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Decoding Inner Speech Using Electrocorticography: Progress and Challenges Toward a Speech Prosthesis
2018
Frontiers in Neuroscience
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In this review article, we describe the state of the art in decoding inner speech, ranging from early acoustic sound features, to higher order speech units. ...
People that cannot communicate due to neurological disorders would benefit from a system that can infer internal speech directly from brain signals. ...
ACKNOWLEDGMENTS This article is adapted from the following doctorate thesis: Understanding and decoding imagined speech using intracranial recordings in the human brain (Martin, 2017) . ...
doi:10.3389/fnins.2018.00422
pmid:29977189
pmcid:PMC6021529
fatcat:s2b7sj7lvnhxnjsu5gviyycrt4
Feature Selection Methods for Zero-Shot Learning of Neural Activity
2017
Frontiers in Neuroinformatics
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In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included ...
imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. ...
a semantic decoding model (i.e., predicting semantic attributes from neural features values) to predict classes that were not included in the training set. ...
doi:10.3389/fninf.2017.00041
pmid:28690513
pmcid:PMC5481359
fatcat:dicurkt4lbhjtalh4cpc3ttwfi
Evidence for a deep, distributed and dynamic semantic code in human ventral anterior temporal cortex
[article]
2019
bioRxiv
pre-print
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We introduce a technique for revealing a dynamically-changing distributed code in simulated neural data, then apply it to neural signals collected from human cortex while participants named line drawings ...
Alternatively, meanings may arise as distributed neural patterns that change radically in real time as a stimulus is processed. ...
are used to decode semantic structure from ECoG data. ...
doi:10.1101/695049
fatcat:43kqz7onbrauld74vwsi7qcbgm
The Potential for a Speech Brain–Computer Interface Using Chronic Electrocorticography
2019
Neurotherapeutics
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Neural speech decoding is a comparatively new field but has shown much promise with recent studies demonstrating semantic, auditory, and articulatory decoding using electrocorticography (ECoG) and other ...
There have been many recent developments in neural decoders, neural feature extraction, and brain recording modalities facilitating BCI for the control of prosthetics and in automatic speech recognition ...
[33] was not intended to study semantics, but to study visual object recognition. In this Fig. 2 Subdural electrocorticography (ECoG). ...
doi:10.1007/s13311-018-00692-2
pmid:30617653
pmcid:PMC6361062
fatcat:6y66u77cdreb7jhku666wklyha
Distributed Representations in Memory: Insights from Functional Brain Imaging
2012
Annual Review of Psychology
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In this review, we discuss how these methods can sensitively index neural representations of perceptual and semantic content and how leverage on the engagement of distributed representations provides unique ...
Over the past decade, researchers have increasingly utilized powerful analytical tools (e.g., multivoxel pattern analysis) to decode the information represented within distributed functional magnetic resonance ...
feature space derived from the semantic properties of the nouns. ...
doi:10.1146/annurev-psych-120710-100344
pmid:21943171
pmcid:PMC4533899
fatcat:5efrqplohfatrmu2zvarkhutq4
Decoding Neural Representational Spaces Using Multivariate Pattern Analysis
2014
Annual Review of Neuroscience
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, hyperalignment, and stimulus-model-based encoding and decoding. ...
This article reviews these advances and integrates neural decoding methods into a common framework organized around the concept of high-dimensional representational spaces. 435 Annu. Rev. ...
In a stimulus representational space, each feature is a stimulus attribute, such as a physical attribute or semantic label. ...
doi:10.1146/annurev-neuro-062012-170325
pmid:25002277
fatcat:ah6sfup2mrct7bkg2kel37z3w4
Decoding visual information from high-density diffuse optical tomography neuroimaging data
2020
NeuroImage
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To assess the feasibility and performance of decoding with HD-DOT in visual cortex. ...
To establish the feasibility of decoding at the single-trial level with HD-DOT, a template matching strategy was used to decode visual stimulus position. ...
The funding sources had no involvement in study design, data collection or analysis, writing, or the decision to submit this article for publication. ...
doi:10.1016/j.neuroimage.2020.117516
pmid:33137479
pmcid:PMC8006181
fatcat:z43dqo2ribhtzct6tzr6p6bspm
Face percept formation in human ventral temporal cortex
2017
Journal of Neurophysiology
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These loci exist within a topological structure of face percept formation in the human ventral visual stream, preceded by category-nonselective activity in pericalcarine early visual areas and in concert ...
We propose that this convergence of proportional and thresholded response may identify active areas where face percepts are extracted from simple visual features. ...
APS and the journal editors take no responsibility for these materials, for the website address, or for any links to or from it.
AUTHOR CONTRIBUTIONS ...
doi:10.1152/jn.00113.2017
pmid:28814631
fatcat:pgnqznzmqbgmbl4mtsyzfyyrnq
Including measures of high gamma power can improve the decoding of natural speech from EEG
[article]
2019
bioRxiv
pre-print
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We used linear regression to investigate speech envelope and attention decoding in EEG at low frequencies, in high gamma power, and in both signals combined. ...
The aim of this study was to determine if high gamma power in scalp recorded EEG carries useful stimulus-related information, despite its reputation for having a poor signal to noise ratio. ...
"Induced Visual Illusions and Gamma Oscillations in Human 301 Primary Visual Cortex." ...
doi:10.1101/785881
fatcat:pkt6dju3mzdb5kwd3dr7pkdxga
Encoding and Decoding Models in Cognitive Electrophysiology
2017
Frontiers in Systems Neuroscience
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Frontiers in Systems Neuroscience | www.frontiersin.org ...
Frontiers in Systems Neuroscience | www.frontiersin.org FIGURE 1 | Predictive modeling overview. The general framework of predictive models consists of three steps. ...
These features are computed or derived from "real world" parameters describing the stimulus (e.g., sound pressure waveform in auditory stimuli, contrast at each pixel in visual stimuli). ...
doi:10.3389/fnsys.2017.00061
pmid:29018336
pmcid:PMC5623038
fatcat:jirumjvwvzc7zkgfcxws7v3uxm
Machine Learning Approaches to Analyze Speech-Evoked Neurophysiological Responses
2019
Journal of Speech, Language and Hearing Research
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Method Two categories of ML-based approaches are introduced: decoding models, which generate a speech stimulus output using the features from the neurophysiological responses, and encoding models, which ...
In this review, we focus on (a) a decoding model classification approach, wherein speech-evoked neurophysiological responses are classified as belonging to 1 of a finite set of possible speech events ( ...
In that study, they first constructed a spectral feature space from a database of vowel stimuli using principle component analysis. ...
doi:10.1044/2018_jslhr-s-astm-18-0244
pmid:30950746
pmcid:PMC6802895
fatcat:npfosmd5ybcx3ephiwn3zw6uwq
An Association between Auditory–Visual Synchrony Processing and Reading Comprehension: Behavioral and Electrophysiological Evidence
2017
Journal of Cognitive Neuroscience
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The perceptual system integrates synchronized auditory-visual signals in part to promote individuation of objects in cluttered environments. ...
word (orthographic-to-phonological) decoding, semantic access, working memory, and the integration of causal and inferential relationships across text. ...
decoding, semantic access, and semantic working memory. ...
doi:10.1162/jocn_a_01052
pmid:28129060
pmcid:PMC5300749
fatcat:fi5hers2fregzbpofea65lztqa
A Guide to Representational Similarity Analysis for Social Neuroscience
2019
Social Cognitive and Affective Neuroscience
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Representational similarity analysis (RSA) is a computational technique that uses pairwise comparisons of stimuli to reveal their representation in higher-order space. ...
Social neuroscience is a field that can particularly benefit from incorporating RSA techniques to explore hypotheses regarding the representation of multidimensional data, how representations can predict ...
If a stimulus can be predicted, or decoded, solely from the pattern of fMRI activity, there must be some information about that stimulus represented in the brain region where the pattern was identified ...
doi:10.1093/scan/nsz099
pmid:31989169
pmcid:PMC7057283
fatcat:ndq3ausqrzc4jfpnz5yarj4u6q
The neuroanatomic and neurophysiological infrastructure for speech and language
2014
Current Opinion in Neurobiology
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First, focusing on spatial organization in the human brain, the revised functional anatomy for speech and language is discussed. ...
This key idea from visual neuroscience was adapted for speech and language in the past 10 years [1, 2, 43, 44] . ...
The successful resetting of neuronal activity, triggered in part by stimulus-driven spikes, provides time constants (or temporal integration windows) for parsing and decoding speech signals. ...
doi:10.1016/j.conb.2014.07.005
pmid:25064048
pmcid:PMC4177440
fatcat:3oie2ja4nrdkdkdvgzzujn656m
Bimodal pilot study on inner speech decoding reveals the potential of combining EEG and fMRI
[article]
2022
bioRxiv
pre-print
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The dataset comprises 1280 trials (4 subjects, 8 stimuli = 2 categories * 4 words, and 40 trials per stimuli) in each modality. ...
The same improvement in performance for word classification (8 classes) can be observed (30.29% with combination, 22.19%, and 17.50% without). ...
The visual stimuli were presented from the stimuli computer to the participant via an Ultra HD LCD display from NordicNeuroLab 2 . ...
doi:10.1101/2022.05.24.492109
fatcat:msrspc2p6fe37otbk3tj7ohhje
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