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The literature establishes that electroencephalography (EEG) is a well-implemented means of studying and diagnosing mental disorders. However, it is known that speech and language provide unique and essential information about human thought. Semantic and emotional content, semantic coherence, syntactic structure, and complexity can thus be combined in a machine learning process to detect schizophrenia. Several studies show that early identification is crucial to prevent the onset of illness or mitigate possible complications. Therefore, it is necessary to identify disease-specific biomarkers for an early diagnosis support system. This work contributes to improving our knowledge about schizophrenia and the features that can identify this mental illness via speech and EEG. The emotional state is a specific characteristic of schizophrenia that can be identified with speech emotion analysis. The most used features of speech found in the literature review are fundamental frequency (F0), intensity\/loudness (I), frequency formants (F1, F2, and F3), Mel-frequency cepstral coefficients (MFCC\u2019s), the duration of pauses and sentences (SD), and the duration of silence between words. Combining at least two feature categories achieved high accuracy in the schizophrenia classification. Prosodic and spectral or temporal features achieved the highest accuracy. The work with higher accuracy used the prosodic and spectral features QEVA, SDVV, and SSDL, which were derived from the F0 and spectrogram. The emotional state can be identified with most of the features previously mentioned (F0, I, F1, F2, F3, MFCCs, and SD), linear prediction cepstral coefficients (LPCC), linear spectral features (LSF), and the pause rate. Using the event-related potentials (ERP), the most promissory features found in the literature are mismatch negativity (MMN), P2, P3, P50, N1, and N2. The EEG features with higher accuracy in schizophrenia classification subjects are the nonlinear features, such as Cx, HFD, and Lya.<\/jats:p>","DOI":"10.3390\/bioengineering10040493","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T02:05:31Z","timestamp":1682042731000},"page":"493","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3439-826X","authenticated-orcid":false,"given":"Felipe Lage","family":"Teixeira","sequence":"first","affiliation":[{"name":"Research Centre in Digitalization and Intelligent Robotics (CEDRI), Instituto Polit\u00e9cnico de Bragan\u00e7a, Campus de Santa Apol\u00f3nia, 5300-253 Bragan\u00e7a, Portugal"},{"name":"Engineering Department, School of Sciences and Technology, University of Tr\u00e1s-os-Montes and Alto Douro (UTAD), Quinta de Prados, 5000-801 Vila Real, Portugal"}]},{"given":"Miguel Rocha e","family":"Costa","sequence":"additional","affiliation":[{"name":"Research Centre in Digitalization and Intelligent Robotics (CEDRI), Instituto Polit\u00e9cnico de Bragan\u00e7a, Campus de Santa Apol\u00f3nia, 5300-253 Bragan\u00e7a, Portugal"}]},{"given":"Jos\u00e9 Pio","family":"Abreu","sequence":"additional","affiliation":[{"name":"Faculty of Medicine of the University of Coimbra, 3000-548 Coimbra, Portugal"},{"name":"Hospital da Universidade de Coimbra, 3004-561 Coimbra, Portugal"}]},{"given":"Manuel","family":"Cabral","sequence":"additional","affiliation":[{"name":"Engineering Department, School of Sciences and Technology, University of Tr\u00e1s-os-Montes and Alto Douro (UTAD), Quinta de Prados, 5000-801 Vila Real, Portugal"},{"name":"Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5862-5706","authenticated-orcid":false,"given":"Salviano Pinto","family":"Soares","sequence":"additional","affiliation":[{"name":"Engineering Department, School of Sciences and Technology, University of Tr\u00e1s-os-Montes and Alto Douro (UTAD), Quinta de Prados, 5000-801 Vila Real, Portugal"},{"name":"Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Intelligent Systems Associate Laboratory (LASI), University of Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6679-5702","authenticated-orcid":false,"given":"Jo\u00e3o Paulo","family":"Teixeira","sequence":"additional","affiliation":[{"name":"Research Centre in Digitalization and Intelligent Robotics (CEDRI), Instituto Polit\u00e9cnico de Bragan\u00e7a, Campus de Santa Apol\u00f3nia, 5300-253 Bragan\u00e7a, Portugal"},{"name":"Laborat\u00f3rio para a Sustentabilidade e Tecnologia em Regi\u00f5es de Montanha (SusTEC), Instituto Polit\u00e9cnico de Bragan\u00e7a, Campus de Santa Apol\u00f3nia, 5300-253 Bragan\u00e7a, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"407","DOI":"10.12740\/PP\/74145","article-title":"Propozycja nowej definicji zdrowia psychicznego","volume":"51","author":"Galderisi","year":"2017","journal-title":"Psychiatr. 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