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Wu X, Wellington S, Fu Z, Zhang D. Speech decoding from stereo-electroencephalography (sEEG) signals using advanced deep learning methods. J Neural Eng 2024; 21:036055. [PMID: 38885688 DOI: 10.1088/1741-2552/ad593a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 06/17/2024] [Indexed: 06/20/2024]
Abstract
Objective.Brain-computer interfaces (BCIs) are technologies that bypass damaged or disrupted neural pathways and directly decode brain signals to perform intended actions. BCIs for speech have the potential to restore communication by decoding the intended speech directly. Many studies have demonstrated promising results using invasive micro-electrode arrays and electrocorticography. However, the use of stereo-electroencephalography (sEEG) for speech decoding has not been fully recognized.Approach.In this research, recently released sEEG data were used to decode Dutch words spoken by epileptic participants. We decoded speech waveforms from sEEG data using advanced deep-learning methods. Three methods were implemented: a linear regression method, an recurrent neural network (RNN)-based sequence-to-sequence model (RNN), and a transformer model.Main results.Our RNN and transformer models outperformed the linear regression significantly, while no significant difference was found between the two deep-learning methods. Further investigation on individual electrodes showed that the same decoding result can be obtained using only a few of the electrodes.Significance.This study demonstrated that decoding speech from sEEG signals is possible, and the location of the electrodes is critical to the decoding performance.
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Affiliation(s)
- Xiaolong Wu
- Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Scott Wellington
- Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Zhichun Fu
- Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Dingguo Zhang
- Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
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2
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Komeiji S, Mitsuhashi T, Iimura Y, Suzuki H, Sugano H, Shinoda K, Tanaka T. Feasibility of decoding covert speech in ECoG with a Transformer trained on overt speech. Sci Rep 2024; 14:11491. [PMID: 38769115 PMCID: PMC11106343 DOI: 10.1038/s41598-024-62230-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 05/15/2024] [Indexed: 05/22/2024] Open
Abstract
Several attempts for speech brain-computer interfacing (BCI) have been made to decode phonemes, sub-words, words, or sentences using invasive measurements, such as the electrocorticogram (ECoG), during auditory speech perception, overt speech, or imagined (covert) speech. Decoding sentences from covert speech is a challenging task. Sixteen epilepsy patients with intracranially implanted electrodes participated in this study, and ECoGs were recorded during overt speech and covert speech of eight Japanese sentences, each consisting of three tokens. In particular, Transformer neural network model was applied to decode text sentences from covert speech, which was trained using ECoGs obtained during overt speech. We first examined the proposed Transformer model using the same task for training and testing, and then evaluated the model's performance when trained with overt task for decoding covert speech. The Transformer model trained on covert speech achieved an average token error rate (TER) of 46.6% for decoding covert speech, whereas the model trained on overt speech achieved a TER of 46.3% ( p > 0.05 ; d = 0.07 ) . Therefore, the challenge of collecting training data for covert speech can be addressed using overt speech. The performance of covert speech can improve by employing several overt speeches.
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Affiliation(s)
- Shuji Komeiji
- Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo, 184-8588, Japan
| | - Takumi Mitsuhashi
- Department of Neurosurgery, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Yasushi Iimura
- Department of Neurosurgery, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Hiroharu Suzuki
- Department of Neurosurgery, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Hidenori Sugano
- Department of Neurosurgery, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Koichi Shinoda
- Department of Computer Science, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan
| | - Toshihisa Tanaka
- Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo, 184-8588, Japan.
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Yu L, Dugan P, Doyle W, Devinsky O, Friedman D, Flinker A. A left-lateralized dorsolateral prefrontal network for naming. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.15.594403. [PMID: 38798614 PMCID: PMC11118423 DOI: 10.1101/2024.05.15.594403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The ability to connect the form and meaning of a concept, known as word retrieval, is fundamental to human communication. While various input modalities could lead to identical word retrieval, the exact neural dynamics supporting this convergence relevant to daily auditory discourse remain poorly understood. Here, we leveraged neurosurgical electrocorticographic (ECoG) recordings from 48 patients and dissociated two key language networks that highly overlap in time and space integral to word retrieval. Using unsupervised temporal clustering techniques, we found a semantic processing network located in the middle and inferior frontal gyri. This network was distinct from an articulatory planning network in the inferior frontal and precentral gyri, which was agnostic to input modalities. Functionally, we confirmed that the semantic processing network encodes word surprisal during sentence perception. Our findings characterize how humans integrate ongoing auditory semantic information over time, a critical linguistic function from passive comprehension to daily discourse.
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Affiliation(s)
- Leyao Yu
- Department of Biomedical Engineering, New York University, New York, 10016, New York, the United States
- Department of Neurology, School of Medicine, New York University, New York, 10016, New York, the United States
| | - Patricia Dugan
- Department of Neurology, School of Medicine, New York University, New York, 10016, New York, the United States
| | - Werner Doyle
- Department of Neurosurgery, School of Medicine, New York University, New York, 10016, New York, the United States
| | - Orrin Devinsky
- Department of Neurology, School of Medicine, New York University, New York, 10016, New York, the United States
| | - Daniel Friedman
- Department of Neurology, School of Medicine, New York University, New York, 10016, New York, the United States
| | - Adeen Flinker
- Department of Biomedical Engineering, New York University, New York, 10016, New York, the United States
- Department of Neurology, School of Medicine, New York University, New York, 10016, New York, the United States
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Schroën JAM, Gunter TC, Numssen O, Kroczek LOH, Hartwigsen G, Friederici AD. Causal evidence for a coordinated temporal interplay within the language network. Proc Natl Acad Sci U S A 2023; 120:e2306279120. [PMID: 37963247 PMCID: PMC10666120 DOI: 10.1073/pnas.2306279120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 10/06/2023] [Indexed: 11/16/2023] Open
Abstract
Recent neurobiological models on language suggest that auditory sentence comprehension is supported by a coordinated temporal interplay within a left-dominant brain network, including the posterior inferior frontal gyrus (pIFG), posterior superior temporal gyrus and sulcus (pSTG/STS), and angular gyrus (AG). Here, we probed the timing and causal relevance of the interplay between these regions by means of concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG). Our TMS-EEG experiments reveal region- and time-specific causal evidence for a bidirectional information flow from left pSTG/STS to left pIFG and back during auditory sentence processing. Adapting a condition-and-perturb approach, our findings further suggest that the left pSTG/STS can be supported by the left AG in a state-dependent manner.
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Affiliation(s)
- Joëlle A. M. Schroën
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig04103, Germany
| | - Thomas C. Gunter
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig04103, Germany
| | - Ole Numssen
- Methods and Development Group Brain Networks, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig04103, Germany
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig04103, Germany
| | - Leon O. H. Kroczek
- Department of Psychology, Clinical Psychology and Psychotherapy, Universität Regensburg, Regensburg93053, Germany
| | - Gesa Hartwigsen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig04103, Germany
- Cognitive and Biological Psychology, Wilhelm Wundt Institute for Psychology, Leipzig04109, Germany
| | - Angela D. Friederici
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig04103, Germany
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Meier A, Kuzdeba S, Jackson L, Daliri A, Tourville JA, Guenther FH, Greenlee JDW. Lateralization and Time-Course of Cortical Phonological Representations during Syllable Production. eNeuro 2023; 10:ENEURO.0474-22.2023. [PMID: 37739786 PMCID: PMC10561542 DOI: 10.1523/eneuro.0474-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 08/15/2023] [Accepted: 08/28/2023] [Indexed: 09/24/2023] Open
Abstract
Spoken language contains information at a broad range of timescales, from phonetic distinctions on the order of milliseconds to semantic contexts which shift over seconds to minutes. It is not well understood how the brain's speech production systems combine features at these timescales into a coherent vocal output. We investigated the spatial and temporal representations in cerebral cortex of three phonological units with different durations: consonants, vowels, and syllables. Electrocorticography (ECoG) recordings were obtained from five participants while speaking single syllables. We developed a novel clustering and Kalman filter-based trend analysis procedure to sort electrodes into temporal response profiles. A linear discriminant classifier was used to determine how strongly each electrode's response encoded phonological features. We found distinct time-courses of encoding phonological units depending on their duration: consonants were represented more during speech preparation, vowels were represented evenly throughout trials, and syllables during production. Locations of strongly speech-encoding electrodes (the top 30% of electrodes) likewise depended on phonological element duration, with consonant-encoding electrodes left-lateralized, vowel-encoding hemispherically balanced, and syllable-encoding right-lateralized. The lateralization of speech-encoding electrodes depended on onset time, with electrodes active before or after speech production favoring left hemisphere and those active during speech favoring the right. Single-electrode speech classification revealed cortical areas with preferential encoding of particular phonemic elements, including consonant encoding in the left precentral and postcentral gyri and syllable encoding in the right middle frontal gyrus. Our findings support neurolinguistic theories of left hemisphere specialization for processing short-timescale linguistic units and right hemisphere processing of longer-duration units.
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Affiliation(s)
- Andrew Meier
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215
| | - Scott Kuzdeba
- Graduate Program for Neuroscience, Boston University, Boston, MA 02215
| | - Liam Jackson
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215
| | - Ayoub Daliri
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215
- College of Health Solutions, Arizona State University, Tempe, AZ 85004
| | - Jason A Tourville
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215
| | - Frank H Guenther
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215
- Department of Biomedical Engineering, Boston University, Boston, MA 02215
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02215
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02215
| | - Jeremy D W Greenlee
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA 52242
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Stephen EP, Li Y, Metzger S, Oganian Y, Chang EF. Latent neural dynamics encode temporal context in speech. Hear Res 2023; 437:108838. [PMID: 37441880 PMCID: PMC11182421 DOI: 10.1016/j.heares.2023.108838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 06/15/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023]
Abstract
Direct neural recordings from human auditory cortex have demonstrated encoding for acoustic-phonetic features of consonants and vowels. Neural responses also encode distinct acoustic amplitude cues related to timing, such as those that occur at the onset of a sentence after a silent period or the onset of the vowel in each syllable. Here, we used a group reduced rank regression model to show that distributed cortical responses support a low-dimensional latent state representation of temporal context in speech. The timing cues each capture more unique variance than all other phonetic features and exhibit rotational or cyclical dynamics in latent space from activity that is widespread over the superior temporal gyrus. We propose that these spatially distributed timing signals could serve to provide temporal context for, and possibly bind across time, the concurrent processing of individual phonetic features, to compose higher-order phonological (e.g. word-level) representations.
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Affiliation(s)
- Emily P Stephen
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, United States; Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States
| | - Yuanning Li
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, United States; School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Sean Metzger
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, United States
| | - Yulia Oganian
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, United States; Center for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
| | - Edward F Chang
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, United States.
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Thomas TM, Singh A, Bullock LP, Liang D, Morse CW, Scherschligt X, Seymour JP, Tandon N. Decoding articulatory and phonetic components of naturalistic continuous speech from the distributed language network. J Neural Eng 2023; 20:046030. [PMID: 37487487 DOI: 10.1088/1741-2552/ace9fb] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 07/24/2023] [Indexed: 07/26/2023]
Abstract
Objective.The speech production network relies on a widely distributed brain network. However, research and development of speech brain-computer interfaces (speech-BCIs) has typically focused on decoding speech only from superficial subregions readily accessible by subdural grid arrays-typically placed over the sensorimotor cortex. Alternatively, the technique of stereo-electroencephalography (sEEG) enables access to distributed brain regions using multiple depth electrodes with lower surgical risks, especially in patients with brain injuries resulting in aphasia and other speech disorders.Approach.To investigate the decoding potential of widespread electrode coverage in multiple cortical sites, we used a naturalistic continuous speech production task. We obtained neural recordings using sEEG from eight participants while they read aloud sentences. We trained linear classifiers to decode distinct speech components (articulatory components and phonemes) solely based on broadband gamma activity and evaluated the decoding performance using nested five-fold cross-validation.Main Results.We achieved an average classification accuracy of 18.7% across 9 places of articulation (e.g. bilabials, palatals), 26.5% across 5 manner of articulation (MOA) labels (e.g. affricates, fricatives), and 4.81% across 38 phonemes. The highest classification accuracies achieved with a single large dataset were 26.3% for place of articulation, 35.7% for MOA, and 9.88% for phonemes. Electrodes that contributed high decoding power were distributed across multiple sulcal and gyral sites in both dominant and non-dominant hemispheres, including ventral sensorimotor, inferior frontal, superior temporal, and fusiform cortices. Rather than finding a distinct cortical locus for each speech component, we observed neural correlates of both articulatory and phonetic components in multiple hubs of a widespread language production network.Significance.These results reveal the distributed cortical representations whose activity can enable decoding speech components during continuous speech through the use of this minimally invasive recording method, elucidating language neurobiology and neural targets for future speech-BCIs.
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Affiliation(s)
- Tessy M Thomas
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, United States of America
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030, United States of America
| | - Aditya Singh
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, United States of America
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030, United States of America
| | - Latané P Bullock
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, United States of America
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030, United States of America
| | - Daniel Liang
- Department of Computer Science, Rice University, Houston, TX 77005, United States of America
| | - Cale W Morse
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, United States of America
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030, United States of America
| | - Xavier Scherschligt
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, United States of America
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030, United States of America
| | - John P Seymour
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, United States of America
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030, United States of America
- Department of Electrical & Computer Engineering, Rice University, Houston, TX 77005, United States of America
| | - Nitin Tandon
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, United States of America
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030, United States of America
- Memorial Hermann Hospital, Texas Medical Center, Houston, TX 77030, United States of America
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Sen O, Sheehan AM, Raman PR, Khara KS, Khalifa A, Chatterjee B. Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:5575. [PMID: 37420741 DOI: 10.3390/s23125575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 06/09/2023] [Accepted: 06/12/2023] [Indexed: 07/09/2023]
Abstract
Brain-Computer Interfaces (BCIs) have become increasingly popular in recent years due to their potential applications in diverse fields, ranging from the medical sector (people with motor and/or communication disabilities), cognitive training, gaming, and Augmented Reality/Virtual Reality (AR/VR), among other areas. BCI which can decode and recognize neural signals involved in speech and handwriting has the potential to greatly assist individuals with severe motor impairments in their communication and interaction needs. Innovative and cutting-edge advancements in this field have the potential to develop a highly accessible and interactive communication platform for these people. The purpose of this review paper is to analyze the existing research on handwriting and speech recognition from neural signals. So that the new researchers who are interested in this field can gain thorough knowledge in this research area. The current research on neural signal-based recognition of handwriting and speech has been categorized into two main types: invasive and non-invasive studies. We have examined the latest papers on converting speech-activity-based neural signals and handwriting-activity-based neural signals into text data. The methods of extracting data from the brain have also been discussed in this review. Additionally, this review includes a brief summary of the datasets, preprocessing techniques, and methods used in these studies, which were published between 2014 and 2022. This review aims to provide a comprehensive summary of the methodologies used in the current literature on neural signal-based recognition of handwriting and speech. In essence, this article is intended to serve as a valuable resource for future researchers who wish to investigate neural signal-based machine-learning methods in their work.
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Affiliation(s)
- Ovishake Sen
- Department of ECE, University of Florida, Gainesville, FL 32611, USA
| | - Anna M Sheehan
- Department of ECE, University of Florida, Gainesville, FL 32611, USA
| | - Pranay R Raman
- Department of ECE, University of Florida, Gainesville, FL 32611, USA
| | - Kabir S Khara
- Department of ECE, University of Florida, Gainesville, FL 32611, USA
| | - Adam Khalifa
- Department of ECE, University of Florida, Gainesville, FL 32611, USA
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Wingfield C, Zhang C, Devereux B, Fonteneau E, Thwaites A, Liu X, Woodland P, Marslen-Wilson W, Su L. On the similarities of representations in artificial and brain neural networks for speech recognition. Front Comput Neurosci 2022; 16:1057439. [PMID: 36618270 PMCID: PMC9811675 DOI: 10.3389/fncom.2022.1057439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction In recent years, machines powered by deep learning have achieved near-human levels of performance in speech recognition. The fields of artificial intelligence and cognitive neuroscience have finally reached a similar level of performance, despite their huge differences in implementation, and so deep learning models can-in principle-serve as candidates for mechanistic models of the human auditory system. Methods Utilizing high-performance automatic speech recognition systems, and advanced non-invasive human neuroimaging technology such as magnetoencephalography and multivariate pattern-information analysis, the current study aimed to relate machine-learned representations of speech to recorded human brain representations of the same speech. Results In one direction, we found a quasi-hierarchical functional organization in human auditory cortex qualitatively matched with the hidden layers of deep artificial neural networks trained as part of an automatic speech recognizer. In the reverse direction, we modified the hidden layer organization of the artificial neural network based on neural activation patterns in human brains. The result was a substantial improvement in word recognition accuracy and learned speech representations. Discussion We have demonstrated that artificial and brain neural networks can be mutually informative in the domain of speech recognition.
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Affiliation(s)
- Cai Wingfield
- Department of Psychology, Lancaster University, Lancaster, United Kingdom
| | - Chao Zhang
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
| | - Barry Devereux
- School of Electronics, Electrical Engineering and Computer Science, Queens University Belfast, Belfast, United Kingdom
| | - Elisabeth Fonteneau
- Department of Psychology, University Paul Valéry Montpellier, Montpellier, France
| | - Andrew Thwaites
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Xunying Liu
- Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Phil Woodland
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
| | | | - Li Su
- Department of Neuroscience, Neuroscience Institute, Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, United Kingdom,Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom,*Correspondence: Li Su
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Verwoert M, Ottenhoff MC, Goulis S, Colon AJ, Wagner L, Tousseyn S, van Dijk JP, Kubben PL, Herff C. Dataset of Speech Production in intracranial.Electroencephalography. Sci Data 2022; 9:434. [PMID: 35869138 PMCID: PMC9307753 DOI: 10.1038/s41597-022-01542-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 07/08/2022] [Indexed: 11/28/2022] Open
Abstract
Speech production is an intricate process involving a large number of muscles and cognitive processes. The neural processes underlying speech production are not completely understood. As speech is a uniquely human ability, it can not be investigated in animal models. High-fidelity human data can only be obtained in clinical settings and is therefore not easily available to all researchers. Here, we provide a dataset of 10 participants reading out individual words while we measured intracranial EEG from a total of 1103 electrodes. The data, with its high temporal resolution and coverage of a large variety of cortical and sub-cortical brain regions, can help in understanding the speech production process better. Simultaneously, the data can be used to test speech decoding and synthesis approaches from neural data to develop speech Brain-Computer Interfaces and speech neuroprostheses. Measurement(s) | Brain activity | Technology Type(s) | Stereotactic electroencephalography | Sample Characteristic - Organism | Homo sapiens | Sample Characteristic - Environment | Epilepsy monitoring center | Sample Characteristic - Location | The Netherlands |
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Rogalsky C, Basilakos A, Rorden C, Pillay S, LaCroix AN, Keator L, Mickelsen S, Anderson SW, Love T, Fridriksson J, Binder J, Hickok G. The Neuroanatomy of Speech Processing: A Large-scale Lesion Study. J Cogn Neurosci 2022; 34:1355-1375. [PMID: 35640102 PMCID: PMC9274306 DOI: 10.1162/jocn_a_01876] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The neural basis of language has been studied for centuries, yet the networks critically involved in simply identifying or understanding a spoken word remain elusive. Several functional-anatomical models of critical neural substrates of receptive speech have been proposed, including (1) auditory-related regions in the left mid-posterior superior temporal lobe, (2) motor-related regions in the left frontal lobe (in normal and/or noisy conditions), (3) the left anterior superior temporal lobe, or (4) bilateral mid-posterior superior temporal areas. One difficulty in comparing these models is that they often focus on different aspects of the sound-to-meaning pathway and are supported by different types of stimuli and tasks. Two auditory tasks that are typically used in separate studies-syllable discrimination and word comprehension-often yield different conclusions. We assessed syllable discrimination (words and nonwords) and word comprehension (clear speech and with a noise masker) in 158 individuals with focal brain damage: left (n = 113) or right (n = 19) hemisphere stroke, left (n = 18) or right (n = 8) anterior temporal lobectomy, and 26 neurologically intact controls. Discrimination and comprehension tasks are doubly dissociable both behaviorally and neurologically. In support of a bilateral model, clear speech comprehension was near ceiling in 95% of left stroke cases and right temporal damage impaired syllable discrimination. Lesion-symptom mapping analyses for the syllable discrimination and noisy word comprehension tasks each implicated most of the left superior temporal gyrus. Comprehension but not discrimination tasks also implicated the left posterior middle temporal gyrus, whereas discrimination but not comprehension tasks also implicated more dorsal sensorimotor regions in posterior perisylvian cortex.
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12
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Ng B, Reh RK, Mostafavi S. A practical guide to applying machine learning to infant EEG data. Dev Cogn Neurosci 2022; 54:101096. [PMID: 35334336 PMCID: PMC8943418 DOI: 10.1016/j.dcn.2022.101096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 03/07/2022] [Accepted: 03/10/2022] [Indexed: 11/08/2022] Open
Abstract
Electroencephalography (EEG) has been widely adopted by the developmental cognitive neuroscience community, but the application of machine learning (ML) in this domain lags behind adult EEG studies. Applying ML to infant data is particularly challenging due to the low number of trials, low signal-to-noise ratio, high inter-subject variability, and high inter-trial variability. Here, we provide a step-by-step tutorial on how to apply ML to classify cognitive states in infants. We describe the type of brain attributes that are widely used for EEG classification and also introduce a Riemannian geometry based approach for deriving connectivity estimates that account for inter-trial and inter-subject variability. We present pipelines for learning classifiers using trials from a single infant and from multiple infants, and demonstrate the application of these pipelines on a standard infant EEG dataset of forty 12-month-old infants collected under an auditory oddball paradigm. While we classify perceptual states induced by frequent versus rare stimuli, the presented pipelines can be easily adapted for other experimental designs and stimuli using the associated code that we have made publicly available.
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13
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Moon J, Chau T, Orlandi S. A comparison and classification of oscillatory characteristics in speech perception and covert speech. Brain Res 2022; 1781:147778. [PMID: 35007548 DOI: 10.1016/j.brainres.2022.147778] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 12/29/2021] [Accepted: 01/03/2022] [Indexed: 11/02/2022]
Abstract
Covert speech, the mental imagery of speaking, has been studied increasingly to understand and decode thoughts in the context of brain-computer interfaces. In studies of speech comprehension, neural oscillations are thought to play a key role in the temporal encoding of speech. However, little is known about the role of oscillations in covert speech. In this study, we investigated the oscillatory involvements in covert speech and speech perception. Data were collected from 10 participants with 64 channel EEG. Participants heard the words, 'blue' and 'orange', and subsequently mentally rehearsed them. First, continuous wavelet transform was performed on epoched signals and subsequently two-tailed t-tests between two classes were conducted to determine statistical differences in frequency and time (t-CWT). Features were also extracted using t-CWT and subsequently classified using a support vector machine. θ and γ phase amplitude coupling (PAC) was also assessed within and between tasks. All binary classifications produced accuracies significantly greater (80-90%) than chance level, supporting the use of t-CWT in determining relative oscillatory involvements. While the perception task dynamically invoked all frequencies with more prominent θ and α activity, the covert task favoured higher frequencies with significantly higher γ activity than perception. Moreover, the perception condition produced significant θ-γ PAC, corroborating a reported linkage between syllabic and phonemic sampling. Although this coupling was found to be suppressed in the covert condition, we found significant cross-task coupling between perception θ and covert speech γ. Covert speech processing appears to be largely associated with higher frequencies of EEG. Importantly, the significant cross-task coupling between speech perception and covert speech, in the absence of within-task covert speech PAC, supports the notion that the γ- and θ-bands subserve, respectively, shared and unique encoding processes across tasks.
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Affiliation(s)
- Jaewoong Moon
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
| | - Tom Chau
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Silvia Orlandi
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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14
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Luo S, Rabbani Q, Crone NE. Brain-Computer Interface: Applications to Speech Decoding and Synthesis to Augment Communication. Neurotherapeutics 2022; 19:263-273. [PMID: 35099768 PMCID: PMC9130409 DOI: 10.1007/s13311-022-01190-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/16/2022] [Indexed: 01/03/2023] Open
Abstract
Damage or degeneration of motor pathways necessary for speech and other movements, as in brainstem strokes or amyotrophic lateral sclerosis (ALS), can interfere with efficient communication without affecting brain structures responsible for language or cognition. In the worst-case scenario, this can result in the locked in syndrome (LIS), a condition in which individuals cannot initiate communication and can only express themselves by answering yes/no questions with eye blinks or other rudimentary movements. Existing augmentative and alternative communication (AAC) devices that rely on eye tracking can improve the quality of life for people with this condition, but brain-computer interfaces (BCIs) are also increasingly being investigated as AAC devices, particularly when eye tracking is too slow or unreliable. Moreover, with recent and ongoing advances in machine learning and neural recording technologies, BCIs may offer the only means to go beyond cursor control and text generation on a computer, to allow real-time synthesis of speech, which would arguably offer the most efficient and expressive channel for communication. The potential for BCI speech synthesis has only recently been realized because of seminal studies of the neuroanatomical and neurophysiological underpinnings of speech production using intracranial electrocorticographic (ECoG) recordings in patients undergoing epilepsy surgery. These studies have shown that cortical areas responsible for vocalization and articulation are distributed over a large area of ventral sensorimotor cortex, and that it is possible to decode speech and reconstruct its acoustics from ECoG if these areas are recorded with sufficiently dense and comprehensive electrode arrays. In this article, we review these advances, including the latest neural decoding strategies that range from deep learning models to the direct concatenation of speech units. We also discuss state-of-the-art vocoders that are integral in constructing natural-sounding audio waveforms for speech BCIs. Finally, this review outlines some of the challenges ahead in directly synthesizing speech for patients with LIS.
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Affiliation(s)
- Shiyu Luo
- Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Qinwan Rabbani
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Nathan E Crone
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
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15
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Angrick M, Ottenhoff M, Goulis S, Colon AJ, Wagner L, Krusienski DJ, Kubben PL, Schultz T, Herff C. Speech Synthesis from Stereotactic EEG using an Electrode Shaft Dependent Multi-Input Convolutional Neural Network Approach. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6045-6048. [PMID: 34892495 DOI: 10.1109/embc46164.2021.9629711] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Neurological disorders can lead to significant impairments in speech communication and, in severe cases, cause the complete loss of the ability to speak. Brain-Computer Interfaces have shown promise as an alternative communication modality by directly transforming neural activity of speech processes into a textual or audible representations. Previous studies investigating such speech neuroprostheses relied on electrocorticography (ECoG) or microelectrode arrays that acquire neural signals from superficial areas on the cortex. While both measurement methods have demonstrated successful speech decoding, they do not capture activity from deeper brain structures and this activity has therefore not been harnessed for speech-related BCIs. In this study, we bridge this gap by adapting a previously presented decoding pipeline for speech synthesis based on ECoG signals to implanted depth electrodes (sEEG). For this purpose, we propose a multi-input convolutional neural network that extracts speech-related activity separately for each electrode shaft and estimates spectral coefficients to reconstruct an audible waveform. We evaluate our approach on open-loop data from 5 patients who conducted a recitation task of Dutch utterances. We achieve correlations of up to 0.80 between original and reconstructed speech spectrograms, which are significantly above chance level for all patients (p < 0.001). Our results indicate that sEEG can yield similar speech decoding performance to prior ECoG studies and is a promising modality for speech BCIs.
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16
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Hamilton LS, Oganian Y, Hall J, Chang EF. Parallel and distributed encoding of speech across human auditory cortex. Cell 2021; 184:4626-4639.e13. [PMID: 34411517 DOI: 10.1016/j.cell.2021.07.019] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 02/11/2021] [Accepted: 07/19/2021] [Indexed: 12/27/2022]
Abstract
Speech perception is thought to rely on a cortical feedforward serial transformation of acoustic into linguistic representations. Using intracranial recordings across the entire human auditory cortex, electrocortical stimulation, and surgical ablation, we show that cortical processing across areas is not consistent with a serial hierarchical organization. Instead, response latency and receptive field analyses demonstrate parallel and distinct information processing in the primary and nonprimary auditory cortices. This functional dissociation was also observed where stimulation of the primary auditory cortex evokes auditory hallucination but does not distort or interfere with speech perception. Opposite effects were observed during stimulation of nonprimary cortex in superior temporal gyrus. Ablation of the primary auditory cortex does not affect speech perception. These results establish a distributed functional organization of parallel information processing throughout the human auditory cortex and demonstrate an essential independent role for nonprimary auditory cortex in speech processing.
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Affiliation(s)
- Liberty S Hamilton
- Department of Neurological Surgery, University of California, San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA
| | - Yulia Oganian
- Department of Neurological Surgery, University of California, San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA
| | - Jeffery Hall
- Department of Neurology and Neurosurgery, McGill University Montreal Neurological Institute, Montreal, QC, H3A 2B4, Canada
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA.
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17
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Sheth J, Tankus A, Tran M, Pouratian N, Fried I, Speier W. Generalizing neural signal-to-text brain-computer interfaces. Biomed Phys Eng Express 2021; 7. [PMID: 33836507 DOI: 10.1088/2057-1976/abf6ab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 04/09/2021] [Indexed: 11/12/2022]
Abstract
Objective:Brain-Computer Interfaces (BCI) may help patients with faltering communication abilities due to neurodegenerative diseases produce text or speech by direct neural processing. However, their practical realization has proven difficult due to limitations in speed, accuracy, and generalizability of existing interfaces. The goal of this study is to evaluate the BCI performance of a robust speech decoding system that translates neural signals evoked by speech to a textual output. While previous studies have approached this problem by using neural signals to choose from a limited set of possible words, we employ a more general model that can type any word from a large corpus of English text.Approach:In this study, we create an end-to-end BCI that translates neural signals associated with overt speech into text output. Our decoding system first isolates frequency bands in the input depth-electrode signal encapsulating differential information regarding production of various phonemic classes. These bands form a feature set that then feeds into a Long Short-Term Memory (LSTM) model which discerns at each time point probability distributions across all phonemes uttered by a subject. Finally, a particle filtering algorithm temporally smooths these probabilities by incorporating prior knowledge of the English language to output text corresponding to the decoded word. The generalizability of our decoder is driven by the lack of a vocabulary constraint on this output word.Main result:This method was evaluated using a dataset of 6 neurosurgical patients implanted with intra-cranial depth electrodes to identify seizure foci for potential surgical treatment of epilepsy. We averaged 32% word accuracy and on the phoneme-level obtained 46% precision, 51% recall and 73.32% average phoneme error rate while also achieving significant increases in speed when compared to several other BCI approaches.Significance:Our study employs a more general neural signal-to-text model which could facilitate communication by patients in everyday environments.
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Affiliation(s)
- Janaki Sheth
- Department of Physics and Astronomy, UCLA, Los Angeles, CA, United States of America
| | - Ariel Tankus
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Functional Neurosurgery Unit, Tel Aviv, Sourasky Medical Center, Tel Aviv, Israel.,Department of Neurology and Neurosurgery, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Michelle Tran
- Department of Neurosurgery, UCLA, Los Angeles, CA, United States of America
| | - Nader Pouratian
- Department of Neurosurgery, UCLA, Los Angeles, CA, United States of America
| | - Itzhak Fried
- Department of Neurosurgery, UCLA, Los Angeles, CA, United States of America
| | - William Speier
- Department of Radiology, UCLA, Los Angeles, CA, United States of America
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18
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Kang YH, Löffler A, Jeurissen D, Zylberberg A, Wolpert DM, Shadlen MN. Multiple decisions about one object involve parallel sensory acquisition but time-multiplexed evidence incorporation. eLife 2021; 10:63721. [PMID: 33688829 PMCID: PMC8112870 DOI: 10.7554/elife.63721] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Accepted: 03/06/2021] [Indexed: 01/31/2023] Open
Abstract
The brain is capable of processing several streams of information that bear on different aspects of the same problem. Here, we address the problem of making two decisions about one object, by studying difficult perceptual decisions about the color and motion of a dynamic random dot display. We find that the accuracy of one decision is unaffected by the difficulty of the other decision. However, the response times reveal that the two decisions do not form simultaneously. We show that both stimulus dimensions are acquired in parallel for the initial ∼0.1 s but are then incorporated serially in time-multiplexed bouts. Thus, there is a bottleneck that precludes updating more than one decision at a time, and a buffer that stores samples of evidence while access to the decision is blocked. We suggest that this bottleneck is responsible for the long timescales of many cognitive operations framed as decisions.
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Affiliation(s)
- Yul Hr Kang
- Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, United States.,Department of Engineering, University of Cambridge, Cambridge, United Kingdom
| | - Anne Löffler
- Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, United States.,Kavli Institute for Brain Science, Columbia University, New York, United States
| | - Danique Jeurissen
- Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, United States.,Howard Hughes Medical Institute, Columbia University, New York, United States
| | - Ariel Zylberberg
- Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, United States.,Department of Brain and Cognitive Sciences, University of Rochester, Rochester, United States
| | - Daniel M Wolpert
- Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, United States
| | - Michael N Shadlen
- Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, United States.,Kavli Institute for Brain Science, Columbia University, New York, United States.,Howard Hughes Medical Institute, Columbia University, New York, United States
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19
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Trumpis M, Chiang CH, Orsborn AL, Bent B, Li J, Rogers JA, Pesaran B, Cogan G, Viventi J. Sufficient sampling for kriging prediction of cortical potential in rat, monkey, and human µECoG. J Neural Eng 2021; 18. [PMID: 33326943 DOI: 10.1088/1741-2552/abd460] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 12/16/2020] [Indexed: 12/22/2022]
Abstract
Objective. Large channel count surface-based electrophysiology arrays (e.g. µECoG) are high-throughput neural interfaces with good chronic stability. Electrode spacing remains ad hoc due to redundancy and nonstationarity of field dynamics. Here, we establish a criterion for electrode spacing based on the expected accuracy of predicting unsampled field potential from sampled sites.Approach. We applied spatial covariance modeling and field prediction techniques based on geospatial kriging to quantify sufficient sampling for thousands of 500 ms µECoG snapshots in human, monkey, and rat. We calculated a probably approximately correct (PAC) spacing based on kriging that would be required to predict µECoG fields at≤10% error for most cases (95% of observations).Main results. Kriging theory accurately explained the competing effects of electrode density and noise on predicting field potential. Across five frequency bands from 4-7 to 75-300 Hz, PAC spacing was sub-millimeter for auditory cortex in anesthetized and awake rats, and posterior superior temporal gyrus in anesthetized human. At 75-300 Hz, sub-millimeter PAC spacing was required in all species and cortical areas.Significance. PAC spacing accounted for the effect of signal-to-noise on prediction quality and was sensitive to the full distribution of non-stationary covariance states. Our results show that µECoG arrays should sample at sub-millimeter resolution for applications in diverse cortical areas and for noise resilience.
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Affiliation(s)
- Michael Trumpis
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
| | - Chia-Han Chiang
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
| | - Amy L Orsborn
- Center for Neural Science, New York University, New York, NY 10003, United States of America.,Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, United States of America.,Department of Bioengineering, University of Washington, Seattle, Washington 98105, United States of America.,Washington National Primate Research Center, Seattle, Washington 98195, United States of America
| | - Brinnae Bent
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
| | - Jinghua Li
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, United States of America.,Department of Materials Science and Engineering, The Ohio State University, Columbus, OH 43210, United States of America.,Chronic Brain Injury Program, The Ohio State University, Columbus, OH 43210, United States of America
| | - John A Rogers
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, United States of America.,Simpson Querrey Institute, Northwestern University, Chicago, IL 60611, United States of America.,Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, United States of America.,Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States of America
| | - Bijan Pesaran
- Center for Neural Science, New York University, New York, NY 10003, United States of America
| | - Gregory Cogan
- Department of Neurosurgery, Duke School of Medicine, Durham, NC 27710, United States of America.,Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States of America.,Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States of America.,Duke Comprehensive Epilepsy Center, Duke School of Medicine, Durham, NC 27710, United States of America
| | - Jonathan Viventi
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America.,Department of Neurosurgery, Duke School of Medicine, Durham, NC 27710, United States of America.,Duke Comprehensive Epilepsy Center, Duke School of Medicine, Durham, NC 27710, United States of America.,Department of Neurobiology, Duke School of Medicine, Durham, NC 27710, United States of America
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20
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Sun P, Anumanchipalli GK, Chang EF. Brain2Char: a deep architecture for decoding text from brain recordings. J Neural Eng 2020; 17. [PMID: 33142282 DOI: 10.1088/1741-2552/abc742] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 11/03/2020] [Indexed: 01/08/2023]
Abstract
OBJECTIVE Decoding language representations directly from the brain can enable new Brain-Computer Interfaces (BCI) for high bandwidth human-human and human-machine communication. Clinically, such technologies can restore communication in people with neurological conditions affecting their ability to speak. APPROACH In this study, we propose a novel deep network architecture Brain2Char, for directly decoding text (specifically character sequences) from direct brain recordings (called Electrocorticography, ECoG). Brain2Char framework combines state-of-the-art deep learning modules --- 3D Inception layers for multiband spatiotemporal feature extraction from neural data and bidirectional recurrent layers, dilated convolution layers followed by language model weighted beam search to decode character sequences, optimizing a connectionist temporal classification (CTC) loss. Additionally, given the highly non-linear transformations that underlie the conversion of cortical function to character sequences, we perform regularizations on the network's latent representations motivated by insights into cortical encoding of speech production and artifactual aspects specific to ECoG data acquisition. To do this, we impose auxiliary losses on latent representations for articulatory movements, speech acoustics and session specific non-linearities. MAIN RESULTS In 3 (out of 4) participants reported here, Brain2Char achieves 10.6%, 8.5% and 7.0%Word Error Rates (WER) respectively on vocabulary sizes ranging from 1200 to 1900 words. SIGNIFICANCE These results establish a new end-to-end approach on decoding text from brain signals and demonstrate the potential of Brain2Char as a high-performance communication BCI.
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Affiliation(s)
- Pengfei Sun
- University of California San Francisco, San Francisco, California, 94143, UNITED STATES
| | | | - Edward F Chang
- University of California San Francisco, San Francisco, UNITED STATES
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21
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Hashimoto H, Kameda S, Maezawa H, Oshino S, Tani N, Khoo HM, Yanagisawa T, Yoshimine T, Kishima H, Hirata M. A Swallowing Decoder Based on Deep Transfer Learning: AlexNet Classification of the Intracranial Electrocorticogram. Int J Neural Syst 2020; 31:2050056. [PMID: 32938263 DOI: 10.1142/s0129065720500562] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
To realize a brain-machine interface to assist swallowing, neural signal decoding is indispensable. Eight participants with temporal-lobe intracranial electrode implants for epilepsy were asked to swallow during electrocorticogram (ECoG) recording. Raw ECoG signals or certain frequency bands of the ECoG power were converted into images whose vertical axis was electrode number and whose horizontal axis was time in milliseconds, which were used as training data. These data were classified with four labels (Rest, Mouth open, Water injection, and Swallowing). Deep transfer learning was carried out using AlexNet, and power in the high-[Formula: see text] band (75-150[Formula: see text]Hz) was the training set. Accuracy reached 74.01%, sensitivity reached 82.51%, and specificity reached 95.38%. However, using the raw ECoG signals, the accuracy obtained was 76.95%, comparable to that of the high-[Formula: see text] power. We demonstrated that a version of AlexNet pre-trained with visually meaningful images can be used for transfer learning of visually meaningless images made up of ECoG signals. Moreover, we could achieve high decoding accuracy using the raw ECoG signals, allowing us to dispense with the conventional extraction of high-[Formula: see text] power. Thus, the images derived from the raw ECoG signals were equivalent to those derived from the high-[Formula: see text] band for transfer deep learning.
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Affiliation(s)
- Hiroaki Hashimoto
- Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan.,Department of Neurosurgery, Otemae Hospital, Chuo-Ku Otemae 1-5-34, Osaka, Osaka 540-0008, Japan.,Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
| | - Seiji Kameda
- Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
| | - Hitoshi Maezawa
- Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
| | - Satoru Oshino
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
| | - Naoki Tani
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
| | - Hui Ming Khoo
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
| | - Takufumi Yanagisawa
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
| | - Toshiki Yoshimine
- Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
| | - Haruhiko Kishima
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
| | - Masayuki Hirata
- Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan.,Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan.,Department of Neurosurgery, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
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22
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Abstract
Intracranial electroencephalography (iEEG) is measured from electrodes placed in or on the brain. These measurements have an excellent signal-to-noise ratio and iEEG signals have often been used to decode brain activity or drive brain-computer interfaces (BCIs). iEEG recordings are typically done for seizure monitoring in epilepsy patients who have these electrodes placed for a clinical purpose: to localize both brain regions that are essential for function and others where seizures start. Brain regions not involved in epilepsy are thought to function normally and provide a unique opportunity to learn about human neurophysiology. Intracranial electrodes measure the aggregate activity of large neuronal populations and recorded signals contain many features. Different features are extracted by analyzing these signals in the time and frequency domain. The time domain may reveal an evoked potential at a particular time after the onset of an event. Decomposition into the frequency domain may show narrowband peaks in the spectrum at specific frequencies or broadband signal changes that span a wide range of frequencies. Broadband power increases are generally observed when a brain region is active while most other features are highly specific to brain regions, inputs, and tasks. Here we describe the spatiotemporal dynamics of several iEEG signals that have often been used to decode brain activity and drive BCIs.
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23
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Abstract
Syntax, the structure of sentences, enables humans to express an infinite range of meanings through finite means. The neurobiology of syntax has been intensely studied but with little consensus. Two main candidate regions have been identified: the posterior inferior frontal gyrus (pIFG) and the posterior middle temporal gyrus (pMTG). Integrating research in linguistics, psycholinguistics, and neuroscience, we propose a neuroanatomical framework for syntax that attributes distinct syntactic computations to these regions in a unified model. The key theoretical advances are adopting a modern lexicalized view of syntax in which the lexicon and syntactic rules are intertwined, and recognizing a computational asymmetry in the role of syntax during comprehension and production. Our model postulates a hierarchical lexical-syntactic function to the pMTG, which interconnects previously identified speech perception and conceptual-semantic systems in the temporal and inferior parietal lobes, crucial for both sentence production and comprehension. These relational hierarchies are transformed via the pIFG into morpho-syntactic sequences, primarily tied to production. We show how this architecture provides a better account of the full range of data and is consistent with recent proposals regarding the organization of phonological processes in the brain.
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Affiliation(s)
- William Matchin
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, 29208, USA
| | - Gregory Hickok
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, 92697, USA
- Department of Language Science, University of California, Irvine, Irvine, CA, 92697, USA
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24
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Herff C, Krusienski DJ, Kubben P. The Potential of Stereotactic-EEG for Brain-Computer Interfaces: Current Progress and Future Directions. Front Neurosci 2020; 14:123. [PMID: 32174810 PMCID: PMC7056827 DOI: 10.3389/fnins.2020.00123] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 01/30/2020] [Indexed: 12/17/2022] Open
Abstract
Stereotactic electroencephalogaphy (sEEG) utilizes localized, penetrating depth electrodes to measure electrophysiological brain activity. It is most commonly used in the identification of epileptogenic zones in cases of refractory epilepsy. The implanted electrodes generally provide a sparse sampling of a unique set of brain regions including deeper brain structures such as hippocampus, amygdala and insula that cannot be captured by superficial measurement modalities such as electrocorticography (ECoG). Despite the overlapping clinical application and recent progress in decoding of ECoG for Brain-Computer Interfaces (BCIs), sEEG has thus far received comparatively little attention for BCI decoding. Additionally, the success of the related deep-brain stimulation (DBS) implants bodes well for the potential for chronic sEEG applications. This article provides an overview of sEEG technology, BCI-related research, and prospective future directions of sEEG for long-term BCI applications.
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Affiliation(s)
- Christian Herff
- Department of Neurosurgery, School of Mental Health and Neurosciences, Maastricht University, Maastricht, Netherlands
| | - Dean J Krusienski
- ASPEN Lab, Biomedical Engineering Department, Virginia Commonwealth University, Richmond, VA, United States
| | - Pieter Kubben
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, Netherlands
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25
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Herff C, Diener L, Angrick M, Mugler E, Tate MC, Goldrick MA, Krusienski DJ, Slutzky MW, Schultz T. Generating Natural, Intelligible Speech From Brain Activity in Motor, Premotor, and Inferior Frontal Cortices. Front Neurosci 2019; 13:1267. [PMID: 31824257 PMCID: PMC6882773 DOI: 10.3389/fnins.2019.01267] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 11/07/2019] [Indexed: 12/17/2022] Open
Abstract
Neural interfaces that directly produce intelligible speech from brain activity would allow people with severe impairment from neurological disorders to communicate more naturally. 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 intelligible speech output that can preserve conversational cues. To produce speech directly from neural data, we adapted a method from the field of speech synthesis called unit selection, in which units of speech are concatenated to form audible output. 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. Brain-To-Speech employed the user's own voice to generate speech that sounded very natural and included features such as prosody and accentuation. By investigating the brain areas involved in speech production separately, we found that speech motor cortex provided more information for the reconstruction process than the other cortical areas.
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Affiliation(s)
- Christian Herff
- School of Mental Health & Neuroscience, Maastricht University, Maastricht, Netherlands
- Cognitive Systems Lab, University of Bremen, Bremen, Germany
| | - Lorenz Diener
- Cognitive Systems Lab, University of Bremen, Bremen, Germany
| | - Miguel Angrick
- Cognitive Systems Lab, University of Bremen, Bremen, Germany
| | - Emily Mugler
- Department of Neurology, Northwestern University, Chicago, IL, United States
| | - Matthew C. Tate
- Department of Neurosurgery, Northwestern University, Chicago, IL, United States
| | - Matthew A. Goldrick
- Department of Linguistics, Northwestern University, Chicago, IL, United States
| | - Dean J. Krusienski
- Biomedical Engineering Department, Virginia Commonwealth University, Richmond, VA, United States
| | - Marc W. Slutzky
- Department of Neurology, Northwestern University, Chicago, IL, United States
- Department of Physiology, Northwestern University, Chicago, IL, United States
- Department of Physical Medicine & Rehabilitation, Northwestern University, Chicago, IL, United States
| | - Tanja Schultz
- Cognitive Systems Lab, University of Bremen, Bremen, Germany
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Moses DA, Leonard MK, Makin JG, Chang EF. Real-time decoding of question-and-answer speech dialogue using human cortical activity. Nat Commun 2019; 10:3096. [PMID: 31363096 PMCID: PMC6667454 DOI: 10.1038/s41467-019-10994-4] [Citation(s) in RCA: 98] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Accepted: 06/06/2019] [Indexed: 01/15/2023] Open
Abstract
Natural communication often occurs in dialogue, differentially engaging auditory and sensorimotor brain regions during listening and speaking. However, previous attempts to decode speech directly from the human brain typically consider listening or speaking tasks in isolation. Here, human participants listened to questions and responded aloud with answers while we used high-density electrocorticography (ECoG) recordings to detect when they heard or said an utterance and to then decode the utterance's identity. Because certain answers were only plausible responses to certain questions, we could dynamically update the prior probabilities of each answer using the decoded question likelihoods as context. We decode produced and perceived utterances with accuracy rates as high as 61% and 76%, respectively (chance is 7% and 20%). Contextual integration of decoded question likelihoods significantly improves answer decoding. These results demonstrate real-time decoding of speech in an interactive, conversational setting, which has important implications for patients who are unable to communicate.
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Affiliation(s)
- David A Moses
- Department of Neurological Surgery and the Center for Integrative Neuroscience at UC San Francisco, 675 Nelson Rising Lane, San Francisco, CA, 94158, USA
| | - Matthew K Leonard
- Department of Neurological Surgery and the Center for Integrative Neuroscience at UC San Francisco, 675 Nelson Rising Lane, San Francisco, CA, 94158, USA
| | - Joseph G Makin
- Department of Neurological Surgery and the Center for Integrative Neuroscience at UC San Francisco, 675 Nelson Rising Lane, San Francisco, CA, 94158, USA
| | - Edward F Chang
- Department of Neurological Surgery and the Center for Integrative Neuroscience at UC San Francisco, 675 Nelson Rising Lane, San Francisco, CA, 94158, USA.
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27
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Angrick M, Herff C, Mugler E, Tate MC, Slutzky MW, Krusienski DJ, Schultz T. Speech synthesis from ECoG using densely connected 3D convolutional neural networks. J Neural Eng 2019; 16:036019. [PMID: 30831567 PMCID: PMC6822609 DOI: 10.1088/1741-2552/ab0c59] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
OBJECTIVE Direct synthesis of speech from neural signals could provide a fast and natural way of communication to people with neurological diseases. Invasively-measured brain activity (electrocorticography; ECoG) supplies the necessary temporal and spatial resolution to decode fast and complex processes such as speech production. A number of impressive advances in speech decoding using neural signals have been achieved in recent years, but the complex dynamics are still not fully understood. However, it is unlikely that simple linear models can capture the relation between neural activity and continuous spoken speech. APPROACH Here we show that deep neural networks can be used to map ECoG from speech production areas onto an intermediate representation of speech (logMel spectrogram). The proposed method uses a densely connected convolutional neural network topology which is well-suited to work with the small amount of data available from each participant. MAIN RESULTS In a study with six participants, we achieved correlations up to r = 0.69 between the reconstructed and original logMel spectrograms. We transfered our prediction back into an audible waveform by applying a Wavenet vocoder. The vocoder was conditioned on logMel features that harnessed a much larger, pre-existing data corpus to provide the most natural acoustic output. SIGNIFICANCE To the best of our knowledge, this is the first time that high-quality speech has been reconstructed from neural recordings during speech production using deep neural networks.
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Affiliation(s)
- Miguel Angrick
- Cognitive Systems Lab, University of Bremen, Bremen, Germany
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28
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Angrick M, Herff C, Johnson G, Shih J, Krusienski D, Schultz T. Interpretation of convolutional neural networks for speech spectrogram regression from intracranial recordings. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.10.080] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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29
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Speech synthesis from neural decoding of spoken sentences. Nature 2019; 568:493-498. [DOI: 10.1038/s41586-019-1119-1] [Citation(s) in RCA: 322] [Impact Index Per Article: 64.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Accepted: 03/21/2019] [Indexed: 12/31/2022]
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30
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Towards reconstructing intelligible speech from the human auditory cortex. Sci Rep 2019; 9:874. [PMID: 30696881 PMCID: PMC6351601 DOI: 10.1038/s41598-018-37359-z] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Accepted: 11/30/2018] [Indexed: 11/08/2022] Open
Abstract
Auditory stimulus reconstruction is a technique that finds the best approximation of the acoustic stimulus from the population of evoked neural activity. Reconstructing speech from the human auditory cortex creates the possibility of a speech neuroprosthetic to establish a direct communication with the brain and has been shown to be possible in both overt and covert conditions. However, the low quality of the reconstructed speech has severely limited the utility of this method for brain-computer interface (BCI) applications. To advance the state-of-the-art in speech neuroprosthesis, we combined the recent advances in deep learning with the latest innovations in speech synthesis technologies to reconstruct closed-set intelligible speech from the human auditory cortex. We investigated the dependence of reconstruction accuracy on linear and nonlinear (deep neural network) regression methods and the acoustic representation that is used as the target of reconstruction, including auditory spectrogram and speech synthesis parameters. In addition, we compared the reconstruction accuracy from low and high neural frequency ranges. Our results show that a deep neural network model that directly estimates the parameters of a speech synthesizer from all neural frequencies achieves the highest subjective and objective scores on a digit recognition task, improving the intelligibility by 65% over the baseline method which used linear regression to reconstruct the auditory spectrogram. These results demonstrate the efficacy of deep learning and speech synthesis algorithms for designing the next generation of speech BCI systems, which not only can restore communications for paralyzed patients but also have the potential to transform human-computer interaction technologies.
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Rabbani Q, Milsap G, Crone NE. The Potential for a Speech Brain-Computer Interface Using Chronic Electrocorticography. Neurotherapeutics 2019; 16:144-165. [PMID: 30617653 PMCID: PMC6361062 DOI: 10.1007/s13311-018-00692-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
A brain-computer interface (BCI) is a technology that uses neural features to restore or augment the capabilities of its user. A BCI for speech would enable communication in real time via neural correlates of attempted or imagined speech. Such a technology would potentially restore communication and improve quality of life for locked-in patients and other patients with severe communication disorders. 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 (ASR). Indeed, ASR and related fields have developed significantly over the past years, and many lend many insights into the requirements, goals, and strategies for speech BCI. 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 neural recording modalities. Because the neural representations for speech and language are widely distributed over cortical regions spanning the frontal, parietal, and temporal lobes, the mesoscopic scale of population activity captured by ECoG surface electrode arrays may have distinct advantages for speech BCI, in contrast to the advantages of microelectrode arrays for upper-limb BCI. Nevertheless, there remain many challenges for the translation of speech BCIs to clinical populations. This review discusses and outlines the current state-of-the-art for speech BCI and explores what a speech BCI using chronic ECoG might entail.
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Affiliation(s)
- Qinwan Rabbani
- Department of Electrical Engineering, The Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
| | - Griffin Milsap
- Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nathan E Crone
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Abstract
OBJECTIVE Advances in electrophysiological methods such as electrocorticography (ECoG) have enabled researchers to decode phonemes, syllables, and words from brain activity. The ultimate aspiration underlying these efforts is the development of a brain-machine interface (BMI) that will enable speakers to produce real-time, naturalistic speech. In the effort to create such a device, researchers have typically followed a bottom-up approach whereby low-level units of language (e.g. phonemes, syllables, or letters) are decoded from articulation areas (e.g. premotor cortex) with the aim of assembling these low-level units into words and sentences. APPROACH In this paper, we recommend that researchers supplement the existing bottom-up approach with a novel top-down approach. According to the top-down proposal, initial decoding of top-down information may facilitate the subsequent decoding of downstream representations by constraining the hypothesis space from which low-level units are selected. MAIN RESULTS We identify types and sources of top-down information that may crucially inform BMI decoding ecosystems: communicative intentions (e.g. speech acts), situational pragmatics (e.g. recurrent communicative pressures), and formal linguistic data (e.g. syntactic rules and constructions, lexical collocations, speakers' individual speech histories). SIGNIFICANCE Given the inherently interactive nature of communication, we further propose that BMIs be entrained on neural responses associated with interactive dialogue tasks, as opposed to the typical practice of entraining BMIs with non-interactive presentations of language stimuli.
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Affiliation(s)
- Leon Li
- Department of Psychology and Neuroscience, Duke University, Durham, NC, United States of America
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Breshears JD, Hamilton LS, Chang EF. Spontaneous Neural Activity in the Superior Temporal Gyrus Recapitulates Tuning for Speech Features. Front Hum Neurosci 2018; 12:360. [PMID: 30279650 PMCID: PMC6153351 DOI: 10.3389/fnhum.2018.00360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Accepted: 08/21/2018] [Indexed: 11/26/2022] Open
Abstract
Background: Numerous studies have demonstrated that individuals exhibit structured neural activity in many brain regions during rest that is also observed during different tasks, however it is still not clear whether and how resting state activity patterns may relate to underlying tuning for specific stimuli. In the posterior superior temporal gyrus (STG), distinct neural activity patterns are observed during the perception of specific linguistic speech features. We hypothesized that spontaneous resting-state neural dynamics of the STG would be structured to reflect its role in speech perception, exhibiting an organization along speech features as seen during speech perception. Methods: Human cortical local field potentials were recorded from the superior temporal gyrus (STG) in 8 patients undergoing surgical treatment of epilepsy. Signals were recorded during speech perception and rest. Patterns of neural activity (high gamma power: 70–150 Hz) during rest, extracted with spatiotemporal principal component analysis, were compared to spatiotemporal neural responses to speech features during perception. Hierarchical clustering was applied to look for patterns in rest that corresponded to speech feature tuning. Results: Significant correlations were found between neural responses to speech features (sentence onsets, consonants, and vowels) and the spontaneous neural activity in the STG. Across subjects, these correlations clustered into five groups, demonstrating tuning for speech features—most robustly for acoustic onsets. These correlations were not seen in other brain areas, or during motor and spectrally-rotated speech control tasks. Conclusions: In this study, we present evidence that the RS structure of STG activity robustly recapitulates its stimulus-evoked response to acoustic onsets. Further, secondary patterns in RS activity appear to correlate with stimulus-evoked responses to speech features. The role of these spontaneous spatiotemporal activity patterns remains to be elucidated.
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Affiliation(s)
- Jonathan D. Breshears
- Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, United States
| | - Liberty S. Hamilton
- Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, United States
- Department of Communication Sciences and Disorders, University of Texas at Austin, Austin, TX, United States
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, TX, United States
| | - Edward F. Chang
- Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, United States
- *Correspondence: Edward F. Chang
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34
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Wen J, Yu T, Wang X, Liu C, Zhou T, Li Y, Li X. Continuous behavioral tracing-based online functional brain mapping with intracranial electroencephalography. J Neural Eng 2018; 15:054002. [DOI: 10.1088/1741-2552/aad405] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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35
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Dichter BK, Breshears JD, Leonard MK, Chang EF. The Control of Vocal Pitch in Human Laryngeal Motor Cortex. Cell 2018; 174:21-31.e9. [PMID: 29958109 PMCID: PMC6084806 DOI: 10.1016/j.cell.2018.05.016] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2018] [Revised: 03/23/2018] [Accepted: 05/08/2018] [Indexed: 11/24/2022]
Abstract
In speech, the highly flexible modulation of vocal pitch creates intonation patterns that speakers use to convey linguistic meaning. This human ability is unique among primates. Here, we used high-density cortical recordings directly from the human brain to determine the encoding of vocal pitch during natural speech. We found neural populations in bilateral dorsal laryngeal motor cortex (dLMC) that selectively encoded produced pitch but not non-laryngeal articulatory movements. This neural population controlled short pitch accents to express prosodic emphasis on a word in a sentence. Other larynx cortical representations controlling voicing and longer pitch phrase contours were found at separate sites. dLMC sites also encoded vocal pitch during a non-speech singing task. Finally, direct focal stimulation of dLMC evoked laryngeal movements and involuntary vocalization, confirming its causal role in feedforward control. Together, these results reveal the neural basis for the voluntary control of vocal pitch in human speech. VIDEO ABSTRACT.
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Affiliation(s)
- Benjamin K Dichter
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; UC Berkeley and UCSF Joint Program in Bioengineering, Berkeley, CA 94720, USA
| | - Jonathan D Breshears
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Matthew K Leonard
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Edward F Chang
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; UC Berkeley and UCSF Joint Program in Bioengineering, Berkeley, CA 94720, USA.
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36
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Martin S, Iturrate I, Millán JDR, Knight RT, Pasley BN. Decoding Inner Speech Using Electrocorticography: Progress and Challenges Toward a Speech Prosthesis. Front Neurosci 2018; 12:422. [PMID: 29977189 PMCID: PMC6021529 DOI: 10.3389/fnins.2018.00422] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 06/04/2018] [Indexed: 01/01/2023] Open
Abstract
Certain brain disorders resulting from brainstem infarcts, traumatic brain injury, cerebral palsy, stroke, and amyotrophic lateral sclerosis, limit verbal communication despite the patient being fully aware. 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. We focused on intracranial recordings, as this technique allows monitoring brain activity with high spatial, temporal, and spectral resolution, and therefore is a good candidate to investigate inner speech. Despite intense efforts, investigating how the human cortex encodes inner speech remains an elusive challenge, due to the lack of behavioral and observable measures. We emphasize various challenges commonly encountered when investigating inner speech decoding, and propose potential solutions in order to get closer to a natural speech assistive device.
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Affiliation(s)
- Stephanie Martin
- Defitech Chair in Brain Machine Interface, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - Iñaki Iturrate
- Defitech Chair in Brain Machine Interface, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - José del R. Millán
- Defitech Chair in Brain Machine Interface, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Robert T. Knight
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
- Department of Psychology, University of California, Berkeley, Berkeley, CA, United States
| | - Brian N. Pasley
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
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37
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Fisher JM, Dick FK, Levy DF, Wilson SM. Neural representation of vowel formants in tonotopic auditory cortex. Neuroimage 2018; 178:574-582. [PMID: 29860083 DOI: 10.1016/j.neuroimage.2018.05.072] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Revised: 05/29/2018] [Accepted: 05/30/2018] [Indexed: 11/25/2022] Open
Abstract
Speech sounds are encoded by distributed patterns of activity in bilateral superior temporal cortex. However, it is unclear whether speech sounds are topographically represented in cortex, or which acoustic or phonetic dimensions might be spatially mapped. Here, using functional MRI, we investigated the potential spatial representation of vowels, which are largely distinguished from one another by the frequencies of their first and second formants, i.e. peaks in their frequency spectra. This allowed us to generate clear hypotheses about the representation of specific vowels in tonotopic regions of auditory cortex. We scanned participants as they listened to multiple natural tokens of the vowels [ɑ] and [i], which we selected because their first and second formants overlap minimally. Formant-based regions of interest were defined for each vowel based on spectral analysis of the vowel stimuli and independently acquired tonotopic maps for each participant. We found that perception of [ɑ] and [i] yielded differential activation of tonotopic regions corresponding to formants of [ɑ] and [i], such that each vowel was associated with increased signal in tonotopic regions corresponding to its own formants. This pattern was observed in Heschl's gyrus and the superior temporal gyrus, in both hemispheres, and for both the first and second formants. Using linear discriminant analysis of mean signal change in formant-based regions of interest, the identity of untrained vowels was predicted with ∼73% accuracy. Our findings show that cortical encoding of vowels is scaffolded on tonotopy, a fundamental organizing principle of auditory cortex that is not language-specific.
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Affiliation(s)
- Julia M Fisher
- Department of Linguistics, University of Arizona, Tucson, AZ, USA; Statistics Consulting Laboratory, BIO5 Institute, University of Arizona, Tucson, AZ, USA
| | - Frederic K Dick
- Department of Psychological Sciences, Birkbeck College, University of London, UK; Birkbeck-UCL Center for Neuroimaging, London, UK; Department of Experimental Psychology, University College London, UK
| | - Deborah F Levy
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Stephen M Wilson
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
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38
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Hamilton LS, Edwards E, Chang EF. A Spatial Map of Onset and Sustained Responses to Speech in the Human Superior Temporal Gyrus. Curr Biol 2018; 28:1860-1871.e4. [DOI: 10.1016/j.cub.2018.04.033] [Citation(s) in RCA: 98] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Revised: 03/04/2018] [Accepted: 04/10/2018] [Indexed: 01/05/2023]
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39
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Moses DA, Leonard MK, Chang EF. Real-time classification of auditory sentences using evoked cortical activity in humans. J Neural Eng 2018; 15:036005. [PMID: 29378977 PMCID: PMC10560396 DOI: 10.1088/1741-2552/aaab6f] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Recent research has characterized the anatomical and functional basis of speech perception in the human auditory cortex. These advances have made it possible to decode speech information from activity in brain regions like the superior temporal gyrus, but no published work has demonstrated this ability in real-time, which is necessary for neuroprosthetic brain-computer interfaces. APPROACH Here, we introduce a real-time neural speech recognition (rtNSR) software package, which was used to classify spoken input from high-resolution electrocorticography signals in real-time. We tested the system with two human subjects implanted with electrode arrays over the lateral brain surface. Subjects listened to multiple repetitions of ten sentences, and rtNSR classified what was heard in real-time from neural activity patterns using direct sentence-level and HMM-based phoneme-level classification schemes. MAIN RESULTS We observed single-trial sentence classification accuracies of [Formula: see text] or higher for each subject with less than 7 minutes of training data, demonstrating the ability of rtNSR to use cortical recordings to perform accurate real-time speech decoding in a limited vocabulary setting. SIGNIFICANCE Further development and testing of the package with different speech paradigms could influence the design of future speech neuroprosthetic applications.
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Affiliation(s)
- David A Moses
- Department of Neurological Surgery, UC San Francisco, CA, United States of America
- Center for Integrative Neuroscience, UC San Francisco, CA, United States of America
- Graduate Program in Bioengineering, UC Berkeley-UC San Francisco, CA, United States of America
| | - Matthew K Leonard
- Department of Neurological Surgery, UC San Francisco, CA, United States of America
- Center for Integrative Neuroscience, UC San Francisco, CA, United States of America
| | - Edward F Chang
- Department of Neurological Surgery, UC San Francisco, CA, United States of America
- Center for Integrative Neuroscience, UC San Francisco, CA, United States of America
- Graduate Program in Bioengineering, UC Berkeley-UC San Francisco, CA, United States of America
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40
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Pfeiffer T, Knight RT, Rose G. Hidden Markov model based continuous decoding of finger movements with prior knowledge incorporation using bi-gram models. Biomed Phys Eng Express 2018. [DOI: 10.1088/2057-1976/aa99f3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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41
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Hamilton LS, Chang DL, Lee MB, Chang EF. Semi-automated Anatomical Labeling and Inter-subject Warping of High-Density Intracranial Recording Electrodes in Electrocorticography. Front Neuroinform 2017; 11:62. [PMID: 29163118 PMCID: PMC5671481 DOI: 10.3389/fninf.2017.00062] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Accepted: 10/05/2017] [Indexed: 11/13/2022] Open
Abstract
In this article, we introduce img_pipe, our open source python package for preprocessing of imaging data for use in intracranial electrocorticography (ECoG) and intracranial stereo-EEG analyses. The process of electrode localization, labeling, and warping for use in ECoG currently varies widely across laboratories, and it is usually performed with custom, lab-specific code. This python package aims to provide a standardized interface for these procedures, as well as code to plot and display results on 3D cortical surface meshes. It gives the user an easy interface to create anatomically labeled electrodes that can also be warped to an atlas brain, starting with only a preoperative T1 MRI scan and a postoperative CT scan. We describe the full capabilities of our imaging pipeline and present a step-by-step protocol for users.
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Affiliation(s)
- Liberty S Hamilton
- Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, United States.,Center for Integrative Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - David L Chang
- Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, United States.,Center for Integrative Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Morgan B Lee
- Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, United States.,Center for Integrative Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Edward F Chang
- Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, United States.,Center for Integrative Neuroscience, University of California, San Francisco, San Francisco, CA, United States
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42
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Iljina O, Derix J, Schirrmeister RT, Schulze-Bonhage A, Auer P, Aertsen A, Ball T. Neurolinguistic and machine-learning perspectives on direct speech BCIs for restoration of naturalistic communication. BRAIN-COMPUTER INTERFACES 2017. [DOI: 10.1080/2326263x.2017.1330611] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Olga Iljina
- GRK 1624 ‘Frequency effects in language’, University of Freiburg, Freiburg, Germany
- Department of German Linguistics, University of Freiburg, Freiburg, Germany
- Hermann Paul School of Linguistics, University of Freiburg, Germany
- BrainLinks-BrainTools, University of Freiburg, Freiburg, Germany
- Neurobiology and Biophysics, Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Johanna Derix
- BrainLinks-BrainTools, University of Freiburg, Freiburg, Germany
- Translational Neurotechnology Lab, Department of Neurosurgery, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Robin Tibor Schirrmeister
- BrainLinks-BrainTools, University of Freiburg, Freiburg, Germany
- Translational Neurotechnology Lab, Department of Neurosurgery, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department of Neurosurgery, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- BrainLinks-BrainTools, University of Freiburg, Freiburg, Germany
| | - Peter Auer
- GRK 1624 ‘Frequency effects in language’, University of Freiburg, Freiburg, Germany
- Department of German Linguistics, University of Freiburg, Freiburg, Germany
- Hermann Paul School of Linguistics, University of Freiburg, Germany
- Freiburg Institute for Advanced Studies (FRIAS), University of Freiburg, Freiburg, Germany
| | - Ad Aertsen
- Neurobiology and Biophysics, Faculty of Biology, University of Freiburg, Freiburg, Germany
- Bernstein Center Freiburg, University of Freiburg, Germany
| | - Tonio Ball
- BrainLinks-BrainTools, University of Freiburg, Freiburg, Germany
- Translational Neurotechnology Lab, Department of Neurosurgery, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Trumpis M, Insanally M, Zou J, Elsharif A, Ghomashchi A, Sertac Artan N, Froemke RC, Viventi J. A low-cost, scalable, current-sensing digital headstage for high channel count μECoG. J Neural Eng 2017; 14:026009. [PMID: 28102827 PMCID: PMC5385258 DOI: 10.1088/1741-2552/aa5a82] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
OBJECTIVE High channel count electrode arrays allow for the monitoring of large-scale neural activity at high spatial resolution. Implantable arrays featuring many recording sites require compact, high bandwidth front-end electronics. In the present study, we investigated the use of a small, light weight, and low cost digital current-sensing integrated circuit for acquiring cortical surface signals from a 61-channel micro-electrocorticographic (μECoG) array. APPROACH We recorded both acute and chronic μECoG signal from rat auditory cortex using our novel digital current-sensing headstage. For direct comparison, separate recordings were made in the same anesthetized preparations using an analog voltage headstage. A model of electrode impedance explained the transformation between current- and voltage-sensed signals, and was used to reconstruct cortical potential. We evaluated the digital headstage using several metrics of the baseline and response signals. MAIN RESULTS The digital current headstage recorded neural signal with similar spatiotemporal statistics and auditory frequency tuning compared to the voltage signal. The signal-to-noise ratio of auditory evoked responses (AERs) was significantly stronger in the current signal. Stimulus decoding based on true and reconstructed voltage signals were not significantly different. Recordings from an implanted system showed AERs that were detectable and decodable for 52 d. The reconstruction filter mitigated the thermal current noise of the electrode impedance and enhanced overall SNR. SIGNIFICANCE We developed and validated a novel approach to headstage acquisition that used current-input circuits to independently digitize 61 channels of μECoG measurements of the cortical field. These low-cost circuits, intended to measure photo-currents in digital imaging, not only provided a signal representing the local cortical field with virtually the same sensitivity and specificity as a traditional voltage headstage but also resulted in a small, light headstage that can easily be scaled to record from hundreds of channels.
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Affiliation(s)
- Michael Trumpis
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America. Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, United States of America
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Nourski KV, Steinschneider M, Rhone AE, Howard Iii MA. Intracranial Electrophysiology of Auditory Selective Attention Associated with Speech Classification Tasks. Front Hum Neurosci 2017; 10:691. [PMID: 28119593 PMCID: PMC5222875 DOI: 10.3389/fnhum.2016.00691] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Accepted: 12/26/2016] [Indexed: 11/30/2022] Open
Abstract
Auditory selective attention paradigms are powerful tools for elucidating the various stages of speech processing. This study examined electrocorticographic activation during target detection tasks within and beyond auditory cortex. Subjects were nine neurosurgical patients undergoing chronic invasive monitoring for treatment of medically refractory epilepsy. Four subjects had left hemisphere electrode coverage, four had right coverage and one had bilateral coverage. Stimuli were 300 ms complex tones or monosyllabic words, each spoken by a different male or female talker. Subjects were instructed to press a button whenever they heard a target corresponding to a specific stimulus category (e.g., tones, animals, numbers). High gamma (70–150 Hz) activity was simultaneously recorded from Heschl’s gyrus (HG), superior, middle temporal and supramarginal gyri (STG, MTG, SMG), as well as prefrontal cortex (PFC). Data analysis focused on: (1) task effects (non-target words in tone detection vs. semantic categorization task); and (2) target effects (words as target vs. non-target during semantic classification). Responses within posteromedial HG (auditory core cortex) were minimally modulated by task and target. Non-core auditory cortex (anterolateral HG and lateral STG) exhibited sensitivity to task, with a smaller proportion of sites showing target effects. Auditory-related areas (MTG and SMG) and PFC showed both target and, to a lesser extent, task effects, that occurred later than those in the auditory cortex. Significant task and target effects were more prominent in the left hemisphere than in the right. Findings demonstrate a hierarchical organization of speech processing during auditory selective attention.
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Affiliation(s)
- Kirill V Nourski
- Human Brain Research Laboratory, Department of Neurosurgery, The University of Iowa Iowa City, IA, USA
| | - Mitchell Steinschneider
- Departments of Neurology and Neuroscience, Albert Einstein College of Medicine Bronx, NY, USA
| | - Ariane E Rhone
- Human Brain Research Laboratory, Department of Neurosurgery, The University of Iowa Iowa City, IA, USA
| | - Matthew A Howard Iii
- Human Brain Research Laboratory, Department of Neurosurgery, The University of IowaIowa City, IA, USA; Pappajohn Biomedical Institute, The University of IowaIowa City, IA, USA
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