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Joint Spatial-Spectral Feature Space Clustering for Speech Activity Detection from ECoG Signals

Vasileios G. Kanas, Iosif Mporas, Heather L. Benz, Kyriakos N. Sgarbas, Anastasios Bezerianos, Nathan E. Crone
2014 IEEE Transactions on Biomedical Engineering  
The purpose of this study was to detect speech activity automatically from electrocorticographic signals based on joint spatialfrequency clustering of the ECoG feature space.  ...  We found that the optimal frequency resolution to detect speech activity from ECoG signals was 8 Hz, achieving 98.8% accuracy by employing support vector machines (SVM) as a classifier.  ...  Such a decoder would require ECoG signals from speech motor cortex or language areas in the frontal and temporal lobes.  ... 
doi:10.1109/tbme.2014.2298897 pmid:24658248 pmcid:PMC4005607 fatcat:e3qi2xivv5ehpk6q7vpiozm7gq

Phase-amplitude coupling supports phase coding in human ECoG

Andrew J Watrous, Lorena Deuker, Juergen Fell, Nikolai Axmacher
2015 eLife  
Further inspection of PAC revealed that category specific HFA modulations occurred at different phases and frequencies of the underlying low-frequency rhythm, permitting decoding of categorical information  ...  This phenomenon of phase-amplitude coupling (PAC) is often interpreted as reflecting phase coding of neural representations, although evidence for this link is still lacking in humans.  ...  NeuroImage 2013, 66:642-647 632 633 Jacobs J, Kahana M: Neural representations of individual stimuli in humans 634 revealed by gamma-band electrocorticographic activity.  ... 
doi:10.7554/elife.07886 pmid:26308582 pmcid:PMC4579288 fatcat:zuzvleec5rgapftnpb7jniclgi

Decoding vowels and consonants in spoken and imagined words using electrocorticographic signals in humans

Xiaomei Pei, Dennis L Barbour, Eric C Leuthardt, Gerwin Schalk
2011 Journal of Neural Engineering  
While recent studies have demonstrated that brain signals can give detailed information about actual and imagined actions, such as different types of limb movements or spoken words, concrete experimental  ...  In this study, we found that it is possible to use signals recorded from the surface of the brain (electrocorticography) to discriminate the vowels and consonants embedded in spoken and in imagined words  ...  Acknowledgments This work was supported by grants from the US Army Research Office (W911NF-07-1-0415 (GS), W911NF-08-1-0216 (GS)), the NIH/NIBIB (EB006356 (GS) and EB000856 (JRW and GS)), and the James  ... 
doi:10.1088/1741-2560/8/4/046028 pmid:21750369 pmcid:PMC3772685 fatcat:fskqlfiygjdd7nphp4dbu33wti

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  
People that cannot communicate due to neurological disorders would benefit from a system that can infer internal speech directly from brain signals.  ...  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.  ...  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

Brain-to-text: decoding spoken phrases from phone representations in the brain

Christian Herff, Dominic Heger, Adriana de Pesters, Dominic Telaar, Peter Brunner, Gerwin Schalk, Tanja Schultz
2015 Frontiers in Neuroscience  
Here, we show for the first time that continuously spoken speech can be decoded into the expressed words from intracranial electrocorticographic (ECoG) recordings.Specifically, we implemented a system,  ...  Over the past decade, studies have suggested that it is feasible to recognize isolated aspects of speech from neural signals, such as auditory features, phones or one of a few isolated words.  ...  Figure 3 illustrates the different steps of decoding continuously spoken phrases from neural data. ECoG signals over time are recorded at every electrode and divided into 50 ms segments.  ... 
doi:10.3389/fnins.2015.00217 pmid:26124702 pmcid:PMC4464168 fatcat:p3z3hcaxabgbpgr6jqpcvns6le

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
The human auditory system is adept at extracting information from speech in both single-speaker and multi-speaker situations.  ...  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.  ...  "Identifying the Attended Speaker Using Electrocorticographic (ECoG) Signals."  ... 
doi:10.1101/785881 fatcat:pkt6dju3mzdb5kwd3dr7pkdxga

Spontaneous Decoding of the Timing and Content of Human Object Perception from Cortical Surface Recordings Reveals Complementary Information in the Event-Related Potential and Broadband Spectral Change

Kai J. Miller, Gerwin Schalk, Dora Hermes, Jeffrey G. Ojemann, Rajesh P. N. Rao, Olaf Sporns
2016 PLoS Computational Biology  
PLOS Computational Biology | We describe a new technique for decoding perception from electrical potentials measured from the human brain surface.  ...  signal.  ...  Here we show that the ECoG signal contains sufficient information to allow near-instantaneous identification of object categories with an accuracy comparable to that of human behavioral performance.  ... 
doi:10.1371/journal.pcbi.1004660 pmid:26820899 pmcid:PMC4731148 fatcat:a4yfipc7ivfsngqsmz7zmu6olu

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  ...  How does the human brain encode the meanings of words and objects? Most theories propose that local neural populations independently encode various semantic features.  ...  are used to decode semantic structure from ECoG data.  ... 
doi:10.1101/695049 fatcat:43kqz7onbrauld74vwsi7qcbgm

Spontaneously Reactivated Patterns in Frontal and Temporal Lobe Predict Semantic Clustering during Memory Search

J. R. Manning, M. R. Sperling, A. Sharan, E. A. Rosenberg, M. J. Kahana
2012 Journal of Neuroscience  
We studied how the similarity relations among items influence their retrieval by analyzing electrocorticographic recordings taken as 46 human neurosurgical patients studied and freely recalled lists of  ...  Thus, our work shows that differences in the neural correlates of semantic information, and how they are reactivated before recall, reveal how individuals organize and retrieve memories of words.  ...  Sensorimotor decoding inferring complex retinal images from patterns of neural activity in human visual cortex come from a series of fMRI studies.  ... 
doi:10.1523/jneurosci.5321-11.2012 pmid:22745488 pmcid:PMC3412364 fatcat:egds45uxt5ap3kb3bpi2rzxsxa

Generating Natural, Intelligible Speech From Brain Activity in Motor, Premotor, and Inferior Frontal Cortices

Christian Herff, Lorenz Diener, Miguel Angrick, Emily Mugler, Matthew C. Tate, Matthew A. Goldrick, Dean J. Krusienski, Marc W. Slutzky, Tanja Schultz
2019 Frontiers in Neuroscience  
Here, we record neural population activity in motor, premotor and inferior frontal cortices during speech production using electrocorticography (ECoG) and show that ECoG signals alone can be used to generate  ...  In our approach, which we call Brain-To-Speech, we chose subsequent units of speech based on the measured ECoG activity to generate audio waveforms directly from the neural recordings.  ...  ECoG Signal Processing To extract meaningful information from the recorded ECoG activity, we extracted logarithmic high-gamma power.  ... 
doi:10.3389/fnins.2019.01267 pmid:31824257 pmcid:PMC6882773 fatcat:s2x7lxcijvfp3afdrivxlymcvu

Word pair classification during imagined speech using direct brain recordings

Stephanie Martin, Peter Brunner, Iñaki Iturrate, José del R. Millán, Gerwin Schalk, Robert T. Knight, Brian N. Pasley
2016 Scientific Reports  
Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals.  ...  People that cannot communicate due to neurological disorders would benefit from an internal speech decoder.  ...  Decoding vowels and consonants in overt and imagined words using electrocorticographic signals in humans has shown promising results 28, 29 , and would allow generating a larger lexicon from a fewer  ... 
doi:10.1038/srep25803 pmid:27165452 pmcid:PMC4863149 fatcat:ljcbfwmlkvepnkl5le2ells4yq

Decoding Speech for Understanding and Treating Aphasia [chapter]

Brian N. Pasley, Robert T. Knight
2013 Progress in Brain Research  
The focus of this framework is a quantitative, model-based characterization of speech and language neural representations that can be used to decode, or predict, speech representations from measured brain  ...  A classification algorithm was used to decode CV category from ECoG signals recorded across STG.  ...  Recent work has also demonstrated that categorical information about CV syllables can be decoded directly from STG (Chang et al., 2010) .  ... 
doi:10.1016/b978-0-444-63327-9.00018-7 pmid:24309265 pmcid:PMC4043958 fatcat:gakyqxyphzftjhps7u4wzid5nu

Imagined speech can be decoded from low- and cross-frequency features in perceptual space [article]

Timothée Proix, Jaime Delgado Saa, Andy Christen, Stephanie Martin, Brian N. Pasley, Robert T. Knight, Xing Tian, David Poeppel, Werner K. Doyle, Orrin Devinsky, Luc H. Arnal, Pierre Mégevand (+1 others)
2021 bioRxiv   pre-print
While decoding overt speech has progressed, decoding imagined speech have met limited success, mainly because the associated neural signals are weak and variable hence difficult to decode by learning algorithms  ...  These findings demonstrate that low-frequency power and cross-frequency dynamics contain key information for imagined speech decoding, and that exploring perceptual spaces offers a promising avenue for  ...  ; doi: bioRxiv preprint Methods 368 Participants 369 Electrocorticographic (ECoG) recordings were obtained in 3 distinct studies from 13 patients  ... 
doi:10.1101/2021.01.26.428315 fatcat:u3j7e4zc4zdx5dqfxkmyxlnfc4

Thinking ahead: prediction in context as a keystone of language in humans and machines [article]

Ariel Goldstein, Zaid Zada, Eliav Buchnik, Mariano Schain, Amy Price, Bobbi Aubrey, Samuel A Nastase, Amir Feder, Dotan Emanuel, Alon Cohen, Aren Jansen, Harshvardhan Gazula (+16 others)
2020 bioRxiv   pre-print
Here we provide empirical evidence for the deep connection between autoregressive DLMs and the human language faculty using a 30-min spoken narrative and electrocorticographic (ECoG) recordings.  ...  After training, autoregressive DLMs are able to generate new 'context-aware' sentences with appropriate syntax and convincing semantics and pragmatics.  ...  At the neural level, we leveraged high-precision electrocorticographic (ECoG) recordings to demonstrate that the brain spontaneously predicts the meaning of forthcoming words, even hundreds of milliseconds  ... 
doi:10.1101/2020.12.02.403477 fatcat:h2nvxq6m75gjbagdjsxqj3qeay

Key Considerations In Designing A Speech Brain-Computer Interface

Florent Bocquelet, Thomas Hueber, Laurent Girin, Stéphan Chabardès, Blaise Yvert
2018 Zenodo  
contain information that differs from word to word.  ...  In another study, ECoG signals recorded from the speech motor cortex were also used to decode all phonemes of American English using discrete classification with a success rate of about 20% across 4 different  ... 
doi:10.5281/zenodo.1242931 fatcat:ztdpzu4g7zfvtnehqfhasvp7nu
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