Dataset Information

Slow Firing Single Units Are Essential for Optimal Decoding of Silent Speech.

ABSTRACT: The motivation of someone who is locked-in, that is, paralyzed and mute, is to find relief for their loss of function. The data presented in this report is part of an attempt to restore one of those lost functions, namely, speech. An essential feature of the development of a speech prosthesis is optimal decoding of patterns of recorded neural signals during silent or covert speech, that is, speaking "inside the head" with output that is inaudible due to the paralysis of the articulators. The aim of this paper is to illustrate the importance of both fast and slow single unit firings recorded from an individual with locked-in syndrome and from an intact participant speaking silently. Long duration electrodes were implanted in the motor speech cortex for up to 13 years in the locked-in participant. The data herein provide evidence that slow firing single units are essential for optimal decoding accuracy. Additional evidence indicates that slow firing single units can be conditioned in the locked-in participant 5 years after implantation, further supporting their role in decoding.

SUBMITTER: Ganesh A

PROVIDER: S-EPMC9382878 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Slow Firing Single Units Are Essential for Optimal Decoding of Silent Speech.

Ganesh Ananya A Cervantes Andre J AJ Kennedy Philip R PR

Frontiers in human neuroscience 20220803

The motivation of someone who is locked-in, that is, paralyzed and mute, is to find relief for their loss of function. The data presented in this report is part of an attempt to restore one of those lost functions, namely, speech. An essential feature of the development of a speech prosthesis is optimal decoding of patterns of recorded neural signals during <i>silent or covert</i> speech, that is, speaking "inside the head" with output that is inaudible due to the paralysis of the articulators. ...[more]

PMID: 35992944

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Project description:Background and Objectives: Recent studies highlight the importance of investigating biomarkers for diagnosing and classifying patients with primary progressive aphasia (PPA). Even though there is ongoing research on pathophysiological indices in this field, the use of behavioral variables, and especially speech-derived factors, has drawn little attention in the relevant literature. The present study aims to investigate the possible utility of speech-derived indices, particularly silent pauses, as biomarkers for primary progressive aphasia (PPA). Materials and Methods: We recruited 22 PPA patients and 17 healthy controls, from whom we obtained speech samples based on two elicitation tasks, i.e., cookie theft picture description (CTP) and the patients' personal narration of the disease onset and course. Results: Four main indices were derived from these speech samples: speech rate, articulation rate, pause frequency, and pause duration. In order to investigate whether these indices could be used to discriminate between the four groups of participants (healthy individuals and the three patient subgroups corresponding to the three variants of PPA), we conducted three sets of analyses: a series of ANOVAs, two principal component analyses (PCAs), and two hierarchical cluster analyses (HCAs). The ANOVAs revealed significant differences between the four subgroups for all four variables, with the CTP results being more robust. The subsequent PCAs and HCAs were in accordance with the initial statistical comparisons, revealing that the speech-derived indices for CTP provided a clearer classification and were especially useful for distinguishing the non-fluent variant from healthy participants as well as from the two other PPA taxonomic categories. Conclusions: In sum, we argue that speech-derived indices, and especially silent pauses, could be used as complementary biomarkers to efficiently discriminate between PPA and healthy speakers, as well as between the three variants of the disease.

Dataset Information

Slow Firing Single Units Are Essential for Optimal Decoding of Silent Speech.

Publications

Slow Firing Single Units Are Essential for Optimal Decoding of Silent Speech.

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