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Decoding Inner Speech Using Electrocorticography: Progress and Challenges Toward a Speech Prosthesis

Stephanie Martin, Iñaki Iturrate, José del R. Millán, Robert T. Knight, Brian N. Pasley
2018 Frontiers in Neuroscience  
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

Carlos A. Caceres, Matthew J. Roos, Kyle M. Rupp, Griffin Milsap, Nathan E. Crone, Michael E. Wolmetz, Christopher R. Ratto
2017 Frontiers in Neuroinformatics  
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]

Timothy T Rogers, Christopher Cox, Qihong Lu, Akihiro Shimotake, Takayuki Kikuch, Takeharu Kunieda, Susumu Miyamoto, Ryosuke Takahashi, Akio Ikeda, Riki Matsumoto, Matthew A Lambon Ralph
2019 bioRxiv   pre-print
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

Qinwan Rabbani, Griffin Milsap, Nathan E. Crone
2019 Neurotherapeutics  
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

Jesse Rissman, Anthony D. Wagner
2012 Annual Review of Psychology  
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

James V. Haxby, Andrew C. Connolly, J. Swaroop Guntupalli
2014 Annual Review of Neuroscience  
, 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

Kalyan Tripathy, Zachary E. Markow, Andrew K. Fishell, Arefeh Sherafati, Tracy M. Burns-Yocum, Mariel L. Schroeder, Alexandra M. Svoboda, Adam T. Eggebrecht, Mark A. Anastasio, Bradley L. Schlaggar, Joseph P. Culver
2020 NeuroImage  
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

Kai J. Miller, Dora Hermes, Franco Pestilli, Gagan S. Wig, Jeffrey G. Ojemann
2017 Journal of Neurophysiology  
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]

Shyanthony R. Synigal, Emily S. Teoh, Edmund C. Lalor
2019 bioRxiv   pre-print
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

Christopher R. Holdgraf, Jochem W. Rieger, Cristiano Micheli, Stephanie Martin, Robert T. Knight, Frederic E. Theunissen
2017 Frontiers in Systems Neuroscience  
Frontiers in Systems Neuroscience |  ...  Frontiers in Systems Neuroscience | 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

Zilong Xie, Rachel Reetzke, Bharath Chandrasekaran
2019 Journal of Speech, Language and Hearing Research  
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

Julia Mossbridge, Jacob Zweig, Marcia Grabowecky, Satoru Suzuki
2017 Journal of Cognitive Neuroscience  
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  
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

David Poeppel
2014 Current Opinion in Neurobiology  
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]

Foteini Simistira Liwicki, Vibha Gupta, Rajkumar Saini, Kanjar De, Nosheen Abid, Sumit Rakesh, Scott Wellington, Holly Wilson, Marcus Liwicki, Johan Eriksson
2022 bioRxiv   pre-print
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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