دورية أكاديمية
Decoding imagined speech with delay differential analysis.
| العنوان: | Decoding imagined speech with delay differential analysis. |
|---|---|
| المؤلفون: | Carvalho VR; RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway.; Postgraduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil., Mendes EMAM; Postgraduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil., Fallah A; Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, United States., Sejnowski TJ; Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, United States.; Institute for Neural Computation University of California, San Diego, La Jolla, CA, United States.; Department of Neurobiology, University of California, San Diego, La Jolla, CA, United States., Comstock L; Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States., Lainscsek C; Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, United States.; Institute for Neural Computation University of California, San Diego, La Jolla, CA, United States. |
| المصدر: | Frontiers in human neuroscience [Front Hum Neurosci] 2024 May 17; Vol. 18, pp. 1398065. Date of Electronic Publication: 2024 May 17 (Print Publication: 2024). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101477954 Publication Model: eCollection Cited Medium: Print ISSN: 1662-5161 (Print) Linking ISSN: 16625161 NLM ISO Abbreviation: Front Hum Neurosci Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: Lausanne, Switzerland : Frontiers Research Foundation, 2008- |
| مستخلص: | Speech decoding from non-invasive EEG signals can achieve relatively high accuracy (70-80%) for strictly delimited classification tasks, but for more complex tasks non-invasive speech decoding typically yields a 20-50% classification accuracy. However, decoder generalization, or how well algorithms perform objectively across datasets, is complicated by the small size and heterogeneity of existing EEG datasets. Furthermore, the limited availability of open access code hampers a comparison between methods. This study explores the application of a novel non-linear method for signal processing, delay differential analysis (DDA), to speech decoding. We provide a systematic evaluation of its performance on two public imagined speech decoding datasets relative to all publicly available deep learning methods. The results support DDA as a compelling alternative or complementary approach to deep learning methods for speech decoding. DDA is a fast and efficient time-domain open-source method that fits data using only few strong features and does not require extensive preprocessing. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision. (Copyright © 2024 Carvalho, Mendes, Fallah, Sejnowski, Comstock and Lainscsek.) |
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| فهرسة مساهمة: | Keywords: delay differential analysis; electroencephalography; non-linear dynamics; signal processing; speech decoding |
| تواريخ الأحداث: | Date Created: 20240603 Latest Revision: 20240604 |
| رمز التحديث: | 20240604 |
| مُعرف محوري في PubMed: | PMC11140152 |
| DOI: | 10.3389/fnhum.2024.1398065 |
| PMID: | 38826617 |
| قاعدة البيانات: | MEDLINE |
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