Our research at a glance

Open Brain AI

Open Brain AI's research platform uses computational analysis of spoken and written language to aid teachers, clinicians, and researchers. Our solutions support teaching, diagnosis, treatment monitoring, and greater understanding of aphasia, dementia, and developmental language conditions.

Clinical Neurolinguistic Assessment

Open Brain AI models are meticulously designed to aid in the diagnosis, prognosis, and assessment of therapy efficacy, drawing insights from both spoken and written speech productions. By encompassing both spoken and written modalities, our models provide a comprehensive overview of a patient's linguistic abilities, thereby contributing significantly to personalized treatment strategies.

AI Models in Education

Open Brain AI offers educators a powerful tool to gain deeper insights into students' classroom performance. It meticulously assesses students' learning progress and evaluates the effectiveness of teaching methods. By leveraging advanced machine learning models, it enables educators to identify individual learning patterns, strengths, and areas needing improvement. This not only helps in tailoring instruction to meet diverse learning needs but also in refining teaching strategies for optimal educational outcomes. The system's comprehensive analysis supports a more informed and effective educational approach, ensuring that both teaching methods and student learning are continuously evolving and improving.

Research Tools

Open Brain AI offers cutting-edge tools designed for comprehensive linguistic analysis. These tools include automatic transcription of speech, which not only saves time but also increases the accuracy and efficiency of data collection. Beyond transcription, our tools offer detailed scoring across multiple linguistic domains. By providing these resources, we aim to facilitate deeper insights into language processing and disorders, ultimately contributing to advancements in diagnostic models and therapeutic strategies.

Computational Language Assessment

Open Brain AI provides online tools to enable clinicians and educators analyze written, and spoken text productions from their patients or students. The following domains are currently covered by the platform.

01Large Language Models

Large Language Models enable clinicians and teachhers by automatically analyzing spoken and written language.

02Computational Discourse Analysis

We are developing automated ways to analyze spoken and written discourse productions, identify pathological characteristics, and provide scores of language and cognition.

03Clinical and Educational Toolkit

Our research focuses on all domains of speech language and communication translatable to applications for clinicians and teachers.

AI Analysis of Discourse

We provide discourse analysis using AI. We offer recommendations on the Cohesion and Coherence, checks the errors, and suggests whether there are pathological impairments.

Written Speech

We analyze written speech and provide measures and scores about all linguistic domains, including phonology, morphology, syntax, semantics, lexicon, and readability measures.

Spoken Speech

We provide automatic transcription of spoken speech and provide measures and scores about all linguistic domains, including phonology, morphology, syntax, semantics, lexicon, and readability measures..

Semantic Analysis

Score the semantics of speech productions in patients, e.g., in confrontational naming tasks.

Phonological Analysis

Score the phonological productions of patients.

Transcribe Sounds into the International Phonetic Alphabet

Multilingual IPA transcription and get measures from the IPA transcription.

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Computational language assessment is a fast and objective way to identify speech, language, and communication impairments as symptoms of other conditions (e.g., dementia or developmental language impairments) (Themistocleous et al. 2023).

Therapy and Teaching Efficacy

We provide automatic online scores that can help assess the efficacy of clinical therapy or teaching (Themistocleous et al. 2021).

Dementia Diagnosis and Prognosis

Language can be employed for early dementia diagnosis and deferential diagnosis into variants and prognosis (Themistocleous et al. 2022).

Disease Monitoring

Providing telemonitory and telehealth with accessible tools (Themistocleous 2023).


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Natural Language Processing

Natural Language Processing allows the analysis of discourse automatically. It provides measures, including counts from phonology (e.g., number of syllables), morphology (e.g., number of parts of speech in text), syntax (e.g., syntactic constituents), semantics (e.g., number of entities), readability measures, and lexical measures.

Acoustic Analysis

It enable the transcription of texts, the automatic segmentation of speech into words and speakers, and the elicitation of acoustic measures, such as information about prosody and voice quality.

Machine Learning

State-of-art Machine Learning and statistical models, especially Deep Neural Network architectures enable us to find patterns from language that characterize language impairments in adults and children.

Latest Papers

Read more about our research at Charalambos Themistocleous's Personal Website

Automatic Subtyping of individuals with Primary Progressive Aphasia

We developed a Machine Learning model to support differential diagnosis of patients with Primary Progressive Aphasia, using combined acoustic and linguistic information elicited automatically. The end-to-end automated machine learning approach enables clinicians and researchers to provide an easy, quick, and inexpensive classification of patients with PPA.

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Volce quality and speech fluency distinguish individuals with Mild Cognitive Impairment from Healthy Controls

We analyzed voice quality and speech fluency in 26 patients with Mild Cognitive Impairment (MCI) and 29 healthy controls using a speech task. We found significant differences in speech acoustic features between the two groups, suggesting voice quality, prosody, and speech analysis are an objective tool for MCI diagnosis.

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Part of Speech Production in Patients With Primary Progressive Aphasia: An Analysis Based on Natural Language Processing

Using an automated analysis of a short picture description task, this study shows that content versus function words can distinguish patients with nonfluent PPA, semantic PPA, and logopenic PPA variants. Verbs are less important as distinguishing features of patients with different PPA variants than earlier thought. Finally, the study showed that among the most important distinguishing features of PPA variants were elaborative speech elements, such as adjectives and adverbs.

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