Schizophrenia is a debilitating neuropsychiatric disorder characterized by profound cortical network dysregulation, for which objective, clinically translatable EEG based biomarkers remain underdeveloped. Existing automated classification pipelines rely predominantly on static power spectral density features inherently blind to amplitude modulation dynamics and cross-frequency coupling, phenomena central to schizophrenia pathophysiology, while adopting epoch level cross validation strategies that introduce temporal data leakage, artificially inflate reported performance.
The paper presents a transformer-based framework for detecting schizophrenia from EEG signals. It converts preprocessed EEG recordings into spectrograms via Short-Time Fourier Transform and classifies them using both traditional machine learning algorithms and deep learning models, including CNN-Transformer hybrids. Subject-level data partitioning ensures reliable evaluation, yielding an AUC-ROC of 88.41% for the CNN-Transformer model and 92.88% for the CNN + Squeeze and Excitation + Transformer model on an independent test set.
By Abtin Shafiei, Mohsen Hooshmand, Majid Ramezani
arXiv:2606. 00884v1 Announce Type: cross Abstract: We study cross-subject emotion recognition from EEG, a practically important yet challenging problem in brain-computer interfaces.
By Jiaxin Qing, Lexin Li
The paper presents a diagnostic framework for Alzheimer’s disease that uses the Large Brain Model (LaBraM), a foundation model pretrained on over 2,500 hours of EEG data, to generate high‑dimensional latent embeddings. These embeddings are fed into a non‑linear Random Forest classifier, achieving an ROC‑AUC of 89.36% ± 3.49%, PR AUC of 81.45% ± 4.43%, and Balanced Accuracy of 82.44% ± 4.34% in a subject‑independent 5‑fold cross‑validation setting, using only 8‑second EEG segments. Post‑hoc occlusion and neurophysiological alignment analyses confirm that the model captures clinically validated biomarkers such as occipital‑frontal Alpha and Theta rhythm degradation and correlates with cognitive performance and clinical severity.
By Maggie Lin, Chung-Lin Hou, Tzyy-Ping Jung
arXiv:2608. 13676v1 Announce Type: new Abstract: Objective: Foundation models represent the next advancement in AI for EEG analysis; however current explainable AI techniques provide attribution scores in the time-channel input space, which is mismatched to clinical intuition about EEG.
By Deeksha M Shama, Punnisa Amornsirikul, Archana Venkataraman
arXiv:2607. 02670v1 Announce Type: new Abstract: Electroencephalography (EEG) offers a noninvasive approach for examining neurophysiological correlates of dimensional psychopathology, yet systematic evidence across EEG paradigms and feature granularities remains limited.
By Haofan Cheng, Jingjing Hu, Jingrong Pei, Shuaiqi Fu, Meilun Shen, Shuai Fang, Meng Wang, Dan Guo, Jie Zhang
arXiv:2609.22092v1 Announce Type: cross
Abstract: Electroencephalography (EEG) is a low-cost and non-invasive signal source for dementia screening, yet existing EEG-based studies remain difficult to...
By Haitian Wang, Chamara Madarasingha, Redowan Mahmud, Aneesh Krishna, Ryu Takechi
arXiv:2607. 24519v2 Announce Type: replace Abstract: Pretrained EEG foundation models are proposed for clinical decoding, but whether reported gains transfer across populations or survive negative controls is unclear.
By Marzieh Zare
arXiv:2607. 23554v1 Announce Type: cross Abstract: In this paper, we propose the MAEConformer, a novel self-supervised learning framework that combines the Conformer architecture with the Masked Autoencoder (MAE) paradigm for large-scale representation learning from unlabelled electroencephalography (EEG) and heart rate variability (HRV) signals.
By Shuwen Yu, William P Marnane, Geraldine B. Boylan, Gordon Lightbody
arXiv:2609.36609v1 Announce Type: cross
Abstract: Electroencephalography (EEG) analysis requires careful choices in preprocessing, statistical modeling, and machine learning because EEG signals are h...
By Parsa Razmara, Woojae Jeong, Aditya Kommineni, Raymundo Cassani, Richard Leahy, Takfarinas Medani
arXiv:2609.36087v1 Announce Type: cross
Abstract: Clinicians and neuroscientists have long analyzed intracranial electroencephalography (iEEG) through directly measurable physiological characteristic...
By Yipeng Zhang, Chenda Duan, Yuanyi Ding, Tianyi Wang, Atsuro Daida, Masaki Izumi, Yuta Tanoue, Naoto Kuroda, Shaun A. Hussain, Nishant Sinha, Eishi Asano. Hiroki Nariai, Vwani Roychowdhury
arXiv:2606. 06647v1 Announce Type: new Abstract: Objective.
By Jun-You Lin, Ying Choon Wu, Tzyy-Ping Jung