A5081952940- EEG and Brain-Computer Interfaces
- Blind Source Separation Techniques
- Emotion and Mood Recognition
- Parkinson's Disease Mechanisms and Treatments
- Epilepsy research and treatment
- Functional Brain Connectivity Studies
- Neurological disorders and treatments
- Neuroscience and Neural Engineering
- ECG Monitoring and Analysis
- Neural dynamics and brain function
- Brain Tumor Detection and Classification
- Neural Networks and Applications
- Online Learning and Analytics
- Human-Automation Interaction and Safety
- Gaze Tracking and Assistive Technology
- Currency Recognition and Detection
- Online and Blended Learning
- Stroke Rehabilitation and Recovery
- Machine Learning in Bioinformatics
- Advanced Control Systems Design
- Sleep and Work-Related Fatigue
- Telecommunications and Broadcasting Technologies
- Medicinal Plants and Neuroprotection
- Advanced Memory and Neural Computing
- Fire Detection and Safety Systems
Nanyang Technological University
2018-2025
Kalasalingam Academy of Research and Education
2025
SRM Institute of Science and Technology
2024
Saveetha University
2024
Sree Chitra Thirunal Institute for Medical Sciences and Technology
2023
Social Service Sericulture Project Trust
2022
Anna University, Chennai
2018
Sri Sivasubramaniya Nadar College of Engineering
2015-2018
Universiti Malaysia Perlis
2012-2017
University of Kentucky
2017
A computerized detection system for the diagnosis of Schizophrenia (SZ) using a convolutional neural is described in this study. an anomaly brain characterized by behavioral symptoms such as hallucinations and disorganized speech. Electroencephalograms (EEG) indicate disorders are prominently used to study diseases. We collected EEG signals from 14 healthy subjects SZ patients developed eleven-layered network (CNN) model analyze signals. Conventional machine learning techniques often...
Visual evaluation of electroencephalogram (EEG) for Interictal Epileptiform Discharges (IEDs) as distinctive biomarkers epilepsy has various limitations, including time-consuming reviews, steep learning curves, interobserver variability, and the need specialized experts. The development an automated IED detector is necessary to provide a faster reliable diagnosis epilepsy. In this paper, we propose based on Convolutional Neural Networks (CNNs). We have evaluated proposed sizable database 554...
Advances in signal processing and machine learning have expedited electroencephalogram (EEG)-based emotion recognition research, numerous EEG features been investigated to detect or characterize human emotions. However, most studies this area used relatively small monocentric data focused on a limited range of features, making it difficult compare the utility different sets for recognition. This study addressed that by comparing classification accuracy (performance) comprehensive feature...
While Parkinson's disease (PD) has traditionally been described as a movement disorder, there is growing evidence of disruption in emotion information processing associated with the disease. The aim this study was to investigate whether are specific electroencephalographic (EEG) characteristics that discriminate PD patients and normal controls during processing. EEG recordings from 14 scalp sites were collected 20 30 age-matched controls. Multimodal (audio-visual) stimuli presented evoke...
The diagnosis of epilepsy often relies on a reading routine scalp electroencephalograms (EEGs). Since seizures are highly unlikely to be detected in EEG, the primary depends heavily visual evaluation Interictal Epileptiform Discharges (IEDs). This process is tedious, expert-centered, and delays treatment plan. Consequently, development an automated, fast, reliable epileptic EEG diagnostic system essential. In this study, we propose classify as or normal based multiple modalities extracted...
The recognition of emotions is one the most challenging issues in human–computer interaction (HCI). EEG signals are widely adopted as a method for recognizing because their ease acquisition, mobility, and convenience. Deep neural networks (DNN) have provided excellent results emotion studies. Most studies, however, use other methods to extract handcrafted features, such Pearson correlation coefficient (PCC), Principal Component Analysis, Higuchi Fractal Dimension (HFD), etc., even though DNN...
Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms (EEGs) is laborious and often subjective. Therefore, it necessary to build an effective IED detector automatic method classify IED-free versus EEGs. In this study, we evaluate features that may provide reliable detection EEG classification. Specifically, investigate the convolutional neural network (ConvNet) with different input (temporal, spectral, wavelet features). We explore ConvNet...
Abstract An early stage detection of Parkinson's disease (PD) is crucial for its appropriate treatment. The quality life degrades with the advancement disease. In this paper, we propose a natural (time) domain technique diagnosis PD. proposed eliminates need transformation signal to other domains by extracting feature electroencephalography signals in time domain. We hypothesize that two inter‐channel similarity features, correlation coefficients and linear predictive coefficients, are able...
De¯cits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), there is need for a method quantifying emotion, which currently performed by clinical diagnosis.Electroencephalogram (EEG) signals, being an activity central nervous system (CNS), can re°ect underlying true emotional state person.This study applied machine-learning algorithms categorize EEG states PD patients that would classify six basic (happiness sadness,...
Epilepsy is a chronic brain disorder that expressed by seizures. Monitoring activity via electroencephalogram (EEG) an established method for epilepsy diagnosis and monitoring patients. Yet, it not favorable to visually inspect EEG signals diagnose epilepsy, especially in the case of long-term recordings. This process time consuming tedious error-prone exercise. In recent years, sub-field machine learning called deep has achieved remarkable success various artificial intelligence research...
This study examines the impact of social media marketing on purchase behavior Generation Z (Gen Z) consumers in India. Leveraging platforms like Instagram, Facebook, and WhatsApp, has emerged as a key driver consumer engagement influence. The Study focuses Indian demographics, research underscores effectiveness strategies connecting with Gen consumers. identifies that influence significantly decisions. Findings emphasize importance influencer marketing, brand authenticity, user-generated...