A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges

Felipe Teixeira · Miguel Rocha e Costa · José Luís Pio-Abreu · Manuel Cabral · Salviano Soares · João Paulo Teixeira

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Emotion and Mood Recognition Schizophrenia research and treatment EEG and Brain-Computer Interfaces

Abstract

Schizophrenia is a mental illness that affects an estimated 21 million people worldwide. 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's), 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.

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References

2010Survey on speech emotion recognition: Features, classification schemes, and databases 2006Emotional speech recognition: Resources, features, and methods 2019Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers 2020Automated assessment of psychiatric disorders using speech: A systematic review 2010Are the available apathy measures reliable and valid? A review of the psychometric evidence 2020A survey of speech emotion recognition in natural environment 2020Using Language Processing and Speech Analysis for the Identification of Psychosis and Other Disorders 2021Predicting Exact Valence and Arousal Values from EEG

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