Dataset Information

Decoding continuous limb movements from high-density epidural electrode arrays using custom spatial filters.

ABSTRACT: Our goal was to identify spatial filtering methods that would improve decoding of continuous arm movements from epidural field potentials as well as demonstrate the use of the epidural signals in a closed-loop brain-machine interface (BMI) system in monkeys.Eleven spatial filtering options were compared offline using field potentials collected from 64-channel high-density epidural arrays in monkeys. Arrays were placed over arm/hand motor cortex in which intracortical microelectrodes had previously been implanted and removed leaving focal cortical damage but no lasting motor deficits. Spatial filters tested included: no filtering, common average referencing (CAR), principle component analysis, and eight novel modifications of the common spatial pattern (CSP) algorithm. The spatial filtering method and decoder combination that performed the best offline was then used online where monkeys controlled cursor velocity using continuous wrist position decoded from epidural field potentials in real time.Optimized CSP methods improved continuous wrist position decoding accuracy by 69% over CAR and by 80% compared to no filtering. Kalman decoders performed better than linear regression decoders and benefitted from including more spatially-filtered signals but not from pre-smoothing the calculated power spectra. Conversely, linear regression decoders required fewer spatially-filtered signals and were improved by pre-smoothing the power values. The 'position-to-velocity' transformation used during online control enabled the animals to generate smooth closed-loop movement trajectories using the somewhat limited position information available in the epidural signals. The monkeys' online performance significantly improved across days of closed-loop training.Most published BMI studies that use electrocorticographic signals to decode continuous limb movements either use no spatial filtering or CAR. This study suggests a substantial improvement in decoding accuracy could be attained by using our new version of the CSP algorithm that extends the traditional CSP method for use with continuous limb movement data.

SUBMITTER: Marathe AR

PROVIDER: S-EPMC3746986 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

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Decoding continuous limb movements from high-density epidural electrode arrays using custom spatial filters.

Marathe A R AR Taylor D M DM

Journal of neural engineering 20130423 3

<h4>Objective</h4>Our goal was to identify spatial filtering methods that would improve decoding of continuous arm movements from epidural field potentials as well as demonstrate the use of the epidural signals in a closed-loop brain-machine interface (BMI) system in monkeys.<h4>Approach</h4>Eleven spatial filtering options were compared offline using field potentials collected from 64-channel high-density epidural arrays in monkeys. Arrays were placed over arm/hand motor cortex in which intraco ...[more]

PMID: 23611833

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Project description:ObjectiveTo evaluate the potential of intracortical electrode array signals for brain-computer interfaces (BCIs) to restore lost speech, we measured the performance of decoders trained to discriminate a comprehensive basis set of 39 English phonemes and to synthesize speech sounds via a neural pattern matching method. We decoded neural correlates of spoken-out-loud words in the 'hand knob' area of precentral gyrus, a step toward the eventual goal of decoding attempted speech from ventral speech areas in patients who are unable to speak.ApproachNeural and audio data were recorded while two BrainGate2 pilot clinical trial participants, each with two chronically-implanted 96-electrode arrays, spoke 420 different words that broadly sampled English phonemes. Phoneme onsets were identified from audio recordings, and their identities were then classified from neural features consisting of each electrode's binned action potential counts or high-frequency local field potential power. Speech synthesis was performed using the 'Brain-to-Speech' pattern matching method. We also examined two potential confounds specific to decoding overt speech: acoustic contamination of neural signals and systematic differences in labeling different phonemes' onset times.Main resultsA linear decoder achieved up to 29.3% classification accuracy (chance = 6%) across 39 phonemes, while an RNN classifier achieved 33.9% accuracy. Parameter sweeps indicated that performance did not saturate when adding more electrodes or more training data, and that accuracy improved when utilizing time-varying structure in the data. Microphonic contamination and phoneme onset differences modestly increased decoding accuracy, but could be mitigated by acoustic artifact subtraction and using a neural speech onset marker, respectively. Speech synthesis achieved r = 0.523 correlation between true and reconstructed audio.SignificanceThe ability to decode speech using intracortical electrode array signals from a nontraditional speech area suggests that placing electrode arrays in ventral speech areas is a promising direction for speech BCIs.

Dataset Information

Decoding continuous limb movements from high-density epidural electrode arrays using custom spatial filters.

Publications

Decoding continuous limb movements from high-density epidural electrode arrays using custom spatial filters.

Similar Datasets

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