Early identification of mental disorders, based on subjective interviews, is extremely challenging in the clinical setting. There is a growing interest in developing automated screening tools for potential mental health problems based on biological markers. Here, we demonstrate the feasibility of an AI-powered diagnosis of different mental disorders using EEG data. Specifically, this work aims to classify different mental disorders in the following ecological context accurately: (1) using raw EEG data, (2) collected during rest, (3) during both eye open, and eye closed conditions, (4) at short 2-min duration, (5) on participants with different psychiatric conditions, (6) with some overlapping symptoms, and (7) with strongly imbalanced classes. To tackle this challenge, we designed and optimized a transformer-based architecture, where class imbalance is addressed through focal loss and class weight balancing. Using the recently released TDBRAIN dataset (n= 1274 participants), our method classifies each participant as either a neurotypical or suffering from major depressive disorder (MDD), attention deficit hyperactivity disorder (ADHD), subjective memory complaints (SMC), or obsessive–compulsive disorder (OCD). We evaluate the performance of the proposed architecture on both the window-level and the patient-level. The classification of the 2-min raw EEG data into five classes achieved a window-level accuracy of 63.2% and 65.8% for open and closed eye conditions, respectively. When the classification is limited to three main classes (MDD, ADHD, SMC), window level accuracy improved to 75.1% and 69.9% for eye open and eye closed conditions, respectively. Our work paves the way for developing novel AI-based methods for accurately diagnosing mental disorders using raw resting-state EEG data.

中文翻译：

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通过原始和不平衡的脑电图信号自主识别精神功能障碍的变压器

在临床环境中，基于主观访谈的精神障碍的早期识别极具挑战性。人们越来越有兴趣开发基于生物标记的潜在心理健康问题的自动筛查工具。在这里，我们展示了使用脑电图数据进行人工智能诊断不同精神障碍的可行性。具体来说，这项工作的目的是在以下生态背景下准确地对不同的精神障碍进行分类：（1）使用原始脑电图数据，（2）在休息期间收集，（3）在睁眼和闭眼条件下，（4）在短时间内收集持续时间为 2 分钟，(5) 参与者具有不同的精神状况，(6) 具有一些重叠的症状，(7) 类别严重不平衡。为了应对这一挑战，我们设计并优化了基于变压器的架构，其中通过焦点损失和类别权重平衡来解决类别不平衡问题。使用最近发布的 TDBRAIN 数据集（n = 1274 名参与者），我们的方法将每个参与者分类为神经典型或患有重度抑郁症 (MDD)、注意力缺陷多动障碍 (ADHD)、主观记忆抱怨 (SMC) 或强迫症 –强迫症（OCD）。我们评估了所提出的架构在窗口级别和患者级别的性能。将 2 分钟原始 EEG 数据分类为五类，在睁眼和闭眼条件下的窗口级准确度分别为 63.2% 和 65.8%。当分类仅限于三个主要类别（MDD、ADHD、SMC）时，睁眼和闭眼条件下的窗位准确度分别提高到 75.1% 和 69.9%。我们的工作为开发基于人工智能的新型方法铺平了道路，以便使用原始静息态脑电图数据准确诊断精神障碍。