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dc.contributor.advisor Perera, I
dc.contributor.author Chandreswaran, Y
dc.date.accessioned 2024-08-13T03:05:54Z
dc.date.available 2024-08-13T03:05:54Z
dc.date.issued 2023
dc.identifier.citation Chandreswaran, Y. (2023). An Architecture for EEG based mental state recognition and monitoring [Master’s theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/22655
dc.identifier.uri http://dl.lib.uom.lk/handle/123/22655
dc.description.abstract Humans invent technologies to make today's life easy. Every human expects a healthy long life. A healthy life includes good physical health and stable mental health. There are multiple causes such as busy lifestyle, stress, sadness, anger, fear, etc. can affect the mental health of Human life. There are several approaches to overcoming this mental illness but the challenge is to monitor and measure the efficiency of treatments followed by humans. Therefore, a solution is proposed as a real-time non-invasive BCI system, which helps to predict the mental state and provides progress of improvement. This research work aims to predict human brain states using EEG-based signals and classify the human brain states in real time. The features and classification methods help to categorize the patterns of the brainwave. EEG signals communicate with BCI through the NeuroSky Headset with four sensors inbuilt. We have generated sample data sets for training and testing using the NeuroSky Headset. Systems have been tested with multiple feature extraction methods and feature pattern classification modes to build the prediction solution. The final solution contains a human-facing mobile web app, which reads the EEG signals from the NeuroSky Headset. In addition, the system contains a prediction component, a backend API component, and system managing dashboard components. en_US
dc.language.iso en en_US
dc.subject CLASSIFICATION, BRAIN WAVES en_US
dc.subject MENTAL STATE en_US
dc.subject MACHINE LEARNING en_US
dc.subject ELECTROENCEPHALOGRAM en_US
dc.subject API en_US
dc.subject BRAIN COMPUTER INTERFACE en_US
dc.subject COMPUTER SCIENCE- Dissertation en_US
dc.subject EMOTIONS en_US
dc.subject COMPUTER SCIENCE & ENGINEERING – Dissertation en_US
dc.title An Architecture for EEG based mental state recognition and monitoring en_US
dc.type Thesis-Abstract en_US
dc.identifier.faculty Engineering en_US
dc.identifier.degree MSc in Computer Science specializing in Software en_US
dc.identifier.department Department of Computer Science and Engineering en_US
dc.date.accept 2023
dc.identifier.accno TH5307 en_US


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