Alzheimer’s disease classification from EEG using a multiscale temporal deep network

Early and accurate diagnosis of Alzheimer’s Disease (AD) is crucial for patient care and intervention but remains challenging due to the complexity and variability of clinical data. Electroencephalography (EEG) has emerged as a promising, non-invasive, and cost-effective tool to detect brain activity patterns associated with AD. In this work, we introduce a lightweight Multiscale Temporal Deep Network (MTDNet) that integrates multiple temporal convolutions with recurrent modeling to capture both short- and long-term EEG patterns. Two patient-level classification strategies are also proposed that combine segment-level EEG predictions based on consensus and score aggregation to better align with clinical practice and utility. We evaluate our method on four benchmark EEG datasets (ADSZ, APAVA, ADFTD, BrainLat) where it consistently outperforms state-of-the-art solutions by about 2% at the segment level and by about 6% at the patient level on the most challenging datasets. Unlike recent computationally heavy transformer-based solutions, MTDNet achieves superior accuracy with only 20.5K parameters and 1.8M FLOPs, enabling its deployment in resource-constrained environments. Ablation studies confirm the critical contribution of the multiscale design and show that simple augmentation techniques increase generalization and robustness. Code is available at https://github.com/unimib-islab/MTDNet . • Lightweight deep learning model for EEG-based Alzheimer’s classification. • Multiscale temporal features extraction and sequential processing for EEG pattern analysis. • Multiple strategies for patient-level Alzheimer’s disease state assessment. • Outperform the state of the art on widely adopted datasets. • Ablation studies show the benefits of adopting multiscale feature extraction and data augmentation procedures.