Delta2Gamma: Band-Wise Adaptive Contrastive Learning of EEG for Alzheimer's Disease Detection

Chanwoo Park; Chanwoo Kim

cs.LGAug 18, 2026

Delta2Gamma: Band-Wise Adaptive Contrastive Learning of EEG for Alzheimer's Disease Detection

Authors: Chanwoo Park, Chanwoo Kim

Organizations: Department of Artificial Intelligence Korea University Seoul, Republic of Korea

Abstract

Low-cost, scalable screening for dementia remains an open problem. Imaging-based diagnosis is costly and hard to deploy widely. Electroencephalography (EEG) is portable and inexpensive, but its recordings are noisy, vary widely across subjects, and carry few clinical labels. We tackle this with Delta2Gamma, a self-supervised framework that learns EEG representations from unlabeled data by contrasting augmented views of each signal. Rather than treat EEG as a single stream, Delta2Gamma decomposes every recording into the five canonical neural rhythms (delta, theta, alpha, beta, gamma). Each band gets its own encoder and projection head. Each also gets a temperature that is predicted adaptively during contrastive training, so bands with different signal statistics are balanced automatically. On the ADFTD cohort under a strict leave-one-subject-out protocol, Delta2Gamma separates Alzheimer's disease from cognitively normal controls with 92.4% accuracy. This exceeds both supervised backbones and recent dedicated EEG methods.

Figures & tables

Fig. 1: Overview of the Delta2Gamma model. (a) Self-supervised pre-training: augmentation is applied to unlabeled EEG, then an adaptive NT-Xent contrastive loss with regularization trains the encoder to learn representations across the five bands ( $\delta,\theta,\alpha,\beta,\gamma$ ). (b) Linear evaluation: a classifier is added on top of the frozen pre-trained encoder for AD vs. cognitively normal (CN) classification. Numbers on linear layers denote layer dimensionality.

Model	Application	Train	Backbone	#Param	Accuracy (%)	F1-score (%)
ATCNet [ 27 ]	General	Supervised	CNN & RNN & Att	113,732	74	74
BIOT [ 29 ]	Sleep Staging Epilepsy	SSL	Att	3,183,879	53	40
CTNet [ 32 ]	Motor Imagery	Supervised	CNN & Att	26,900	74	73
Deep4Net [ 26 ]	General	Supervised	CNN	282,879	49	49
EEGConformer [ 28 ]	General	Supervised	CNN & Att	789,572	57	54
EEGInception [ 33 ]	Motor Imagery ERP & SSVEP	Supervised	CNN	558,028	39	37

TABLE I: AD vs. CN classification performance of the proposed model against leading supervised and self-supervised EEG benchmarks. “Application” denotes the model’s typical target domain, with “General” indicating no specific one; “#Param” is the number of parameters required to instantiate the model. Att denotes attention mechanisms.

Model	Acc (%)	F1 (%)	Prec. (%)	Rec. (%)
kNN [ 38 ]	60.30	58.90	57.90	59.90
CNN [ 39 ]	79.45	77.60	76.32	76.06
Random Forest [ 40 ]	80.00	81.69	–	80.55
DICE-Net [ 41 ]	83.28	84.12	88.94	79.81
CNN [ 42 ]	84.62	86.11	–	86.11
MJANet [ 43 ]	85.23	86.37	88.12	84.69

TABLE II: LOSO comparison for AD vs. CN classification on the ADFTD dataset [ 19 ] .

Model	Acc	F1	Prec.	Rec.	AUC
Adaptive 5 band heads	92.37	92.33	92.61	92.37	93.18
w/o self-supervised learning	62.54	61.02	62.79	62.54	65.61
constant temp. ( $\tau{=}0.1$ )	87.37	87.40	87.99	87.37	88.38
Single-head	89.65	89.62	90.04	89.65	91.88
w/o regularization	90.50	90.47	91.22	89.89	91.13
Multi-head (5 heads)	91.14	91.11	91.49	91.14	93.21

TABLE III: Ablation study of the proposed model on the ADFTD dataset [ 19 ] . Multi-head (5 heads) denotes multiple independently learned MLP projection heads, not frequency bands.

Explore similar work

Leveraging a Foundation Model for the EEG-Based Diagnosis of Alzheimer's Disease
Aug 27, 2026Maggie Lin, Chung-Lin Hou, Tzyy-Ping JungAlzheimer's Disease Classification Electroencephalography
Department of Bioengineering University of California, San Diego La Jolla, CA 92037, USA · Research and Development HippoScreen Neurotech Corp. Taipei, Taiwan · Swartz Center for Computational Neuroscience University of California, San Diego La Jolla, CA 92037, USA
GraM-Diff: A Unified Graph-Mamba Diffusion Framework for EEG-Based Alzheimer's Disease Data Generation and Diagnosis
Aug 30, 2026M. Tanveer, Ayush Singh Rana, Sanskriti Jain +5Synthetic Data Augmentation Electroencephalography
Indian Institute of Technology Indore, Indore 453552, Madhya Pradesh, India
Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networks
Oct 13, 2025Jun-En Ding, Anna Zilverstand, Shihao Yang +2Alzheimer's Disease Electroencephalography
Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ, USA · Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA · Institute of Brain Science, College of Medicine, National Yang-Ming Chiao Tung University, Taipei City, Taiwan

Aug 27, 2026J/K move · Enter open · S save

Leveraging a Foundation Model for the EEG-Based Diagnosis of Alzheimer's Disease

Maggie Lin, Chung-Lin Hou, Tzyy-Ping Jung

Department of Bioengineering University of California, San Diego La Jolla, CA 92037, USA · Research and Development HippoScreen Neurotech Corp. Taipei, Taiwan · Swartz Center for Computational Neuroscience University of California, San Diego La Jolla, CA 92037, USA

Alzheimer's Disease Classification Electroencephalography EEG Foundation Models

Open paper AskPDF arXiv

Biological heterogeneity in Alzheimer's Disease (AD) poses a critical diagnostic challenge, particularly for traditional linear methods that fail to capture non-linear neural dynamics. To address this, we propose a diagnostic framework utilizing the Large Brain Model (LaBraM), pretrained on over 2,500 hours of EEG data. By integrating these high-dimensional latent embeddings with a non-linear Random Forest classifier, our approach effectively isolates robust disease markers. Under a rigorous subject-independent 5-fold cross-validation protocol, the method achieves an ROC-AUC of 89.36% +/- 3.49%, PR AUC of 81.45% +/- 4.43%, and Balanced Accuracy of 82.44% +/- 4.34% in distinguishing dementia patients from healthy controls. Notably, this performance uses only 8-second EEG segments, surpassing traditional spectral baselines, including band-power and parameterized oscillatory features (FOOOF). Post-hoc occlusion analysis confirms the model captures clinically validated biomarkers, specifically occipital-frontal Alpha and Theta rhythm degradation. Additional neurophysiological alignment analysis demonstrated that higher LaBraM-predicted dementia probability significantly correlated with worse cognitive performance, greater clinical severity, increased theta and alpha relative power, and higher aperiodic exponent. These findings demonstrate that deep latent representations extract clinically relevant signatures from noisy signals, enabling precise, rapid, and data-efficient diagnosis.

Abstract

Explore similar work

Leveraging a Foundation Model for the EEG-Based Diagnosis of Alzheimer's Disease

GraM-Diff: A Unified Graph-Mamba Diffusion Framework for EEG-Based Alzheimer's Disease Data Generation and Diagnosis

Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networks