Label Distribu�on Learning for Memory Decline:
A Deep Learning Approach Using EEG Analysis
Wei Chen1, Aldrin Domer1, Kapeleshh KS1, and Hong Ji2

D5P-37-011

1Brain AI Innovation Lab, MACNICA, Inc., Japan

2The Shaanxii Key Laboratory of Clothing Intelligence, Xi’an Polytechnic University, China• Challenge :••••Dynamic Graph Convolutional Neural Network

INTRODUCTION

Increased dementia prevalence due to aging population necessitates
better evaluation methods and interventions for elderly.
Electroencephalogram (EEG) Potential:Oﬀers a promising, portable, and aﬀordable way to assess cognitive
decline compared to traditional psychometric tools.
Novel Approach :This framework proposes Dynamic Graph Convolutional
Neural Network (DGCNN), Label Distribution Learning (LDL)on EEG measurements data to estimate memory decline.
Initial Results :EEG has the potential to complement psychometric tools
for assessing memory loss.
Clinical relevance:This framework could enhance early detection andpotentially aid in dementia diagnosis.

(Target)Early stage

Dynamic Graph Convolution:
Learns how EEG
channels interact during tasks, adapting tochanging patterns.
Convolution & Activation:
Extracts and reﬁnes
meaningful features from EEG signals,
introducing non - linearity for complex patternrecognition.
Combines features andDense & Softmax Layers:
predicts the probability of cognitive decline.
5- Fold Cross - Validation: Rigorous testing
ensures model accuracy and reliability on
unseen data.

Dementia

Key Advantages

In Japan, about one in ﬁve elderly
people aged 65 and over is
expected to develop dementia.

Captures Spatial Relationships:
The graph structure eﬀectively represents the spatial connections between
diﬀerent brain regions, allowing the model to learn how these regions interact.
Dynamic Learning:
The ability to adapt the graph connections during training enables the model to capture
the dynamic nature of brain activity.
Feature Extraction:
The combination of graph convolution and traditional convolution layers allows foreﬀective feature extraction from EEG data.

OBJECTIVES

Loss function

The output layer is also treated as a label distribution,with the input distribution using Kullback - Leibler (KL)divergence as loss to optimize the model.

This study aims to develop an AI driven EEG framework using DGCNN and LDL to predict memory decline
and potentially complement dementia diagnostics in elderly population.

=Cogni�ve

EEG capture

AI analysis

EEG dataset for cognitive
detection collected withnon - invasive devices.

Employ advanced machine learningtechniques to analyze EEG patterns
and predict cognitive decline.

Instant result

Node; Channel
Edge;connection
between eachnode

Illustration of the connections
among the 62 EEG channels

αThe weights of the adjacency
matrix is computed as follows:

βSplit intofrequency bands

Output Distribution

Feature foreach node=exp −

20,,,

[1]

if dist( , ) ≤otherwise• Data selection: Resting state, eyes closed, rest
- state EEG signals from Max Planck Institute Leipzig[2] .
Mind - Brain - Body open dataset (LEMON)• Preprocessing: The data was down sampled to 100 Hz and ﬁltered• Feature extraction: Band diﬀerential entropy was calculated for each channel.
• Adjacency matrix:
A fully connected adjacency matrix was created, indicating that all channels
are considered to be connected.

Classiﬁcation

Expected value==̂̂

Upper range of the credential interval

Cumulative distribution function

Sample of output distribution

Φ ( )= ( ≤ )=When z = 0.95

Health controls (119)Subjects with latentmemory loss (73)Ground truth

Predict

PCA ( Principal Component Analysis)

PCA scores were derived from multiple cognitive battery test measures to create acomposite label reﬂecting general cognitive function.

 CVLT̲4= ProactiveInterference(Interference frompreviously learnedinformation) - 4～4
 CVLT̲ 9= number ofcorrect recalls (shortdelay) 0 ～16
 CVLT̲10 = number ofcorrect recalls whencategory cues arepresented (shortdelay) 0 ～16

CVLT biplot of each component
and subject scatter plots

Bar graph of PC1 score distribution
by subject age and CVLT score line

• Horizontal axis : number of words
recalled in CVLT short time delay, longtime delay, and overall word count• Subjects' overall CVLT scores show aneﬀect trend.

• Vertical axis : susceptibility of subjects
to interference• Forced Choice Recognition (FCR) cutoﬀ
demonstrated high speciﬁcity
in theperformance validity testing (PVT) CVLT̲11 = number ofcorrect recalls after 20
minutes (long delayrecall) 0 ～16
 CVLT̲12 = number ofcorrect recalls whencategory cues arepresented (long delayrecall) 0 ～16

LDL (Label Distribution Learning)

LDL assigns to an instance a distribution over a set of labels
rather than a single label or multiple labels, allowing us toobtain useful information to improve model performance
( − )
(−)
2

The probability density function of the normal distribution isused to generate the grand- truth distribution p.

Severity Indicator:[3] to categorize
Utilized the Force Choice Recognition (FCR) cutoﬀ
individuals as controls or potential memory decline cases.

The model demonstrated promising performance in classifying
dementia severity, achieving an F1- score of 0.624 and an AUC of 0.71,
suggesting potential for further development and reﬁnement for clinicalapplication.

CVLT cognitive battery test

2

Maximum value

Performance Metrics:
Sensitivity, Speciﬁcity, F1 - Score, Area Under the Curve (AUC)- word list
• The task tests memory over time. Subjects learn a 16
repeated ﬁve times, then encounter another list potentiallyinterfering with their memory.
• After immediate recall and categorization of the ﬁrst list,subjects perform various tasks (unspeciﬁed).
• A delayed free recall test occurs after 20 minutes, where theyagain recall and categorize words from the ﬁrst list.
• Finally, recognition memory is assessed by presenting a newlist and asking subjects to identify words from the original 16.

=RESULT

Model comparison:• LightGBM :A gradient boosting framework that uses
tree - based learning algorithms.
(SVR):• Support Vector Regression
A regression algorithm that uses ahyperplane to predict continuous values.
• LDL- DGCNN (Our models):
This comparison aimed to assess thestrengths and weaknesses of each modelin predicting memory decline.

CVLT (California Verbal Learning Task),Label Distribution

Model evaluation on expectedvalue of distribution achieving
the best CC of 0.443 and
RMSE of 9.844.

LABEL DISTRIBUTION LEARNING

1αEvaluation metrics:• Correlation Coeﬃcient(CC)• Root Mean Square Error(RMSE)
between the ground - truth andexpected memory scores.

γδ̂RegressionTransferθ

KL lossαRegression Analysis:
Predicted the severity of memorydecline using continuous values.
Classiﬁcation Analysis:
Classiﬁed individuals into severity
indicator groups for memory decline.
Distribution Conversion Metrics:
Assessed model performance bycomparing predicted and actual label
distributions using three diﬀerent
metrics.

Generate real - time feedback onmemory function, potentially aiding
early detection for dementia.

FEATURE AND GRAPH REPRESENTATION

EEG signallnPositive

Negative

Positive

Negative

2995

4424

Classification model result• Novel Framework

CONCLUSION:
This paper introduced a new framework for predicting memory decline using DGCNN with LDL,
demonstrating its potential for dementia detection.

• Moderate Accuracy:
The model achieved moderate accuracy on the LEMON dataset, indicating the potential of EEG
approaches for early detection.

- based• Limitations :Sample heterogeneity and lack of standardized clinical data may have impacted the model'sperformance.
• Future Directions:
Further research will focus on evaluating the framework with simpler EEG devices and exploring
additional dementia indicators. This could lead to more accessible and comprehensive tools for early
dementia detection and intervention.

REFERENCE[1] T. Song et al., “EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks.” IEEE Transactions on
Affective Computing, vol. 11, no. 3, Jul. 2020, pp. 532–541, doi: https://doi.org/10.1109/taffc.2018.2817622[2] A. Babayan et al., "A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and oldadults." Scientific data 6.1, 2019, pp. 1-21, doi: https://doi.org/10.1038/sdata.2018.308[3] E. S. Schwartz et al., “CVLT-II forced choice recognition trial as an embedded validity indicator: A systematic review of theevidence.” Journal of the International Neuropsychological Society, 2016, 22(8), pp. 851-858, doi:
https://doi.org/10.1017/S1355617716000746
ACKNOWLEDGMENTααThis research is made possible by Macnica andneurotechnology R&D initiatives. Acknowledgment also goes toDr. Toshihisa Tanaka of Tokyo University of Agriculture andTechnology for the rendered technical advice.

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