Passive BCI for Dementia Onset Detection
and Cognitive Intervention Monitoring
Tomasz M Rutkowski ● Masato S Abe ● Mihoko Otake-Matsuura
Cognitive Behavioral Assistive Technology Team, RIKEN AIP, Tokyo, Japan

tomasz.rutkowski@riken.jp ● http://aip.riken.jp/Abstract
The poster presents a practical application of machine learning (ML) in the so-called `AI forsocial good' domain and in particular concerning the problem of a dementia onset prediction.
An increase in dementia cases is producing a significant medical and economic weight inmany countries. Approximately 47 million older adults live with a dementia spectrum ofneurocognitive disorders, according to a recent report of the World Health Organization(WHO), and this number will triple within the thirty years. This growing problem calls forpossible application of AI-based technologies to support early diagnostics for cognitive
interventions and a subsequent mental wellbeing monitoring as well as maintenance with theso-called 'digital-pharma' or 'beyond a pill' therapeutical strategies. The poster presents ourstudy results of EEG brainwave responses analysis in a BCI-based emotional stimulus andworking implicit memory learning task. We focus on the advancement of digital biomarkers fordementia progress detection and monitoring. We discuss a range of machine-learningresulting accuracies with encouraging results using classical shallow and deep learning
approaches for automatic discrimination of normal cognition versus a mild cognitive
impairment (MCI). The classifier input features consist of an older adult emotional valence andarousal EEG responses, as obtained from a group of 35 older adults participating voluntarily
in the reported dementia biomarker development project. The presented results showcase
the inherent social benefits of artificial intelligence (AI) utilization for the elderly and establish astep forward to advance machine learning (ML) approaches for the subsequent employmentof simple BCI-based EEG examination for MCI and dementia onset diagnostics.

Results: Median errors of the tested regression
methods for MoCA score predictions using variousmethods.

Results: Pairwise scatter plots with features used for MoCA score regression or
MCI classification with clear linear dependencies.

Results: Median classification results ofvarious classifiers tested in our project. Fully
connected deep neural network (FNN) andlinear regression (LR) resulted withaccuracies round 90%.

EEG Results

Results: Auditory (amplitude modulated) P300 Results: Median classification accuracy results
latency responses to easy (normal-P300; real (white dots within percentile ranges) depictedsound; blue), difficult (anomalous-P300; virtual; together with result distributions (a chance levelof 0.5 representing 50% of binary classificationgreen) and ignored (non-target; red) spatial
cases depicted with bold gray line).
auditory stimuli.

Results: Multi-fractal DFA analysis results forResults: Spectral 1/f slope exponent analysis
MCI and normal elderly with significant statistical results for MCI and normal elderly with significandifferences of the distributions as evaluated with statistical å of the distributions as evaluated withranksums (pr) and Kruskal-Wallis (pk) tests.
ranksums (pr) and Kruskal-Wallis (pk) tests.

Results: Median classification results of various classifiers tested in our project in he workingimplicit-visuospatial-memory learning task and using EEG complexity (MFDFA and 1/fexponent) features. Random forest and FNN classifiers resulted with accuracies round 95%.

Results: Median classification results of various classifiers tested in our project in he workingimplicit-visuospatial-memory testing task and using EEG complexity (MFDFA and 1/fexponent) features. Random forest and FNN classifiers resulted with accuracies round 95%.

Conclusions
Methods
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We conducted experiments with human subjects with guidelines
and approval of the RIKEN Ethical Committee for Experimentswith Human Subjects in the Center for Advanced Intelligence
Project (AIP). In the experimental session, 35 elder participants(number of females = 22; mean age = 73.5 years old; age
standard deviation of ±4.85 years; recruited from Silver Human
Resources Center and Honobono Laboratory). Each subject
experiment consisted of 72 video presentation trials (5∼7 secondseach) with 24 different emotion categories. Behavioral response ofvalence and arousal working implicit memory-based responses
together with reaction times and EEG were recorded.

Time [s]0sTesting session - results used for MCI/dementia prediction

Methods: Working-implicit-visuospatial-memory
learning (top) and testing (bottom) paradigms.

Behavioral Results• The study resulted in MCI pathology prediction from behavioral and EEG responses in the spatial- and implicitworking-memory task of emotional valence and arousal levels estimation together with reaction times in simple
video clips watching task.
• In the study involving older adults with known MoCA scores, we were able to evaluate several shallow learningclassifiers.
• The prediction of MCI (usually MoCA ︎of 25 has been considered as mild cognitive impairment (MCI) stage
already, while above this threshold an elderly adult cognition has been evaluated as standard) in the simple
emotional faces evaluation task resulted in robust and small errors.
• The successful application of such AI/ML-based dementia onset prediction shall lead to a healthcare costlowering benefiting the aging societies.
• We also acknowledge the potential limitations of the current approach as we only infer human-error-prone
MoCA scores-based MCI stages, which are only proxy estimators of dementia.
• AI-based dementia estimators, if used without proper evaluation, might also pose a danger of misuse or abuse;
thus, proper ethical standards will need to be in place too.
• In the next step of our research project, we plan to evaluate the developed methods with a larger sample ofordinary versus SCI/MCI, or even dementia diagnosed members of the society.

References
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Results: Linear regression of the working-implicitvisuospatial-memory learning-based arousal judgementerrors versus MoCA scores with a significant increase forlower cognitive scores.

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Results: Reaction times (top), valence (middle) andarousal (bottom) errors with statistically significant
median differences of low-MoCA (MCI) group.

Results: Linear regression of reaction times versus MoCA scoreswith a significant slowing for lower cognitive scores.

Results: Linear regression of the working-implicitvisuospatial-memory learning-based valence judgement
errors versus MoCA scores with a significant increase forlower cognitive scores.

Results: Linear regression of the working-implicit-visuospatial-memory
learning-based median for each subject arousal and valence judgement
errors versus MoCA scores further confirming the error increases forlower cognitive scores.

Rutkowski TM, Abe MS, Koculak M, Otake-Matsuura M. Classifying Mild Cognitive Impairment from Behavioral Responses in Emotional Arousal andValence Evaluation Task - AI Approach for Early Dementia Biomarker in Aging Societies -. In: 42nd Annual International Conference of the IEEE
Engineering in Medicine and Biology Society (EMBC). IEEE Engineering in Medicine and Biology Society. Montreal, Canada: IEEE Press; 2020. p.
5537-5543.
Rutkowski TM, Abe MS, Koculak M, Otake-Matsuura M. Cognitive Assessment Estimation from Behavioral Responses in Emotional Faces Evaluation
Task - AI Regression Approach for Dementia Onset Prediction in Aging Societies. In: NeurIPS Joint Workshop on AI for Social Good - Track 1 Producing Good Outcomes. Vancouver, Canada; 2019. p. 1-4.
Rutkowski TM. Multisensory reactive and passive BCIs - applications for robotics, VR/AR and dementia diagnostics. In: The 5th International
Conference BCI: Science and Practice - Samara 2019. Samara, Russia; 2019. p. 1.
Rutkowski TM, Zhao Q, Abe MS, Otake-Matsuura M. Passive BCI for Task-load and Dementia Biomarker Elucidation. In: 41st Annual International
Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Engineering in Medicine andBiology Society. Berlin, Germany: IEEE Press; 2019. p. ThC01.1.
Rutkowski TM, Koculak M, Abe MS, Otake-Matsuura M. Brain Correlates of Task–Load and Dementia
Elucidation with Tensor Machine Learning Using Oddball BCI Paradigm. In: ICASSP 2019 - 2019 IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2019. p. 8578–8582.
Rutkowski TM. Multisensory reactive and passive BCIs - applications for robotics, VR/AR and dementia
diagnostics. In: The 5th International Conference BCI: Science and Practive - Samara 2019. Samara, Russia;
2019. p. 1.
Rutkowski TM, Zhao Q, Abe MS, Otake M. AI Neurotechnology for Aging Societies – Task-load and Dementia
EEG Digital Biomarker Development Using Information Geometry Machine Learning Methods. In: AI for Social
Good Workshop at the Neural Information Processing Systems (NeurIPS = formerly NIPS) 2018. Montreal,Canada; 2018. p. 1-4.

AI and Brain Science Symposium 2020, Tokyo, Japan