WO2011127483A1 - Décodage de mots à l'aide de signaux neuronaux - Google Patents

Décodage de mots à l'aide de signaux neuronaux Download PDF

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Publication number
WO2011127483A1
WO2011127483A1 PCT/US2011/031995 US2011031995W WO2011127483A1 WO 2011127483 A1 WO2011127483 A1 WO 2011127483A1 US 2011031995 W US2011031995 W US 2011031995W WO 2011127483 A1 WO2011127483 A1 WO 2011127483A1
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WIPO (PCT)
Prior art keywords
electrodes
word
frequency
classifier
domain information
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PCT/US2011/031995
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English (en)
Inventor
Bradley Greger
Paul House
Spencer Kellis
Kyle Thomson
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University Of Utah Research Foundation
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Publication of WO2011127483A1 publication Critical patent/WO2011127483A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4803—Speech analysis specially adapted for diagnostic purposes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/24—Speech recognition using non-acoustical features
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the present disclosure relates to methods and systems for decoding neural signals, e.g., local field potentials recorded from a brain cortical surface.
  • Pathological conditions such as amyotrophic lateral sclerosis or damage to the brainstem can leave patients paralyzed but fully aware, in a condition known as locked-in syndrome. Communication in this state is laborious, often reduced to selecting individual letters or words by arduous residual movement.
  • Certain embodiments of the present technology provide systems and methods for decoding words using neural signals, e.g., local field potentials, recorded from a cortical surface.
  • neural signals e.g., local field potentials
  • a system for decoding words using neural signals comprises a receiver configured to receive a neural signal from each of a plurality of electrodes implanted in a patient when the patient speaks or attempts to speak a word.
  • the system further comprises a processor configured to convert the neural signal for each of the plurality of electrodes into frequency-domain information, and to apply a classifier to the frequency-domain information for the plurality of electrodes to decode the word.
  • the plurality of electrodes are placed over a cortical surface.
  • the plurality of electrodes are placed over a face motor cortex.
  • each neural signal comprises a local field potential from the cortical surface.
  • the frequency-domain information for each of the plurality of electrodes comprises a power spectra.
  • the plurality of electrodes comprise micro-electrodes.
  • the classifier comprises a principal component analysis classifier.
  • the processor is configured to decode the word by finding a centroid from a plurality of centroids that is nearest to an output of the principal component analysis classifier.
  • a method of decoding words using neural signals comprises receiving a neural signal from each of a plurality of electrodes implanted in a patient when the patient speaks or attempts to speak a word, and converting the neural signal for each of the plurality of electrodes into frequency-domain information.
  • the method further comprises applying a classifier to the frequency-domain information for the plurality of electrodes to decode the word.
  • the plurality of electrodes are placed over a cortical surface.
  • the plurality of electrodes are placed over a face motor cortex.
  • each neural signal comprises a local field potential from the cortical surface.
  • the frequency-domain information for each of the plurality of electrodes comprises a power spectra.
  • the plurality of electrodes comprise micro-electrodes.
  • the classifier is a principle component analysis classifier.
  • the method further comprising finding a centroid from a plurality of centroids that is nearest to an output of the principal component analysis classifier.
  • a method of training a classifier to decode words using neural signals comprises receiving a neural signal from each of a plurality of electrodes implanted in a patient for each one of a plurality of trials, wherein the patient speaks or attempts to speak the word for each trial, and for each trial, converting the neural signal for each of the plurality of electrodes into frequency- domain information.
  • the method further comprises training the classifier to decode the word based on the frequency-domain information for each of the plurality of electrodes for each trial.
  • the plurality of electrodes are placed over a cortical surface.
  • the frequency-domain information for each of the plurality of electrodes and each trial comprises a power spectra.
  • the training the classifier comprises performing a principal component analysis on the frequency-domain information for the plurality of electrodes for each trial.
  • Fig. la shows an example of a 16-channel 4x4 micro-electrode array.
  • Fig. lb shows placement of two micro-electrode over a cortical surface, in which one of the micro-electrode arrays is placed over the face motor cortex and the other micro- electrode array is placed over Wernicke's area.
  • Fig. lc shows an audio waveform (top) of a verbal task and a corresponding spectrogram (bottom) of neural data recorded from a single channel over the face motor cortex.
  • Fig. Id shows an audio waveform (top) of conversation, verbal task and verbal reward (top) and a corresponding spectrogram (bottom) of neural data recorded from a single channel over Wernicke's area.
  • Fig. 2a shows windows temporally aligned to spoken words that contain a frequency-domain structure in a spectrogram of neural data recorded from a micro-electrode over the face motor cortex.
  • Fig. 2b shows power spectra calculated for multiple trials and multiple electrodes.
  • Fig. 2c shows a two-dimensional matrix of micro-electrode power spectra and trial information.
  • Fig. 2d shows a principal component analysis performed on micro-electrode power spectra and trial information for two words.
  • Fig. 3a shows a distribution of performance results for each unique combination of two- word through ten- word combinations.
  • Fig. 3b shows a topography of channel performance for micro-electrodes resting over the face motor cortex.
  • Fig. 3c shows a topography of channel performance for micro-electrodes resting over Wernicke's area.
  • Fig. 4 shows a block diagram of a system for recording and analyzing data from a micro-electrode array according to some embodiments of the present invention.
  • Pathological conditions such as amyotrophic lateral sclerosis or damage to the brainstem can leave patients severely paralyzed but fully aware, in a condition known as locked- in syndrome. Communication in this state is laborious, often reduced to selecting individual letters or words by arduous residual movement. More intuitive communication may be possible by directly interfacing with language areas of the cerebral cortex. Many studies of neural interfaces for communication have focused on the challenging problem of reconstructing continuous, dynamic speech. Described herein is a more tractable approach of classifying a set of words.
  • a grid or array of subdural, nonpenetrating, high-impedance micro-electrodes are used to record local field potentials (LFPs) from the cortical surface over the face motor cortex and Wernicke's area.
  • LFP local field potentials
  • a LFP may be an electric field potential from a group of neutrons located near the corresponding electrode.
  • Neural data from many regions of the brain may be used to decode speech; however, data from electrodes over the face motor cortex were found to be the most accurately decodable.
  • Embodiments of the present invention provide a trial- by-trial decoding of spoken words from cortical surface LFPs in the human neocortex, as discussed further below.
  • BCIs brain computer interfaces
  • Penetrating electrodes have been used to perform rapid decoding of continuous motor movements from neuronal activity in the primary motor area of human neocortex; however, because of the risks associated with implantation in language centers, few studies have explored their use in speech BCIs.
  • the neurotrophic electrode is a penetrating electrode designed to mitigate the risks of chronic implantation that has been used to decode the formant frequencies of speech from neuronal activity in the left ventral premotor cortex.
  • Embodiments of the present invention provide a novel recording device and method for decoding speech.
  • LFPs local field potentials
  • a micro- electrode array may comprise a plurality of nonpenetrating, 40- ⁇ microwires with 1-mm inter- electrode spacing.
  • Such micro-electrode grids or arrays have been shown to support high temporal- and spatial-resolution recordings.
  • embodiments of the present invention decode speech by classifying finite sets of words from cortical surface LFPs, thereby reducing the complexity of the problem to determining a limited number of classes.
  • Fig. la shows an example of a single 16-channel 4x4 micro-electrode grid or array that may be used to record LFPs on the cortical surface.
  • the micro-electrode array is shown next to a U.S. quarter-dollar coin for size comparison.
  • Fig. lb shows two 16-channel 4x4 micro-electrode arrays placed beneath the dura closely approximated to the cortical surface over the face motor cortex and Wernicke's area.
  • the wire bundle 1 12a leads to the array 110a over Wernicke's area and the wire bundle 112b leads to the array 110b over the face motor cortex.
  • EoG electrocorticographic
  • Fig. lc shows an audio waveform (top) of a verbal task, in which a patient repeated the word "yes.”
  • Fig. lc also shows a corresponding spectrogram (bottom) of neural data recorded from a single channel or micro-electrode over the face motor cortex.
  • Fig. lc includes a normalized power scale indicating the power levels in the spectrogram. As shown in Fig. lc, the spectrogram reveals frequency-domain structure aligned to the individual words during the verbal task.
  • Fig. Id shows an audio waveform (top) of conversation, verbal task and verbal reward and a corresponding spectrogram (bottom) of neural data recorded from a single channel over Wernicke's area.
  • Wernicke's area is predominantly active when the patient converses and receives verbal rewards after completing an experiment, and was less active during the verbal task.
  • PCA principal component analysis
  • Fig. 2a shows an example of spectrograms 210a-210d of neural data for four different electrodes of a micro-electrode array placed over the face motor cortex.
  • a particular word is repeated three times during a verbal task with each repetition of the word corresponding to a trial.
  • the subject may speak the word or attempt to speak the word for the case where the subject is unable to intelligibly vocalize the word.
  • Fig. 2a shows three 500-msec windows 220a-220c where each window is temporally aligned to one instance of the spoken word. As shown in Fig.
  • the windows 220a- 220c contain frequency-domain structure in each spectrogram 210a-210d corresponding to the spoken word at the three trials.
  • Fig. 2b shows a power spectra for each electrode 210a-210d and each trial.
  • Fig. 2c shows a two-dimensional matrix of micro-electrode power spectra and trial information for a word.
  • power spectra information is collected for each of N electrodes of the array and each of M trials.
  • Fig. 2d shows a principal component analysis performed on micro-electrode power spectra and trial information for the words "hungry” and "thirsty.”
  • principal component analysis performed on micro-electrode power spectra and trial information for the word "hungry” generates a cluster 250 in the principal component space, where each point in the cluster 250 represents one trial.
  • principal component analysis performed on micro-electrode power spectra and trial information for the word "thirsty” generates a cluster 255 in the principal component space.
  • three dimensions of the principal component space are shown for ease of illustration, although it is to be understood that the principal component space may comprise any number of dimensions.
  • a center of mass or centroid may be computed for each cluster corresponding to a particular word.
  • the word may be classified by performing principal component analysis on micro-electrode spectra information from the patient to project the spectra
  • classification examples include maximum likelihood, support vector machine and Bayesian classification.
  • FIG. 3b,c shows performance results for individual electrodes over the face motor cortex for different words
  • Fig. 3c shows performance results for individual electrodes over Wernicke's area for different words. Examining the mean performance of each word against all other words, it was found that electrode 14 ranged from 51.5% accuracy for the word "cold” to 81.5% accuracy for the word "yes.” The standard deviation of performance across all 16 motor-sensory electrodes was measured as 6.6 ⁇ 1.5 percentage points, suggesting that surface LFPs recorded from some electrodes corresponded to aspects of speech production present in some words but not others.
  • micro-electrode that provided the highest accuracy for any single word varied. Selecting the five electrodes of the array with best overall accuracy from the face motor cortex improved classification accuracy to 89.6 ⁇ 10.8% of two-word combinations (median 90.0%; Fig. 3a). However, selecting the five highest-performing electrodes over Wernicke's area did not improve performance (73.5 ⁇ 16.4% of two-word combinations correctly classified; median 73.3%) when compared with using all 16 electrodes over that region of cortex. Some micro- electrodes over the face motor cortex may not have recorded neural signals useful in decoding the specific set of words presented, indicating a more concrete mapping of the neural signal onto patterns of speech articulation. Conversely, most of the 16 micro-electrodes over Wernicke's area appear to have recorded neural signal related to language processing, supporting a more distributed and abstract encoding of speech.
  • micro-electrode grids or array could be reduced with epidural placement, as shown for similar recording devices. Furthermore, a wireless
  • Training and decoding used subsets of channels and combinations of two through ten words. Mean, median, and standard deviation were computed for results of each
  • Topographical performance [0065] The algorithm was run using data from each electrode individually and for all combinations of two words. Classification accuracies from all combinations involving the selected word and channel were averaged.
  • FIG. 4 is block diagram showing an example of a system 450 for recording and processing LFPs from an micro-electrode array 410 that may be used for various embodiments of the invention.
  • the system 450 may include a receiver 452, a processor 455, and a memory 460.
  • the receiver 452 may be used to condition the electrical signals from the micro-electrode array 410 for processing by the processor 455.
  • the receiver 452 may include one or more of the following components: amplifiers (e.g., low-noise amplifiers) for amplifying the electrical signals, a filter for isolating electrical signals within a desired frequency bandwidth, and an analog-to-digital converter for digitizing the electrical signals for processing by the processor 455.
  • amplifiers e.g., low-noise amplifiers
  • filter for isolating electrical signals within a desired frequency bandwidth
  • an analog-to-digital converter for digitizing the electrical signals for processing by the processor 455.
  • the processor 455 may comprise a general purpose processor, a digital signal processors (DSPs), application specific integrated circuit (ASICs), discrete hardware
  • the memory 460 may comprise any computer-readable media known in the art including volatile memory, nonvolatile memory, a Random Access Memory (RAM), a flash memory, a Read Only Memory (ROM), a removable disk, a CD-ROM, a DVD, any other suitable storage device, or a combination thereof.
  • RAM Random Access Memory
  • ROM Read Only Memory
  • the processor 455 may also output raw electrical signals, processed electrical signals, and/or results of analysis to an output device 465, including, but not limited to, a display for viewing by a neurologist, a printer for generating a computer readout, a computer-readable media, and/or to another computer via a computer network connection.
  • the output device 465 may also include an audio output device that outputs the decoded word as an audio output, e.g., a synthetic voice vocalizing the decoded word.
  • the processor 455 may decode a word by receiving neural signals, e.g., local field potentials, from the micro-electrode array 410 when the patient speaks the word or attempts to speak the word.
  • the processor 455 may then convert the neural signals into frequency-domain information, e.g., power spectra, for one or more electrodes of the array.
  • the processor 455 may then classify the frequency-domain information for the one or more electrodes into one of a set of words.
  • the processor 455 may perform principal . component analysis on the frequency-domain information to project the frequency-domain information into the principal component space and determine its nearest centriod in the principal component space, as described above.
  • the processor 455 may display the decoded word on a display and/or vocalize the decoded word from an audio output device.
  • the processor 455 may be trained to classify a particular word using the methods described above with reference to Figs. 2a-2d.

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Abstract

L'invention porte sur des procédés et des systèmes pour le décodage de mots à l'aide de signaux neuronaux, par exemple des potentiels de champ locaux, enregistrés à partir de, par exemple, à partir d'une surface corticale cérébrale. Certains procédés comprennent la réception de signaux neuronaux provenant d'une pluralité d'électrodes en contact avec un patient lorsque le patient prononce, ou essaie de prononcer, un mot; la conversion du signal neuronal pour chaque électrode en informations dans le domaine fréquentiel; et l'application d'un classificateur sur les informations dans le domaine fréquentiel pour la pluralité d'électrodes de façon à déterminer le mot.
PCT/US2011/031995 2010-04-09 2011-04-11 Décodage de mots à l'aide de signaux neuronaux WO2011127483A1 (fr)

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US61/322,797 2010-04-09

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014066855A1 (fr) * 2012-10-26 2014-05-01 The Regents Of The University Of California Procédés de décodage de la parole à partir de données d'activités cérébrales, et dispositifs de mise en œuvre des procédés
US10653330B2 (en) 2016-08-25 2020-05-19 Paradromics, Inc. System and methods for processing neural signals

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US6020110A (en) * 1994-06-24 2000-02-01 Cambridge Sensors Ltd. Production of electrodes for electrochemical sensing
US20060217782A1 (en) * 1998-10-26 2006-09-28 Boveja Birinder R Method and system for cortical stimulation to provide adjunct (ADD-ON) therapy for stroke, tinnitus and other medical disorders using implantable and external components
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014066855A1 (fr) * 2012-10-26 2014-05-01 The Regents Of The University Of California Procédés de décodage de la parole à partir de données d'activités cérébrales, et dispositifs de mise en œuvre des procédés
US10264990B2 (en) 2012-10-26 2019-04-23 The Regents Of The University Of California Methods of decoding speech from brain activity data and devices for practicing the same
US10653330B2 (en) 2016-08-25 2020-05-19 Paradromics, Inc. System and methods for processing neural signals

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