Dia Mamadou, Khodabandelou Ghazaleh, Anwar Syed Muhammad, Othmani Alice
Laboratoire Image Signaux Systèmes Intelligents, Université Paris-Est Créteil-Val-de-Marne, Vitry-sur-Seine, 94400, France.
Children's National Medical Center, Washington, DC, 20010, USA.
Sci Rep. 2025 May 2;15(1):15387. doi: 10.1038/s41598-025-98264-w.
Mental disorders represent a critical global health challenge that affects millions around the world and significantly disrupts daily life. Early and accurate detection is paramount for timely intervention, which can lead to improved treatment outcomes. Electroencephalography (EEG) provides the non-invasive means for observing brain activity, making it a useful tool for detecting potential mental disorders. Recently, deep learning techniques have gained prominence for their ability to analyze complex datasets, such as electroencephalography recordings. In this study, we introduce a novel deep-learning architecture for the classification of mental disorders such as post-traumatic stress disorder, depression, or anxiety, using electroencephalography data. Our proposed model, the multichannel convolutional transformer, integrates the strengths of both convolutional neural networks and transformers. Before feeding the model as low-level features, the input is pre-processed using a common spatial pattern filter, a signal space projection filter, and a wavelet denoising filter. Then the EEG signals are transformed using continuous wavelet transform to obtain a time-frequency representation. The convolutional layers tokenize the input signals transformed by our pre-processing pipeline, while the Transformer encoder effectively captures long-range temporal dependencies across sequences. This architecture is specifically tailored to process EEG data that has been preprocessed using continuous wavelet transform, a technique that provides a time-frequency representation, thereby enhancing the extraction of relevant features for classification. We evaluated the performance of our proposed model on three datasets: the EEG Psychiatric Dataset, the MODMA dataset, and the EEG and Psychological Assessment dataset. Our model achieved classification accuracies of 87.40% on the EEG and Psychological Assessment dataset, 89.84% on the MODMA dataset, and 92.28% on the EEG Psychiatric dataset. Our approach outperforms every concurrent approaches on the datasets we used, without showing any sign of over-fitting. These results underscore the potential of our proposed architecture in delivering accurate and reliable mental disorder detection through EEG analysis, paving the way for advancements in early diagnosis and treatment strategies.
精神障碍是一项严峻的全球健康挑战，影响着世界各地数百万人，并严重扰乱日常生活。早期准确检测对于及时干预至关重要，而及时干预可改善治疗效果。脑电图（EEG）提供了观察大脑活动的非侵入性手段，使其成为检测潜在精神障碍的有用工具。最近，深度学习技术因其能够分析复杂数据集（如脑电图记录）而备受瞩目。在本研究中，我们引入了一种新颖的深度学习架构，用于使用脑电图数据对创伤后应激障碍、抑郁症或焦虑症等精神障碍进行分类。我们提出的模型——多通道卷积变换器，融合了卷积神经网络和变换器的优势。在将模型作为低级特征输入之前，使用共同空间模式滤波器、信号空间投影滤波器和小波去噪滤波器对输入进行预处理。然后，使用连续小波变换对脑电图信号进行变换，以获得时频表示。卷积层对由我们的预处理管道变换后的输入信号进行标记化，而变换器编码器则有效地捕获序列间的长期时间依赖性。这种架构专门针对处理使用连续小波变换预处理的脑电图数据进行了定制，该技术提供了时频表示，从而增强了用于分类的相关特征的提取。我们在三个数据集上评估了我们提出的模型的性能：脑电图精神疾病数据集、MODMA数据集和脑电图与心理评估数据集。我们的模型在脑电图与心理评估数据集上的分类准确率为87.40%，在MODMA数据集上为89.84%，在脑电图精神疾病数据集上为92.28%。我们的方法在我们使用的数据集上优于所有同期方法，且没有显示出任何过拟合迹象。这些结果强调了我们提出的架构在通过脑电图分析提供准确可靠的精神障碍检测方面的潜力，为早期诊断和治疗策略的进步铺平了道路。
