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Paul Ferrari - IEEE Xplore Author Profile

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Author details

Paul Ferrari

Publications

12

Citations

95

Publications by Year

20162023

Co-Authors:

Larry AbrahamNatasha AlvesAbbas Babajani-FeremiAmir BornaTom Chau

Show All Co-Authors (27)

Paul Ferrari

Affiliation

Helen DeVos Children's Hospital

Spectrum Health

Grand Rapids, MI, United States

Publication Topics

Decoding Performance,
Amyotrophic Lateral Sclerosis,
Electrocorticography,
Locked-in Syndrome,
Magnetoencephalography Signals,
Neural Decoding,
Support Vector Machine,
Imagination,
Linear Discriminant Analysis,
Speech Decoding,
Average Accuracy,
Brain Activity

Biography

Authors’ photographs and biographies not available at the time of publication.(Based on document published on 11 November 2015).

Publications

12

Citations

95

Publications by Year

20162023

Co-Authors:

Larry Abraham
Natasha Alves
Abbas Babajani-Feremi
Amir Borna
Tom Chau

Show All Co-Authors (27)

Author's Published Works

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Showing 1-12 of 12 results

Conferences (10)

Journals (2)

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Paul Ferrari(12)
Jun Wang(10)
Debadatta Dash(9)
Amir Borna(3)
Saleem Malik(2)
Department of Psychology, University of Texas at Austin, Austin, Texas, United States(2)
Semiconductor Material and Device Sciences Department, Sandia National Laboratories, Albuquerque, NM, USA(2)
Department of Psychology, The University of Texas at Austin, and the Research Director of the MEG lab, Dell Children’s Medical Center, Austin, TX, USA(2)
Department of Bioengineering and Callier Center for Communication Disorders, The University of Texas at Dallas, TX, USA(1)
Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, USA(1)
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)(1)
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(1)
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)(1)
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)(1)
2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)(1)
IEEE(12)
Amsterdam, Netherlands(1)
Anaheim, CA, USA(1)
Baltimore, MD, USA(1)
Berlin, Germany(1)
Glasgow, UK(1)
Magnetoencephalography Signals(9)
Amyotrophic Lateral Sclerosis(8)
Locked-in Syndrome(7)
Brain Activity(6)
Decoding Performance(6)

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Subject Generalization in Classifying Imagined and Spoken Speech with MEG

Debadatta Dash;Paul Ferrari;Abbas Babajani-Feremi;David Harwath;Amir Borna;Jun Wang

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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Speech decoding-based brain-computer interfaces (Speech-BCIs) decode speech directly from brain signals, which have the potential to offer faster and natural communication to patients with locked-in syndrome than the current BCI-spellers. On account of the huge cognitive variance among subjects, most of the current speech-BCI models have focused on subject-dependent decoding where the training and...Show More

Subject Generalization in Classifying Imagined and Spoken Speech with MEG

Debadatta Dash;Paul Ferrari;Abbas Babajani-Feremi;David Harwath;Amir Borna;Jun Wang

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)

Year: 2023 | Conference Paper |

Magnetometers vs Gradiometers for Neural Speech Decoding

Debadatta Dash;Paul Ferrari;Abbas Babajani-Feremi;Amir Borna;Peter D. D. Schwindt;Jun Wang

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Year: 2021 | Conference Paper |

Cited by: Papers (2)

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Neural speech decoding aims at providing natural rate communication assistance to patients with locked-in state (e.g. due to amyotrophic lateral sclerosis, ALS) in contrast to the traditional brain-computer interface (BCI) spellers which are slow. Recent studies have shown that Magnetoencephalography (MEG) is a suitable neuroimaging modality to study neural speech decoding considering its excellen...Show More

Magnetometers vs Gradiometers for Neural Speech Decoding

Debadatta Dash;Paul Ferrari;Abbas Babajani-Feremi;Amir Borna;Peter D. D. Schwindt;Jun Wang

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Year: 2021 | Conference Paper |

Visual Decoding of Phrases from Occipital Neuromagnetic Signals

Debadatta Dash;Paul Ferrari;Amir Borna;Joonas Iivanainen;Peter D. D. Schwindt;Jun Wang

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)

Year: 2021 | Conference Paper |

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Orthographic visual perception (reading) is encoded via a widespread dynamic interaction between different language centers of the brain and visual cortex. In this study, we investigated orthographic visual perception decoding with Magnetoencephalography (MEG), where phrases were visually presented to participants. We compared the decoding performance obtained with sensors within the occipital lob...Show More

Visual Decoding of Phrases from Occipital Neuromagnetic Signals

Debadatta Dash;Paul Ferrari;Amir Borna;Joonas Iivanainen;Peter D. D. Schwindt;Jun Wang

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)

Year: 2021 | Conference Paper |

Role of Brainwaves in Neural Speech Decoding

Debadatta Dash;Paul Ferrari;Jun Wang

2020 28th European Signal Processing Conference (EUSIPCO)

Year: 2021 | Conference Paper |

Cited by: Papers (7)

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Neural speech decoding aims at direct decoding of speech from the brain to restore speech communication in patients with locked-in syndrome (fully paralyzed but aware). Despite the recent progress, exactly which aspects of neural activities are characterizing the decoding process is still unclear. Neural oscillations have been associated with playing a key functional role in neural information pro...Show More

Role of Brainwaves in Neural Speech Decoding

Debadatta Dash;Paul Ferrari;Jun Wang

2020 28th European Signal Processing Conference (EUSIPCO)

Year: 2021 | Conference Paper |

MEG Sensor Selection for Neural Speech Decoding

Debadatta Dash;Alan Wisler;Paul Ferrari;Elizabeth Moody Davenport;Joseph Maldjian;Jun Wang

Year: 2020 | Volume: 8 | Journal Article |

Cited by: Papers (20)

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Direct decoding of speech from the brain is a faster alternative to current electroencephalography (EEG) speller-based brain-computer interfaces (BCI) in providing communication assistance to locked-in patients. Magnetoencephalography (MEG) has recently shown great potential as a non-invasive neuroimaging modality for neural speech decoding, owing in part to its spatial selectivity over other high...Show More

MEG Sensor Selection for Neural Speech Decoding

Debadatta Dash;Alan Wisler;Paul Ferrari;Elizabeth Moody Davenport;Joseph Maldjian;Jun Wang

Year: 2020 | Volume: 8 | Journal Article |

Decoding Speech Evoked Jaw Motion from Non-invasive Neuromagnetic Oscillations

Debadatta Dash;Paul Ferrari;Jun Wang

2020 International Joint Conference on Neural Networks (IJCNN)

Year: 2020 | Conference Paper |

Cited by: Papers (8)

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Speech decoding-based brain-computer interfaces (BCIs) are the next-generation neuroprostheses that have the potential for real-time communication assistance to patients with locked-in syndrome (fully paralyzed but aware). Recent invasive speech decoding studies have demonstrated the possibility of speech kinematics decoding, where articulatory movements were decoded from the brain activity signal...Show More

Decoding Speech Evoked Jaw Motion from Non-invasive Neuromagnetic Oscillations

Debadatta Dash;Paul Ferrari;Jun Wang

2020 International Joint Conference on Neural Networks (IJCNN)

Year: 2020 | Conference Paper |

Decoding Speech from Single Trial MEG Signals Using Convolutional Neural Networks and Transfer Learning

Debadatta Dash;Paul Ferrari;Daragh Heitzman;Jun Wang

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year: 2019 | Conference Paper |

Cited by: Papers (11)

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Decoding speech directly from the brain has the potential for the development of the next generation, more efficient brain computer interfaces (BCIs) to assist in the communication of patients with locked-in syndrome (fully paralyzed but aware). In this study, we have explored the spectral and temporal features of the magnetoencephalography (MEG) signals and trained those features with convolution...Show More

Decoding Speech from Single Trial MEG Signals Using Convolutional Neural Networks and Transfer Learning

Debadatta Dash;Paul Ferrari;Daragh Heitzman;Jun Wang

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year: 2019 | Conference Paper |

Automatic Speech Activity Recognition from MEG Signals Using Seq2Seq Learning

Debadatta Dash;Paul Ferrari;Saleem Malik;Jun Wang

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)

Year: 2019 | Conference Paper |

Cited by: Papers (7)

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Accurate interpretation of speech activity from brain signals is critical for understanding the relationship between neural patterns and speech production. Current research on speech activity recognition from the brain activity heavily relies on the region of interest (ROI) based functional connectivity analysis or source separation strategies to map the activity as a spatial localization of a bra...Show More

Automatic Speech Activity Recognition from MEG Signals Using Seq2Seq Learning

Debadatta Dash;Paul Ferrari;Saleem Malik;Jun Wang

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)

Year: 2019 | Conference Paper |

OVERT SPEECH RETRIEVAL FROM NEUROMAGNETIC SIGNALS USING WAVELETS AND ARTIFICIAL NEURAL NETWORKS

Debadatta Dash;Paul Ferrari;Saleem Malik;Jun Wang

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

Year: 2018 | Conference Paper |

Cited by: Papers (14)

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Speech production involves the synchronization of neural activity between the speech centers of the brain and the oral-motor system, allowing for the conversion of thoughts into meaningful sounds. This hierarchical mechanism is hindered due to partial or complete paralysis of the articulators for patients suffering from locked-in-syndrome. These patients are in dire need of effective brain-communi...Show More

OVERT SPEECH RETRIEVAL FROM NEUROMAGNETIC SIGNALS USING WAVELETS AND ARTIFICIAL NEURAL NETWORKS

Debadatta Dash;Paul Ferrari;Saleem Malik;Jun Wang

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

Year: 2018 | Conference Paper |

An MRI-compatible force sensor for measuring differential isometric precision grip force

Chungmin Han;Ethan Oblak;Larry Abraham;Paul Ferrari;Mark McManis;David Schnyer;James Sulzer

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year: 2017 | Conference Paper |

Cited by: Papers (1)

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Investigating neural correlates of fine motor control in a magnetically sensitive environment requires special considerations in sensor design. Our application requires measurement of forefinger and thumb forces during precision grip in a relatively low (<; 20 N) force range. This work describes the design, characterization and performance evaluation of an MR-compatible precision grip sensor that ...Show More

An MRI-compatible force sensor for measuring differential isometric precision grip force

Chungmin Han;Ethan Oblak;Larry Abraham;Paul Ferrari;Mark McManis;David Schnyer;James Sulzer

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year: 2017 | Conference Paper |

Towards decoding speech production from single-trial magnetoencephalography (MEG) signals

Jun Wang;Myungjong Kim;Angel W. Hernandez-Mulero;Daragh Heitzman;Paul Ferrari

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2017 | Conference Paper |

Cited by: Papers (11)

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Patients with locked-in-syndrome (fully paralyzed but aware) struggle in their life and communication. Providing a level of communication offers these patients a chance to resume a meaningful life. Current brain-computer interface (BCI) communication requires users to build words from single letters selected on a screen, which is extremely inefficient. Faster approaches for their speech communicat...Show More

Towards decoding speech production from single-trial magnetoencephalography (MEG) signals

Jun Wang;Myungjong Kim;Angel W. Hernandez-Mulero;Daragh Heitzman;Paul Ferrari

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2017 | Conference Paper |

An MEG-Compatible Electromagnetic-Tracking System for Monitoring Orofacial Kinematics

Natasha Alves;Cecilia Jobst;Fanny Hotze;Paul Ferrari;Marc Lalancette;Tom Chau;Pascal van Lieshout;Douglas Cheyne

IEEE Transactions on Biomedical Engineering

Year: 2016 | Volume: 63, Issue: 8 | Journal Article |

Cited by: Papers (13)

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We describe a novel motion-tracking system, called MASK (magnetoarticulography for the assessment of speech kinematics) designed to track detailed orofacial movements during magnetoencephalographic (MEG) measures of human brain activity. A three-dimensional electromagnetic-tracking method was employed using lightweight coils energized with high-frequency sinusoidal currents, creating magnetic dipo...Show More

An MEG-Compatible Electromagnetic-Tracking System for Monitoring Orofacial Kinematics

Natasha Alves;Cecilia Jobst;Fanny Hotze;Paul Ferrari;Marc Lalancette;Tom Chau;Pascal van Lieshout;Douglas Cheyne

IEEE Transactions on Biomedical Engineering

Year: 2016 | Volume: 63, Issue: 8 | Journal Article |

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