Label Distribution Learning for Memory Decline:
A Deep Learning Approach Using EEG Analysis
Wei Chen1, Aldrin Domer1, Kapeleshh KS1, and Hong Ji2
1
Brain AI Innovation Lab, MACNICA, Inc., JapanThe Shaanxii Key Laboratory of Clothing Intelligence, Xi’an Polytechnic University, China

2

Abstract— Electroencephalogram (EEG) is a potent means
for assessing cognitive capabilities, thus suitable for monitoring
cognitive interventions, and potentially complementing
diagnostic approaches in dementia among the elderly. In thispaper, we introduce a novel framework using severity indicatorand expectation regression approach to predict memory decline,employing a dynamic graph convolutional neural network(DGCNN) and label distribution learning (LDL). Theframework is applied to the open dataset Mind-Brain-Body(LEMON) from the Max Planck Institute Leipzig. Our initial
findings suggest that EEG measurements can effectively
supplement traditional psychometric tools in assessing memory
loss and cognitive decline.
Clinical Relevance— This framework positions EEG
measurements as an adjunct to bolster existing psychometricassessments for memory loss and cognitive decline.

I. INTRODUCTION
Ageing population in society poses risk of high dementia
prevalence, prompting the need for self-management
approaches for evaluation methods, and personalizedinterventions among the elderly. The increasing portability andaffordability of off-the-shelf EEG devices show great promise
as a modality for measuring cognitive impairments associatedwith early stages of dementia, which are otherwise executedwith psychometric tools in clinical settings. One such tool is
the California Verbal Learning Task (CVLT) is a widely usedpsychometric tool and is a sensitive measure of episodic
memory, available in the LEMON [1] dataset for 228
participants.
II. METHODS
Fig. 1 shows the proposed model workflow. EEG data used are
resting state, eyes closed EEG signals, which were then down
sampled and filtered to all 62 EEG channels. The adjacency
matrix for graph representation consists of band differential
entropy for features and spatial relationships between EEG
channel for nodes. DGCNN model [2] was used to
dynamically learn the intrinsic relationship. Memory generalscale was reduced into principal component based onstandardized CVLT measurement criteria, then a gaussianfunction was applied to generate LDL input label distribution.
The output layer is also treated as a label distribution, with theinput distribution using Kullback-Leibler (KL) divergence asloss to optimize the model.

III. RESULTS

Comparing regression for model performance andassessing the classification accuracy for diagnosing memory
decline are used to evaluate three label distribution conversion
metrics in Table I. Regression employed correlation
coefficient (CC) and Root Mean Square Error (RMSE) areused to evaluate the model performance. Classification
performance of the severity indicator uses a Force Choice
Recognition (FCR) cutoff [3] to categorize controls, evaluatedwith sensitivity, specificity, F1-score, and Area Under theCurve (AUC).
TABLE I.

Regression

Severity Classification

Conversion
metrics

CCRMSE

SENS

SPEC

F1AUC

Expected value

.499

.094

.603

.798

.624

.71

Maximum value

.430

.156

.616

.739

.604

.71

Upper range

.399

.094

.712

.513

.568

.67

IV. DISCUSSION & CONCLUSION

This paper proposes a novel framework for predicting
memory decline in dementia detection using a DGCNN with
LDL for a severity indicator and expectation regression.
Machine learning experiments on the LEMON dataset
achieved moderate accuracy: 0.499 CC and 0.094 RMSE forlabel distribution expectation regression, and 0.624 F1-score
and 0.71 AUC for severity classification. Sample
heterogeneity and lack of standardized clinical examinations
might be contributing factors to the model's shortcomings.
Future work will evaluate the model with a simpler EEG
device and explore broader dementia indicators beyondmemory.
ACKNOWLEDGMENT
This research is made possible by Macnica andneurotechnology R&D initiatives. Acknowledgment also
goes to Dr. Toshihisa Tanaka of Tokyo University ofAgriculture and Technology for the rendered technical advice.
REFERENCES
[1]

[2]

[3]

Figure 1. The instruction pipeline of the proposed model.

COMPARISON OF REGRESSION AND SEVERITY INDICATOR

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