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Context Independent Expectation Maximization Algorithm for Segmentation of Brain MR Images
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Brain Topographic Mapping of Emotions using Computational Cerebellum
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Proceeding Paper -
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Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool
Published 2018“…The use of sensors known as Brain-Computer Interface (BCI) tool can monitor the physical processes and mental states that occur in the human brain. …”
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Thesis -
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EEG signal classification for real-time brain-computer interface applications: a review
Published 2011“…Brain-computer interface (BCI) is linking the brain activity to computer, which allows a person to control devices directly with his brain waves and without any use of his muscles. …”
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Proceeding Paper -
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Brain computer interface design and applications: challenges and future
Published 2010“…However, it is only in the last ten years that these systems have been shown to be feasible in laboratories. Successful Brain Computer Interface (BCI) systems have many potential applications, especially for patients who are paralyzed. …”
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Investigation Of Edge Detection Techniques Based On Brain Tumor Images
Published 2018“…However, from visual perspective, Sobel operator produced better edge maps of the brain tumor compared to the Modified Canny algorithm.…”
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Monograph -
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Brain tumor image segmentation using deep learning approach
Published 2022“…In neurosurgical, Magnetic Resonance Images (MRI) scans are used to detect cancerous cell grow thin brain called brain tumor. Application that can aid in providing automatic brain tumor segmentation is crucial as segmentation of the exact size and spatial location of these tumors are a time-consuming task. …”
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Final Year Project / Dissertation / Thesis -
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Applying SAX-based time series analysis to classify EEG signal using a COTS EEG device
Published 2021“…One of the proposals that could help solve this problem is the use of brain-computer interface (BCI)s. Brain-Computer Interface (BCI) can be used as a direct communication path between the brain and an external device. …”
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Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm
Published 2025“…Furthermore, the low computational burden of MSEDA rendered it a strong alternative to heuristic multi-agent algorithms, which frequently encounter high computational costs with large controller design parameters.…”
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Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm
Published 2025“…Furthermore, the low computational burden of MSEDA rendered it a strong alternative to heuristic multi-agent algorithms, which frequently encounter high computational costs with large controller design parameters.…”
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Deep learning segmentation of brain ischemic lesion from magnetic resonance images for three-dimensional modelling
Published 2025“…Future improvements on the present U-Net is necessary so that the accuracy can be increased further, computationally economic, and to produce a near accurate semantic segmentation of brain lesion.…”
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Electroencephalogram signal interpretation system for mobile robot
Published 2013“…The most popular approach is a non-invasive method, using Electroencephalogram (EEG) analysis which acquires signals from the brain. Currently, the BCI application is to acquire signals from 32 to 64 electrodes’ recordings and translate them to a movement using various computing algorithm which can be used in wheelchair navigation, or control robot movements. …”
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Development of Acute Stroke Lesion Segmentation Algorithm in Brain MRI using Pseudo-colour with K-means Clustering
Published 2021“…The development of an automatic segmentation algorithm was successfully achieved by entirely depending on the computer without human interaction. …”
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Detection of partial seizure: An application of fuzzy rule system for wearable ambulatory systems
Published 2014“…Here the paper shows preliminary results of the normal state, pre-seizure state and seizure state of the subject's brain signal data. This can be observed and the algorithm with the detection structure can produce cautioning signals for epileptic seizure. …”
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Conference or Workshop Item -
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A Proposed Frame Work for Real Time Epileptic Seizure Prediction using Scalp EEG
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Comparison on performance of adaptive algorithms for eye blinks removal in electroencephalogram
Published 2018“…The interference of eye blink artifacts can cause serious distortion to electroencephalogram (EEG) which could bias the signal interpretation and reduce the classification accuracy in a brain-computer interface (BCI) application. …”
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Proceeding Paper