A5066230777- EEG and Brain-Computer Interfaces
- stochastic dynamics and bifurcation
- Neural dynamics and brain function
- Functional Brain Connectivity Studies
- Neural Networks and Applications
- Intravenous Infusion Technology and Safety
- Visual perception and processing mechanisms
- Chaos control and synchronization
- Mental Health via Writing
- Traditional Chinese Medicine Studies
- Internet of Things and AI
- Nonlinear Dynamics and Pattern Formation
- Non-Invasive Vital Sign Monitoring
- Mental Health Research Topics
- Brain Tumor Detection and Classification
- Neural Networks Stability and Synchronization
- Attention Deficit Hyperactivity Disorder
- Machine Learning in Healthcare
- Suicide and Self-Harm Studies
- Face and Expression Recognition
- Human auditory perception and evaluation
- Time Series Analysis and Forecasting
- Technology and Human Factors in Education and Health
- Complex Systems and Time Series Analysis
International Islamic University Malaysia
2019-2023
RIKEN Center for Brain Science
2019-2021
University of Malaya
2019-2021
Toyota Motor Corporation (Switzerland)
2019
The University of Tokyo
2016-2017
We investigate a discrete-time network model composed of excitatory and inhibitory neurons dynamic synapses with the aim at revealing dynamical properties behind oscillatory phenomena possibly related to brain functions. use stochastic neural derive corresponding macroscopic mean field dynamics, subsequently analyze network. In addition slow fast oscillations arising from networks, respectively, we show that interaction between these two networks generates phase-amplitude cross-frequency...
Metastability in the brain is thought to be a mechanism involved dynamic organization of cognitive and behavioral functions across multiple spatiotemporal scales. However, it not clear how such realized underlying neural oscillations high-dimensional state space. It was shown that macroscopic often form phase-phase coupling (PPC) phase-amplitude (PAC), which result synchronization amplitude modulation, respectively, even without external stimuli. These can also make spontaneous transitions...
Abstract It is an open question as to whether macroscopic human brain responses repeatedly presented external inputs show consistent patterns across trials. We here provide experimental evidence that noisy time-varying visual inputs, measured by scalp electroencephalography (EEG), a signature of consistency. The results indicate the EEG-recorded are robust against fluctuating ongoing activity, and they respond stimuli in repeatable manner. This consistency presumably mediates information...
Over the past decade, social media has been attracting a growing number of people to online space. Due increase in internet usage, huge text data produced. Such can reflectusers’ mental healthstatus, but it is still challenging predictsuicide risk from data,due high complexity texts.This research aims predict suicide Reddit posts using artificial intelligence (AI). The were collected Kaggle dataset, which includedpostingsof subreddits.The datawere pre-processed throughnatural language...
Abstract Metastability in the brain is thought to be a mechanism involved dynamic organization of cognitive and behavioral functions across multiple spatiotemporal scales. However, it not clear how such realized underlying neural oscillations high-dimensional state space. It was shown that macroscopic often form phase-phase coupling (PPC) phase-amplitude (PAC) which result synchronization amplitude modulation, respectively, even without external stimuli. These can also make spontaneous...
The world over, mental illness is a serious issue. Many people use the social media that may affect their health positively, but often result in negative sentiments. This research aims to determine an individual's state based on behavior Twitter. We analysed dataset including 170000 real tweets by using natural language processing and machine learning techniques. Decision tree, support vector machine, recurrent neural network (RNN) were used for classifying twitter users, detect if they are...
Recently, the field of brain science often yields 'big' data and utilizes machine learning, which is central for present artificial intelligence (AI) starts usually from extracting hidden features.However, recorded are dynamic where property changes with time, different photos that static over time.Then, following question emerges: Are 's really suitable AI techniques?More specifically can we extract exact features what kind dynamics makes this feature extraction more reliable?To answer...