These signals can be used under a Brain Computer Interface (BCI) framework, which can be implemented to provide a new communication channel to people that are unable to speak due to motor disabilities or other neurological diseases. Nevertheless, EEG-based BCI systems have presented challenges to be implemented in real life situations for imagined speech recognition due to the difficulty to interpret EEG signals because of their low signal-to-noise ratio (SNR). As consequence, in order to help the researcher make a wise decision when approaching this problem, we offer a review article that sums the main findings of the most relevant studies on this subject since 2009. This review focuses mainly on the pre-processing, feature extraction, and classification techniques used by several authors, as well as the target vocabulary. Furthermore, we propose ideas that may be useful for future work in order to achieve a practical application of EEG-based BCI systems toward imagined speech decoding.

With the advent of new algorithms, brain-computer interfacing has been extensively used in medical and non-medical fields. In this regard, an experiment was conducted by the authors to recognize the imagined speech, the results of which are reported in this paper. This work can act as a speech prosthesis for completely paralyzed patients who cannot communicate normally. Thirteen subjects imagined five English words (sos, stop, medicine, comehere, washroom) while their electroencephalogram (EEG) signals were recorded simultaneously. The word pairs were analyzed in six natural frequencies of the brain. The envelopes of analytical signals acquired from Hilbert transform were calculated for all the frequency bands and the resulting features were classified using seven classifiers. The maximum accuracy reached up to 88.36%. The experimental study showed that alpha and theta frequency bands were able to classify the highest amount of imagined speech with a maximum average accuracy of 72.73% and 69.41% respectively. The results were comparable to state-of-the-art methods. The findings reported in this work will encourage the research community to use non-invasive modalities like EEG for exploring more in this area. [ABSTRACT FROM AUTHOR]

Interpretation of neural signals to a form that is as intelligible as speech facilitates the development of communication mediums for the otherwise speech/motor-impaired individuals. Speech perception, production, and imagination often constitute phases of human communication. The primary goal of this article is to analyze the similarity between these three phases by studying electroencephalogram(EEG) patterns across these modalities, in order to establish their usefulness for brain computer interfaces. Neural decoding of speech using such non-invasive techniques necessitates the optimal choice of signal analysis and translation protocols. By employing selection-by-exclusion based temporal modeling algorithms, we discover fundamental syllable-like units that reveal similar set of signal signatures across all the three phases. Significantly higher than chance accuracies are recorded for single trial multi-unit EEG classification using machine learning approaches over three datasets across 30 subjects. Repeatability and subject independence tests performed at every step of the analysis further strengthens the findings and holds promise for translating brain signals to speech non-invasively.

Imagined speech can be used to send commands without any muscle movement or emitting audio. The current status of research is in the early stage, and there is a shortage of open-access datasets for imagined speech analysis. We have proposed an openly accessible electroencephalograph (EEG) dataset for six imagined words in this work. We have selected six phonetically distributed, monosyllabic, and emotionally neutral words from W-22 CID word lists. The phonetic distribution of words consisted of the different places of consonants’ articulation and different positions of tongue advancement for vowel pronunciation. The selected words were “could,” “yard,” “give,” “him,” “there,” and “toe.” The experiment was performed over 15 subjects who performed the overt and imagined speech task for the displayed word. Each word was presented 50 times in random order. EEG signals were recorded during the experiment using a 64-channel EEG acquisition system with a sampling rate of 2,048 Hz. A preliminary analysis of the recorded data is presented by performing the classification of EEGs corresponding to the imagined words. The achieved accuracy is above the chance level for all subjects, which suggests that the recorded EEGs contain distinctive information about the imagined words.

The use of brain-computer interfaces to produce imagined speech from brain waves has the potential to assist individuals with difficulty producing speech or communicating silently. The decoding of covert speech has been observed to have limited efficacy due to the diverse nature of the associated measured brain waves and the limited number of covert speech databases. As a result, traditional machine learning algorithms for learning and inference are challenging, and one of the real alternatives could be to leverage transfer of learning. The main goals of this research were to create a new deep learning (DL) framework for decoding imagined speech electroencephalography (EEG) signals tasks using transfer learning and to transfer the model learning of the source task of an imagined speech EEG dataset to the model training on the target task of another imagined speech EEG dataset, essentially the cross-task learning transfer of discriminative characteristics of the source task to the target task of imagined speech. The experiment was carried out using two distinct open-access EEG datasets, FEIS and KaraOne, that recorded the imagined speech classes of neural signals from multiple individuals. The target FEIS model and the target KaraOne model for multiclass classification exhibit overall accuracy of 89.01% and 82.35%, respectively, according to the proposed transfer learning. The experiment results indicate that the cross-task deep transfer learning design reliably classifies the imagined speech EEG signals by applying the source task learning to the target task learning. The findings suggest the feasibility of a consistent strategy for classifying multiclass imagined speech with transfer learning, which could thereby open up the possibility of future investigation into cross-task imagined speech classification knowledge usability for generalization of new imagined speech prompts.

In this paper we propose a framework using multi-channel convolutional neural network (MC–CNN) for recognizing the grammatical class (verb or noun) of covertly-spoken words from electroencephalogram (EEG) signals. Our proposed network extracts features by taking into account spatial, temporal, and spectral properties of the EEG signal. Further, sets of signals acquired from different regions of the brain are processed separately within the proposed framework and are subsequently combined at the classification stage. This approach enables the network to effectively learn discriminative features from the locations of the brain where imagined speech is processed. Our network was tested using challenging experiments, including cases where the test subject did not take part in system training. In our main application scenario, where no instance of a specific noun or verb was used during training, our method achieved 85.7% recognition. Further, our proposed method was evaluated on a publicly available EEG dataset and achieved recognition rate of 93.8% in binary classification. These results demonstrate the potential of our method. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

Imagined speech is the inner pronunciation of words (unspoken speech, silent speech, or covert speech) without emitting sounds or making movements of face. Speech is the simple, normal and effective way people can communicate with one another. But some brain injuries caused by brain stroke, traumatic brain injuries, brain paralyses, stroke and ALS (amyotrophic lateral sclerosis) restrict vocal speech, even when the patient is completely conscious. People that cannot communicate due to neural conditions will benefit from a device that can specifically infer brain impulses from internal expression. For this speech impaired population, Brain Computer Interface(BCI) may be a possible communication alternative. A Brain Computer Interface that uses brain impulses to manipulate electronic equipment by a detection of complex variations of brain electrical activity needs to be applied in order to provide quiet communication skills between the two individuals to gather data relating to imaginary talk. This study document focuses on BCI research on the identification of imagined expression and explanations of end-to-end EEG signal processing methodology. [ABSTRACT FROM AUTHOR]

• We proposed a D-UDA method for cross-subject EEG-based imagined speech recognition. • A novel loss is introduced to refine decision boundaries from target subject data. • The proposed method may build an effective classifier over a target subject. • Our proposal outperforms to other D-UDA methods on two imagined speech datasets. Recent advances in imagined speech recognition from EEG signals have shown their capability of enabling a new natural form of communication, which is posed to improve the lives of subjects with motor disabilities. However, differences among subjects may be an obstacle to the applicability of a previously trained classifier to new users, since a significant amount of labeled samples must be acquired for each new user, making this process tedious and time-consuming. In this sense, unsupervised domain adaptation (UDA) methods, especially those based on deep learning (D-UDA), arise as a potential solution to address this issue by reducing the differences among feature distributions of subjects. It has been shown that the divergence in the marginal and conditional distributions must be reduced to encourage similar feature distributions. However, current D-UDA methods may become sensitive under adaptation scenarios where a low discriminative feature space among classes is given, reducing the accuracy performance of the classifier. To address this issue, we introduce a D-UDA method, named Standardization-Refinement Domain Adaptation (SRDA), which combines Adaptive Batch Normalization (AdaBN) with a novel loss function based on the variation of information (VOI), in order to build an adaptive classifier on EEG data corresponding to imagined speech. Our proposal, applied over two imagined speech datasets, resulted in SRDA outperforming standard classifiers for BCI and existing D-UDA methods, achieving accuracy performances of 61.02 ± 08.14% and 62.99 ± 04.78%, assessed using leave-one-out cross-validation. [ABSTRACT FROM AUTHOR]

Purpose: The present study which addressed adults who stutter (AWS) attempted to investigate power spectral dynamics in the stuttering state by answering the questions using quantitative electroencephalography (qEEG). Method: A 64-channel electroencephalography (EEG) setup was used for data acquisition at 20 AWS. Since the speech, especially stuttering, causes significant noise in the EEG, 2 conditions of speech preparation (SP) and imagined speech (IS) were considered. EEG signals were decomposed into 6 bands. The corresponding sources were localized using the standard low-resolution electromagnetic tomography (sLORETA) tool in both fluent and dysfluent states. Results: Significant differences were noted after analyzing the time-locked EEG signals in fluent and dysfluent utterances. Consistent with previous studies, poor alpha and beta suppression in SP and IS conditions were localized in the left frontotemporal areas in a dysfluent state. This was partly true for the right frontal regions. In the theta range, disfluency was concurrence with increased activation in the left and right motor areas. Increased delta power in the left and right motor areas as well as increased beta2 power over left parietal regions was notable EEG features upon fluent speech. Conclusion: Based on the present findings and those of earlier studies, explaining the neural circuitries involved in stuttering probably requires an examination of the entire frequency spectrum involved in speech., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Brain-computer interface (BCI) is oriented toward intuitive systems that users can easily operate. Imagined speech and visual imagery are emerging paradigms that can directly convey a user’s intention. We investigated the underlying characteristics that affect the decoding performance of these two paradigms. Twenty-two subjects performed imagined speech and visual imagery of twelve words/phrases frequently used for patients’ communication. Spectral features were analyzed with thirteen-class classification (including rest class) using EEG filtered in six frequency ranges. In addition, cortical regions relevant to the two paradigms were analyzed by classification using single-channel and pre-defined cortical groups. Furthermore, we analyzed the word properties that affect the decoding performance based on the number of syllables, concrete and abstract concepts, and the correlation between the two paradigms. Finally, we investigated multiclass scalability in both paradigms. The high-frequency band displayed a significantly superior performance to that in the case of any other spectral features in the thirteen-class classification (imagined speech: 39.73 ± 5.64%; visual imagery: 40.14 ± 4.17%). Furthermore, the performance of Broca’s and Wernicke’s areas and auditory cortex was found to have improved among the cortical regions in both paradigms. As the number of classes increased, the decoding performance decreased moderately. Moreover, every subject exceeded the confidence level performance, implying the strength of the two paradigms in BCI inefficiency. These two intuitive paradigms were found to be highly effective for multiclass communication systems, having considerable similarities between each other. The results could provide crucial information for improving the decoding performance for practical BCI applications. [ABSTRACT FROM AUTHOR]

• EEG oscillations during imagined speech synchronize with the overt speech envelope. • This synchronization was observed around the EEG electrodes in the frontal region. • EEG during imagined speech classified speech with different envelopes reliably. Neural oscillations synchronize with the periodicity of external stimuli such as the rhythm of the speech amplitude envelope. This synchronization induces a speech-specific, replicable neural phase pattern across trials and enables perceived speech to be classified. In this study, we hypothesized that neural oscillations during articulatory imagination of speech could also synchronize with the rhythm of speech imagery. To validate the hypothesis, after replacing the imagined speech with overt speech due to the physically unobservable nature of imagined speech, we investigated (1) whether the EEG-based regressed speech envelopes correlate with the overt speech envelope and (2) whether EEG during the imagined speech can classify speech stimuli with different envelopes. The variability of the duration of the imagined speech across trials was corrected using dynamic time warping. The classification was based on the distance between a test data and a template waveform of each class. Results showed a significant correlation between the EEG-based regressed envelope and the overt speech envelope. The average classification accuracy was 38.5%, which is significantly above the rate of chance (33.3%). These results demonstrate the synchronization between EEG during the imagined speech and the envelope of the overt counterpart. [ABSTRACT FROM AUTHOR]

The use of imagined speech with electroencephalographic (EEG) signals is a promising field of brain-computer interfaces (BCI) that seeks communication between areas of the cerebral cortex related to language and devices or machines. However, the complexity of this brain process makes the analysis and classification of this type of signals a relevant topic of research. The goals of this study were: to develop a new algorithm based on Deep Learning (DL), referred to as CNNeeg1-1, to recognize EEG signals in imagined vowel tasks; to create an imagined speech database with 50 subjects specialized in imagined vowels from the Spanish language (/a/,/e/,/i/,/o/,/u/); and to contrast the performance of the CNNeeg1-1 algorithm with the DL Shallow CNN and EEGNet benchmark algorithms using an open access database (BD1) and the newly developed database (BD2). In this study, a mixed variance analysis of variance was conducted to assess the intra-subject and inter-subject training of the proposed algorithms. The results show that for intra-subject training analysis, the best performance among the Shallow CNN, EEGNet, and CNNeeg1-1 methods in classifying imagined vowels (/a/,/e/,/i/,/o/,/u/) was exhibited by CNNeeg1-1, with an accuracy of 65.62% for BD1 database and 85.66% for BD2 database.

Decoding speech from intracranial recordings serves two main purposes: understanding the neural correlates of speech processing and decoding speech features for targeting speech neuroprosthetic devices. Intracranial recordings have high spatial and temporal resolution, and thus offer a unique opportunity to investigate and decode the electrophysiological dynamics underlying speech processing. In this review article, we describe current approaches to decoding different features of speech perception and production - such as spectrotemporal, phonetic, phonotactic, semantic, and articulatory components - using intracranial recordings. A specific section is devoted to the decoding of imagined speech, and potential applications to speech prosthetic devices. We outline the challenges in decoding human language, as well as the opportunities in scientific and neuroengineering applications. [ABSTRACT FROM AUTHOR]

Highlights • Bag of Features is able the discrimination of imagined speech in electroencephalograms. • Bag of Features aids the transfer learning of imagined speech to recognize new imagined words. • Using time domain features of the electroencephalogram for a Bag of Features model achieves a competitive classification performance. Abstract The Brain–Computer Interfaces (BCI) based on electroencephalograms (EEG) are systems which aim is to provide a communication channel to any person with a computer, initially it was proposed to aid people with disabilities, but actually wider applications have been proposed. These devices allow to send messages or to control devices using the brain signals. There are different neuro-paradigms which evoke brain signals of interest for such purposes. Imagined speech is one of the most recent paradigms, and it is explored in this work, it consists of the internal pronunciation of a word, i.e. a subject imagines the utterance of a word without emitting sounds or articulating facial movements. Under this neuro-paradigm, to increase the initial vocabulary reducing drastically the training time using few or none new data is an open challenge. The proposed method extracts characteristic units (i.e. codewords) of the EEGs associated with the words of an initial vocabulary. Subsequently, a new imagined word is represented with these codewords, and then a classification algorithm is applied. The method was tested both, with and without calibration examples, in a 27 subjects dataset. An initial vocabulary of 4 words, with 33 epochs for each word was considered. The results were obtained by averaging the accuracies of every subject, without calibration data a 65.65% accuracy was achieved. In comparison to the baseline method, which obtained an average accuracy of 68.9%, the proposed method showed no statistical difference. [ABSTRACT FROM AUTHOR]

Highlights • An EEG-based imagined speech system was assessed for subjects recognition. • More than one word can be used as a password without decreasing the method performance. • Impact of instances/channels reduction and feature extraction techniques were studied. Abstract Due to the problems presented in current traditional/biometric security systems, the interest to use new security systems, have been increasing. This paper explores the use of brain signals EEG-based during imagined speech in order to use it as a new biometric measure for Subjects identification and thus create a new biometric security system. The main contribution of this paper are two methods for feature extraction, first to improve the signal-to-noise ratio the Common Average Reference was applied. The first method was based on Discrete Wavelet Transform, and the second method was based on statistical features directly from the raw signal. The proposed methods were tested in a dataset of 27 Subjects who performed 33 repetitions of 5 imagined words in Spanish. The results show the feasibility of the task with accurate identification of the Subject, regardless of the imagined word used and using a commercial EEG system (EMOTIV EPOC). In addition, the scope of the method is displayed by decreasing the training data, as well as the number of active sensors for the identification task. Using the proposed method with future improvements and implementing it in a low-cost device can be a new and valuable biometric security system. [ABSTRACT FROM AUTHOR]