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Inner Speech Decoding: A Comprehensive Review

delete2025-11-02
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PRE
AI
M
Maram Fahaad Almufareh *
S
Sumaira Kausar *
M
Mamoona Humayun
S
Samabia Tehsin
A
Asad Farooq
DOI:10.1002/wcs.70016delete
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Abstract

Abstract

En 中文
Inner speech decoding is the process of identifying silently generated speech from neural signals. In recent years, this candidate technology has gained momentum as a possible way to support communication in severely impaired populations. Specifically, this approach promises hope for people with a variety of physical or neurological disabilities who need alternative means of verbal expression. This review covers recording modalities that range from the noninvasive EEG to the high-density electrocorticography and discusses how linear discriminant analysis, deep convolutional networks, and hybrid fusion of EEG with fMRI are integrated into machine learning strategies to infer covert speech. This review synthesizes evidence to suggest that small vocabularies, under controlled conditions, can yield relatively reasonable accuracy while further refining the decoding outcome via context-based approaches. The impact of sensor quality, training data size, and domain adaptation is illustrated by focusing on public datasets of imagined or articulated speech. Throughout the article, the methodological standards emerging across laboratories will be discussed, emphasizing that effective inner speech recognition involves high-quality preprocessing, subject calibration, and informed modeling choices balanced against computational power for interpretability. In addition to technical advancements, this review also examines the ethical, societal, and regulatory challenges surrounding inner speech decoding, including brain data privacy, neural rights, informed consent, and user trust. Addressing these interdisciplinary issues is critical for the responsible development and real-world adoption of such technologies.
Keywords:
brain–computer interface (BCI)
EEG–fMRI fusion
inner speech recognition
silent speech interfaces

Journal

W
WIREs Cognitive Science
IF:
2.4
Papers:
19
Citations:
0

Organization

J
jouf university
Scholars:
722
Papers: 546
Citations: 0
U
University of Roehampton
Scholars:
66
Papers: 62
Citations: 992
B
Bahria University
Scholars:
354
Papers: 221
Citations: 847
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Cited Papers

Cited Papers

Leveraging Motor Imagery Rehabilitation for Individuals with Disabilities: A Review
err2023-09-29
err0
errOAAI
errMaram Fahaad Almufareh; Sumaira Kausar; Mamoona Humayun; Samabia Tehsin
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Bimodal electroencephalography-functional magnetic resonance imaging dataset for inner-speech recognition
err2023-06-13
err0
errOAAI
errFoteini Simistira Liwicki; Vibha Gupta; Rajkumar Saini; Kanjar De; Nosheen Abid; Sumit Rakesh; Scott Wellington; Holly Wilson; Marcus Liwicki; Johan Eriksson
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Thinking out loud, an open-access EEG-based BCI dataset for inner speech recognition
err2022-02-14
err0
errOAAI
errNicolás Nieto; Victoria Peterson; Hugo Leonardo Rufiner; Juan Esteban Kamienkowski; Ruben Spies
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Real-time decoding of question-and-answer speech dialogue using human cortical activity
err2019-07-30
err144
errOAAI
errMoses, David A.; Leonard, Matthew K.; Makin, Joseph G.; Chang, Edward F.
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Four ethical priorities for neurotechnologies and AI
errNATURE
IF48.5
err2017-11-09
err231
errOAAI
errYuste, Rafael; Goering, Sara
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